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The following is a conversation with Doug Lennett, creator of Psyche, a system that for close to 40 years, and still today, has sought to solve the core problem of artificial intelligence, the acquisition of common sense knowledge and the use of that knowledge to think, to reason, and to understand the world.
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To support this podcast, please check out our sponsors in the description.
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As a side note, let me say that in the excitement of the modern era of machine learning, it is easy to forget just how little we understand exactly how to build the kind of intelligence that matches the power of the human mind.
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To me, many of the core ideas behind psych, in some form, in actuality, or in spirit, will likely be part of the AI system that achieves general superintelligence.
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But perhaps more importantly, solving this problem of common sense knowledge will help us humans understand our own minds, the nature of truth, and finally, how to be more rational and more kind to each other.
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As usual, I ll do a few minutes of ads now.
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They make it easy for you to make your own website.
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I used Squarespace to create GogginsChallenge.com back in the spring for the 4x4x48 challenge where you run 4 miles every 4 hours for 48 hours.
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So it ends up being 48 miles.
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It literally took a few minutes to create the website where there was a form where people can submit their story leading up and after the challenge.
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I said no problem, got it done.
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Quick websites, big websites, doesn t matter.
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This is the Lex Friedman Podcast, and here is my conversation with Doug Lenat.
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Psyche is a project launched by you in 1984 and still is active today, whose goal is to assemble a knowledge base that spans the basic concepts and rules about how the world works.
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In other words, it hopes to capture common sense knowledge, which is a lot harder than it sounds.
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Can you elaborate on this mission and maybe perhaps speak to the various sub goals within this mission?
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When I was a faculty member in the computer science department at Stanford, my colleagues and I did research in all sorts of artificial intelligence programs.
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So natural language understanding programs, robots, expert systems, and so on.
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And we kept hitting the very same brick wall.
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Our systems would have impressive early successes,
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And so if your only goal was academic, namely to get enough material to write a journal article, that might actually suffice.
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But if you re really trying to get AI, then you have to somehow get past the brick wall.
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And the brick wall was the programs didn t have what we would call common sense.
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They didn t have general world knowledge.
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They didn t really understand what they were doing, what they were saying, what they were being asked.
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And so, very much like a clever dog performing tricks.
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We could get them to do tricks, but they never really understood what they were doing.
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Sort of like when you get a dog to fetch your morning newspaper.
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The dog might do that successfully, but the dog has no idea what a newspaper is or what it says or anything like that.
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What does it mean to understand something?
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Can you maybe elaborate on that a little bit?
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Is it, is understanding action of like combining little things together, like through inference, or is understanding the wisdom you gain over time that forms a knowledge?
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I think of understanding more like the ground you stand on, which could be very shaky, could be very unsafe, but most of the time is not, because underneath it is more ground.
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and eventually rock and other things, but layer after layer after layer, that solid foundation is there.
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And you rarely need to think about it, you rarely need to count on it, but occasionally you do.
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And I ve never used this analogy before, so bear with me, but I think the same thing is true in terms of getting computers to understand things, which is,
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You ask a computer a question, for instance, Alexa or some robot or something, and maybe it gets the right answer.
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But if you were asking that of a human, you could also say things like, why?
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or how might you be wrong about this, or something like that.
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And the person would answer you.
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And it might be a little annoying if you have a small child and they keep asking why questions in series.
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Eventually, you get to the point where you throw up your hands and say, I don t know, it s just the way the world is.
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But for many layers, you actually have that layered, solid foundation of support so that when you need it,
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you can count on it.
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And when do you need it?
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Well, when things are unexpected, when you come up against a situation which is novel.
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For instance, when you re driving, it may be fine to have a small program, a small set of rules that cover 99% of the cases, but that 1% of the time when something strange happens,
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You really need to draw on common sense.
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For instance, my wife and I were driving recently and there was a trash truck in front of us.
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And I guess they had packed it too full and the back exploded and trash bags went everywhere.
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And we had to make a split second decision.
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Are we going to slam on our brakes?
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Are we going to swerve into another lane?
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Are we going to just run it over?
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Because there were cars all around us.
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In front of us was a large trash bag.
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And we know what we throw away in trash bags.
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Probably not a safe thing to run over.
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Over on the left was a bunch of fast food restaurant trash bags.
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And it s like, oh, well, those things are just like Styrofoam and leftover food.
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We ll run over that.
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And so that was a safe thing for us to do.
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Now, that s the kind of thing that s going to happen maybe once in your life.
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But the point is that there s almost no telling what little bits of knowledge about the world you might actually need in some situations which were unforeseen.
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But see, when you sit on that mountain or that ground that goes deep of knowledge, in order to make a split second decision about fast food trash or random trash from the back of a trash truck,
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you need to be able to leverage that ground you stand on in some way.
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It s not merely, you know, it s not enough to just have a lot of ground to stand on.
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It s your ability to leverage it, to utilize it in a split, like integrate it all together to make that split second decision.
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And I suppose understanding isn t just, you know,
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having common sense knowledge to access, it s the act of accessing it somehow.
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Like correctly filtering out the parts of the knowledge that are not useful, selecting only the useful parts, and effectively making conclusive decisions.
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So let s tease apart two different tasks, really, both of which are incredibly important and even necessary if you re going to have this in a useful, usable fashion, as opposed to, say, like library books sitting on a shelf, and so on, where the knowledge might be there, but if a fire comes, the books are gonna burn because they don t know what s in them, and they re just gonna sit there while they burn.
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So there are two aspects of using the knowledge.
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One is a kind of a theoretical, how is it possible at all?
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And then the second aspect of what you said is, how can you do it quickly enough?
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So how can you do it at all is something that philosophers have grappled with.
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And fortunately, philosophers 100 years ago and even earlier developed a kind of formal language
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Like English, it s called predicate logic or first-order logic or something like predicate calculus and so on.
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So there s a way of representing things in this formal language, which enables a mechanical procedure to sort of grind through and algorithmically produce all of the same logical
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entailments, all the same logical conclusions that you or I would from that same set of pieces of information that are represented that way.
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So, that sort of raises a couple questions.
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One is, how do you get all this information from, say, observations and English and so on, into this logical form?
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And secondly, how can you then efficiently run these algorithms to actually get the information you need, in the case I mentioned, in a tenth of a second, rather than, say, in
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you know, 10 hours or 10,000 years of computation.
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And those are both really important questions.
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And like a corollary addition to the first one is how many such things do you need to gather for it to be useful in certain contexts?
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So like what, in order, you mentioned philosophers, in order to capture this world and represent it in a logical way and with a form of logic,
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Like, how many statements are required?
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Is it five?
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Is it 10?
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Is it 10 trillion?
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Is it like, that s, as far as I understand, is probably still an open question.
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It may forever be an open question to say, like, definitively about.
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To describe the universe perfectly, how many facts do you need?
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I guess I m going to disappoint you by giving you an actual answer to your question.
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Okay.
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Well, no, this sounds exciting.
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Yes.
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Okay.
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So now we have like three things to talk about.
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We ll keep adding more.
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It s okay.
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The first and the third are related.
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Yes.
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So let s leave the efficiency question aside for now.
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So how does all this information get represented in logical form so that these algorithms, resolution theorem proving and other algorithms, can actually grind through all the logical consequences of what you said?
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And that ties into your question about, well, how many of these things do you need?
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Because if the answer is small enough, then by hand, you could write them out one at a time.
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So in the early 1984, I held a meeting at Stanford, where I was a faculty member there, where we assembled about half a dozen of the smartest people I know.
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people like Alan Newell and Marvin Minsky and Alan Kay and a few others.
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Was Feynman there by chance?
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Because he liked your, he commented about your sister Murisko at the time.
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No, he wasn t part of this meeting.
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That s a heck of a meeting anyway.
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I think Ed Feigenbaum was there.
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I think
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Josh Lederberg was there.
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So we have all these different smart people and we came together to address the question that you raised, which is, if it s important to represent common sense knowledge and world knowledge in order for AIs to not be brittle, in order for AIs not to just have the veneer of intelligence, well, how many pieces of common sense, how many if-then rules
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for instance, would we have to actually write in order to essentially cover what people expect perfect strangers to already know about the world?
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And I expected there would be an enormous divergence of opinion and computation, but amazingly, everyone got an answer which was around a million.
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And one person got the answer by saying, well, look,
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can only burn into human long-term memory a certain number of things per unit time, like maybe one every 30 seconds or something.
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And other than that, it s just short-term memory and it flows away like water and so on.
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So by the time you re, say, 10 years old or so, how many things could you possibly have burned into your long-term memory?
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And it s like about a million.
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Another person went in a completely different direction and said, well, if you look at the number of words in a dictionary, not a whole dictionary, but for someone to essentially be considered to be fluent in a language, how many words would they need to know?
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And then about how many things about each word would you have to tell it?
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And so they got to a million that way.
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Another person said, well, let s actually look at one single short one-volume desk encyclopedia article.
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And so we ll look at what was like a four-paragraph article or something.
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I think about grebes.
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Grebes are a type of waterfowl.
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And if we were going to sit there and represent every single thing that was there, how many assertions or rules or statements would we have to write in this logical language and so on, and then multiply that by all of the number of articles that there were and so on.
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So all of these estimates came out with a million.
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And so if you do the math, it turns out that like, oh, well then maybe in something like 100 person years, in one or two person centuries, we could actually get this written down by hand.
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And
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a marvelous coincidence, an opportunity existed right at that point in time, the early 1980s.
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There was something called the Japanese Fifth Generation Computing Effort.
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Japan had threatened to do in computing and AI and hardware what they had just finished doing in consumer electronics and the automotive industry, namely resting control away from the United States and more generally away from the West.
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And so America was scared.
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And Congress did something.
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That s how you know it was a long time ago, because Congress did something.
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Congress passed something called the National Cooperative Research Act, NCRA.
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And what it said was, hey, all you big American companies, that s also how you know it was a long time ago, because they were American companies rather than multinational companies.
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Hey, all you big American companies,
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Normally, it would be an antitrust violation if you colluded on R&D, but we promise for the next 10 years, we won t prosecute any of you if you do that to help combat this threat.
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And so overnight, the first two research consortia in America sprang up, both of them coincidentally in Austin, Texas.
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One called Sematech, focusing on hardware chips and so on, and then one called MCC, the Microelectronics and Computer Technology Corporation, focusing more on software, on databases and AI and natural language understanding and things like that.
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And I got the opportunity, thanks to my friend Woody Bledsoe, who was one of the people who founded that, to come and be its principal scientist.
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And he sent Admiral Bob Inman, who was the person running MCC,
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came and talked to me and said, look, Professor, you re talking about doing this project.
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It s going to involve person centuries of effort.
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You ve only got a handful of graduate students.
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You do the math.
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It s going to take you longer than the rest of your life to finish this project.
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But if you move to the wilds of Austin, Texas, we ll put 10 times as many people on it, and you ll be done in a few years.
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And so that was pretty exciting.
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And so I did that.
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I took my leave from Stanford.
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I came to Austin.
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I worked for MCC.
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The good news and bad news is that all of us were off by an order of magnitude.
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It turns out what you need are tens of millions of these
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pieces of knowledge about everyday, sort of like if you have a coffee cup with stuff in it and you turn it upside down, the stuff in it s going to fall out.
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So you need tens of millions of pieces of knowledge like that, even if you take trouble to make each one as general as it possibly could be.
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But the good news was that thanks to initially the fifth generation effort and then later US government agency funding and so on, we were able to get enough funding not for a couple person centuries of time, but for a couple person millennia of time, which is what we ve spent since 1984.
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getting Psyche to contain the tens of millions of rules that it needs in order to really capture and span not all of human knowledge, but the things that you assume other people know, the things you count on other people knowing.
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By now, we ve done that.
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And the good news is, since you ve waited 38 years, just about, to talk to me, we re about at the end of that process.
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So most of what we re doing now is not putting in even what you would consider common sense, but more putting in domain-specific application-specific knowledge about
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healthcare in a certain hospital, or about oil pipes getting clogged up, or whatever the applications happen to be.
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We ve almost come full circle, and we re doing things very much like the expert systems of the 1970s and the 1980s, except instead of resting on nothing,
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and being brittle, they re now resting on this massive pyramid, if you will, this massive lattice of common sense knowledge so that when things go wrong, when something unexpected happens, they can fall back on more and more and more general principles, eventually bottoming out in things like, for instance, if we have a problem with the microphone, one of the things you ll do is
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unplug it, plug it in again, and hope for the best, right?
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Because that s one of the general pieces of knowledge you have in dealing with electronic equipment or software systems or things like that.
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Is there a basic principle like that?
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Is it possible to encode something that generally captures this idea of turn it off and turn it back on and see if it fixes?
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Oh absolutely, that s one of the things that Psyche knows.
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That s actually one of the fundamental laws of nature.
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I wouldn t call it a law, it s more like a... Seems to work every time, so it sure looks like a law, I don t know.
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So that basically covered the resources needed, and then we had to devise a method to actually figure out, well, what are the tens of millions of things that we need to tell the system?
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And for that, we found a few techniques which worked really well.
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One is to take any piece of text almost, it could be an advertisement, it could be a transcript, it could be a novel, it could be an article,
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Don t pay attention to the actual type that s there, the black space on the white page.
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Pay attention to the complement of that, the white space, if you will.
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What did the writer of this sentence assume that the reader already knew about the world?
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For instance, if they used a pronoun,
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How did they figure out that you, why did they think that you would be able to understand what the intended referent of that pronoun was?
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That they used an ambiguous word.
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How did they think that you would be able to figure out what they meant by that word?
1
SPEAKER_02
The other thing we look at is the gap between one sentence and the next one.
1
SPEAKER_02
What are all the things that the writer expected you to fill in and infer occurred between the end of one sentence and the beginning of the other?
1
SPEAKER_02
So like if the sentence says,
1
SPEAKER_02
Fred Smith robbed the Third National Bank, period.
1
SPEAKER_02
He was sentenced to 20 years in prison, period.
1
SPEAKER_02
Well, between the first sentence and the second, you re expected to infer things like Fred got caught, Fred got arrested, Fred went to jail, Fred had a trial, Fred was found guilty.
1
SPEAKER_02
and so on.
1
SPEAKER_02
If my next sentence starts out with something like the judge dot dot dot, then you assume it s the judge at his trial.
1
SPEAKER_02
If my next sentence starts out something like the arresting officer dot dot dot, you assume that it was the police officer who arrested him after he committed the crime and so on.
1
SPEAKER_02
So those are two techniques for getting that knowledge.
1
SPEAKER_02
The other thing we sometimes look at is sort of like fake news or sort of humorous onion headlines or headlines in the Weekly World News, if you know what that is, or the National Enquirer, where it s like, oh, we don t believe this.
1
SPEAKER_02
Then we introspect on why don t we believe it.
1
SPEAKER_02
So there are things like B-17 lands on the moon.
1
SPEAKER_02
It s like, what do we know about the world that causes us to believe that that s just silly or something like that?
1
SPEAKER_02
Or another thing we look for are contradictions, things which can t both be true.
1
SPEAKER_02
And we say, what is it that we know that causes us to know that both of these can t be true at the same time?
1
SPEAKER_02
For instance, in one of the weekly World News editions, in one article, it talked about how Elvis was cited
1
SPEAKER_02
even though he was getting on in years and so on.
1
SPEAKER_02
And another article in the same one talked about people seeing Elvis s ghost.
1
SPEAKER_02
So it s like, why do we believe that at least one of these articles must be wrong?
1
SPEAKER_02
And so on.
1
SPEAKER_02
So we have a series of techniques like that that enable our people.
1
SPEAKER_02
And by now, we have about 50 people working full-time on this and have for decades.
1
SPEAKER_02
So we ve put in the thousands of person years of effort.
1
SPEAKER_02
We ve built up these tens of millions of rules.
1
SPEAKER_02
We constantly police the system to make sure that we re saying things as generally as we possibly can.
1
SPEAKER_02
So you don t want to say things like, no mouse is also a moose.
1
SPEAKER_02
because if you said things like that, then you d have to add another one or two or three zeros onto the number of assertions you d actually have to have.
1
SPEAKER_02
So at some point, we generalize things more and more, and we get to a point where we say, oh yeah, for any two biological taxons, if we don t know explicitly that one is a generalization of another, then almost certainly they re disjoint.
1
SPEAKER_02
A member of one is not going to be a member of the other, and so on.
1
SPEAKER_00
And the same thing with the Elvis and the ghost.
1
SPEAKER_00
It has nothing to do with Elvis.
1
SPEAKER_00
It s more about human nature and the mortality and that kind of stuff.
1
SPEAKER_00
Right.
1
SPEAKER_02
In general, things are not both alive and dead at the same time.
1
SPEAKER_00
Yeah.
1
SPEAKER_00
Unless special cats in theoretical physics examples.
1
SPEAKER_02
Well, that raises a couple important points.
1
SPEAKER_00
Well, that s the onion headline situation type of thing, okay, sorry.
1
SPEAKER_02
But no, no, so what you bring up is this really important point of like, well, how do you handle exceptions and inconsistencies and so on?
1
SPEAKER_02
And one of the hardest lessons for us to learn, it took us about five years to really grit our teeth and learn to love it,
1
SPEAKER_02
is we had to give up global consistency.
1
SPEAKER_02
So the knowledge base can no longer be consistent.
1
SPEAKER_02
So this is a kind of scary thought.
1
SPEAKER_02
I grew up watching Star Trek and anytime a computer was inconsistent, it would either freeze up or explode or take over the world or something bad would happen.
1
SPEAKER_02
If you come from a mathematics background, once you can prove false, you can prove anything, so that s not good.
1
SPEAKER_02
That s why the old knowledge-based systems were all very, very consistent, but the trouble
1
SPEAKER_02
by and large are models of the world, the way we talk about the world and so on.
1
SPEAKER_02
There are all sorts of inconsistencies that creep in here and there that will sort of kill some attempt to build some enormous globally consistent knowledge base.
1
SPEAKER_02
And so what we had to move to was a system of local consistency.
1
SPEAKER_02
So a good analogy is, you know that the surface of the earth is more or less spherical.
1
SPEAKER_02
globally.
1
SPEAKER_02
But you live your life every day as though the surface of the Earth were flat.
1
SPEAKER_02
When you re talking to someone in Australia, you don t think of them as being oriented upside down to you.
1
SPEAKER_02
When you re planning a trip, even if it s 1,000 miles away, you may think a little bit about time zones, but you rarely think about the curvature of the Earth and so on.
1
SPEAKER_02
And for most purposes, you can live your whole life without really worrying about that, because the Earth is locally flat.
1
SPEAKER_02
In much the same way, the psych knowledge base is divided up into almost like tectonic plates, which are individual contexts.
1
SPEAKER_02
And each context is more or less consistent, but there can be small inconsistencies at the boundary between one context and the next one and so on.
1
SPEAKER_02
And so by the time you move, say, 20 contexts over,
1
SPEAKER_02
there could be glaring inconsistencies.
1
SPEAKER_02
Eventually, you get from the normal modern real-world context that we re in right now to something like Roadrunner cartoon context where physics is very different.
1
SPEAKER_02
In fact, life and death are very different because no matter how many times he s killed, the coyote comes back in the next scene and so on.
1
SPEAKER_02
So that was a hard lesson to learn, and we had to make sure that our representation language, the way that we actually encode the knowledge and represent it, was expressive enough that we could talk about things being true in one context and false in another.
1
SPEAKER_02
Things that are true at one time and false in another.
1
SPEAKER_02
Things that are true, let s say, in one region, like one country, but false in another.
1
SPEAKER_02
Things that are true in one person s belief system,
1
SPEAKER_02
but false in another person s belief system.
1
SPEAKER_02
Things that are true at one level of abstraction and false at another.
1
SPEAKER_02
For instance, at one level of abstraction, you think of this table as a solid object, but down at the atomic level, it s mostly empty space and so on.
1
SPEAKER_00
So then that s fascinating, but it puts a lot of pressure on context to do a lot of work.
1
SPEAKER_00
So you say tectonic plates.
1
SPEAKER_00
Is it possible to formulate contexts that are general and big that do this kind of capture of knowledge bases?
1
SPEAKER_00
Or do you then get turtles on top of turtles, again, where there s just a huge number of contexts?
1
SPEAKER_02
So, it s good you asked that question, because you re pointed in the right direction, which is,
1
SPEAKER_02
You want context to be first-class objects in your system s knowledge base, in particular in Psyche s knowledge base.
1
SPEAKER_02
And by first-class object, I mean that we should be able to have Psyche think about and talk about and reason about
1
SPEAKER_02
one context or another context, the same way it reasons about coffee cups and tables and people and fishing and so on.
1
SPEAKER_02
And so, contexts are just terms in its language, just like the ones I mentioned.
1
SPEAKER_02
And so, Psyche can reason about contexts, contexts can arrange hierarchically and so on.
1
SPEAKER_02
And so, you can say things about, let s say,
1
SPEAKER_02
Things that are true in the modern era, things that are true in a particular year, would then be a sub-context of the things that are true in, let s say, a century or a millennium or something like that.
1
SPEAKER_02
Things that are true in Austin, Texas,
1
SPEAKER_02
are generally going to be a specialization of things that are true in Texas, which is going to be a specialization of things that are true in the United States, and so on.
1
SPEAKER_02
And so you don t have to say things over and over again at all these levels.
1
SPEAKER_02
You just say things at the most general level that it applies to.
1
SPEAKER_02
And you only have to say it once, and then it essentially inherits to all these more specific contexts.
1
SPEAKER_00
Jessica, slightly technical question.
1
SPEAKER_00
Is this inheritance a tree or a graph?
1
SPEAKER_02
Oh, you definitely have to think of it as a graph.
1
SPEAKER_02
So we could talk about, for instance, why the Japanese fifth generation computing effort failed.
1
SPEAKER_02
There were about half a dozen different reasons.
1
SPEAKER_02
One of the reasons they failed was because they tried to represent knowledge as a tree rather than as a graph.
1
SPEAKER_02
And so each node in their representation
1
SPEAKER_02
could only have one parent node.
1
SPEAKER_02
So if you had a table that was a wooden object, a black object, a flat object, and so on, you had to choose one, and that s the only parent it could have.
1
SPEAKER_02
When, of course, depending on what it is you need to reason about it, sometimes it s important to know that it s made out of wood, like if we re talking about a fire.
1
SPEAKER_02
Sometimes it s important to know that it s flat, if we re talking about resting something on it, and so on.
1
SPEAKER_02
One of the problems was that they wanted a kind of Dewey decimal numbering system for all of their concepts, which meant that each node could only have at most 10 children, and each node could only have one parent.
1
SPEAKER_02
And while that does enable the Dewey Decimal type numbering of concepts, labeling of concepts, it prevents you from representing all the things you need to about objects in our world.
1
SPEAKER_02
And that was one of the things which they never were able to overcome.
1
SPEAKER_02
And I think that was one of the main reasons that that project failed.
1
SPEAKER_00
So we ll return to some of the doors you ve opened, but if we can go back to that room in 1984 around there with Marvin Minsky and Stanford.
1
SPEAKER_02
By the way, I should mention that Marvin wouldn t do his estimate until someone brought him an envelope so that he could literally do a back of the envelope calculation to come up with his number.
1
SPEAKER_00
Well, because I feel like the conversation in that room is an important one.
1
SPEAKER_00
Sometimes science is done in this way.
1
SPEAKER_00
A few people get together and plant the seed
2
SPEAKER_00
of ideas and they reverberate throughout history and some kind of dissipate and disappear and some, you know, Drake equation and, you know, they, you know, it seems like a meaningless equation, somewhat meaningless, but I think it drives and motivates a lot of scientists and when the aliens finally show up,
2
SPEAKER_00
that equation will get even more valuable because then we ll be able to, in the long arc of history, the Drake equation will prove to be quite useful, I think.
2
SPEAKER_00
In that same way, a conversation of just how many facts are required to capture the basic common sense knowledge of the world.
2
SPEAKER_00
That s a fascinating question.
2
SPEAKER_01
I want to distinguish between what you think of as facts and the kind of things that we represent.
2
SPEAKER_01
So we map to and essentially make sure that Psych has the ability to, as it were, read and access the kind of facts you might find, say, in Wikidata or stated in a Wikipedia article or something like that.
2
SPEAKER_01
So what we re representing, the things that we need a small number of tens of millions of, are more like rules of thumb, rules of good guessing, things which are usually true and which help you to make sense of the facts that are on sort of sitting off in some database or some other more static story.
2
SPEAKER_00
So they re almost like platonic forms.
2
SPEAKER_00
So like when you read stuff on Wikipedia, that s going to be like projections of those ideas.
2
SPEAKER_00
You read an article about the fact that Elvis died, that s a projection of the idea that humans are mortal and like.
2
SPEAKER_00
Very few Wikipedia articles will write, humans are mortal.
2
SPEAKER_01
Exactly, that s what I meant about ferreting out the unstated things in text.
2
SPEAKER_01
What are all the things that were assumed?
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SPEAKER_01
And so those are things like, if you have a problem with something, turning it off and on often fixes it for reasons we don t really understand and we re not happy about.
2
SPEAKER_01
Or people can t be both alive and dead at the same time.
2
SPEAKER_01
Or water flows downhill.
2
SPEAKER_01
If you search online for water flowing uphill and water flowing downhill, you ll find more references for water flowing uphill, because it s used as a kind of a metaphorical reference for some unlikely thing, because of course everyone already knows that water flows downhill, so why would anyone bother saying that?
2
SPEAKER_00
Do you have a word you prefer, because we said facts isn t the right word, is there a word like concepts?
2
SPEAKER_01
I would say assertions.
2
SPEAKER_01
Assertions.
2
SPEAKER_01
Assertions or rules, because I m not talking about rigid rules, but rules of thumb.
2
SPEAKER_01
But assertions is a nice one that covers all of these things.
2
SPEAKER_00
Yeah, as a programmer, to me, assert has a very dogmatic, authoritarian feel to them.
2
SPEAKER_00
Oh, I m sorry.
2
SPEAKER_00
I m so sorry.
2
SPEAKER_00
Okay, but assertions works.
2
SPEAKER_00
Okay, so if we go back to that room with Marvin Minsky, with you, all these seminal figures, Ed Feigenbaum, thinking about this very philosophical but also engineering question,
2
SPEAKER_00
We can also go back a couple of decades before then and thinking about artificial intelligence broadly when people were thinking about, you know, how do you create super intelligent systems, general intelligence?
2
SPEAKER_00
And I think people s intuition was off at the time.
2
SPEAKER_00
And I mean, this continues to be the case that we re not, when we re grappling with these exceptionally difficult ideas, we re not always, it s very difficult to,
2
SPEAKER_00
truly understand ourselves, when we re thinking about the human mind, to introspect how difficult it is to engineer intelligence, to solve intelligence, we re not very good at estimating that.
2
SPEAKER_00
And you are somebody who has really stayed with this question for decades.
2
SPEAKER_00
do you, what s your sense from 1984 to today?
2
SPEAKER_00
Have you gotten a stronger sense of just how much knowledge is required?
2
SPEAKER_00
So you ve kind of said with some level of certainty that it s still on the order of magnitude of tens of millions.
2
SPEAKER_01
Right, for the first several years, I would have said that it was on the order of one or two million.
2
SPEAKER_01
And so it took us about five or six years to realize that we were off
2
SPEAKER_01
by a factor of 10.
2
SPEAKER_00
But I guess what I m asking, you know, Marvin Misk is very confident in the 60s.
2
SPEAKER_00
Yes.
2
SPEAKER_00
Right.
2
SPEAKER_00
What s your sense, if you
2
SPEAKER_00
you know, 200 years from now, you re still, you know, you re not going to be any longer in this particular biological body, but your brain will still be in the digital form, and you ll be looking back.
2
SPEAKER_00
Would you think you were smart today, like your intuition was right, or do you think you may be really off?
2
SPEAKER_01
So I think I m right enough, and let me explain what I mean by that, which is,
2
SPEAKER_01
Sometimes like if you have an old-fashioned pump, you have to prime the pump, and then eventually it starts.
2
SPEAKER_01
So I think I m right enough in the sense that- To prime the pump.
2
SPEAKER_01
What we ve built, even if it isn t, so to speak, everything you need, it s primed the knowledge pump enough that Psych can now itself help to learn more and more automatically on its own.
2
SPEAKER_01
by reading things and understanding and occasionally asking questions like a student would or something, and by doing experiments and discovering things on its own and so on.
2
SPEAKER_01
So through a combination of psych-powered discovery and psych-powered reading, it will be able to bootstrap itself
2
SPEAKER_01
maybe it s the final 2%, maybe it s the final 99%.
2
SPEAKER_01
So even if I m wrong, all I really need to build is a system which has primed the pump enough that it can begin that cascade upward, that self-reinforcing sort of quadratically or maybe even exponentially increasing path upward that we get from, for instance, talking with each other.
2
SPEAKER_01
That s why
2
SPEAKER_01
humans today know so much more than humans 100,000 years ago.
2
SPEAKER_01
We re not really that much smarter than people were 100,000 years ago, but there s so much more knowledge, and we have language, and we can communicate, we can check things on Google, and so on.
2
SPEAKER_01
So effectively, we have this enormous power at our fingertips.
2
SPEAKER_01
And there s almost no limit to how much you could learn if you wanted to, because you ve already gotten to a certain level of understanding of the world that enables you to read all these articles and understand them, that enables you to go out and, if necessary, do experiments, although that s slower, as a way of gathering data and so on.
2
SPEAKER_01
And I think this is really an important point, which is if we have artificial intelligence, real general artificial intelligence, human-level artificial intelligence,
2
SPEAKER_01
then people will become smarter.
2
SPEAKER_01
It s not so much that it ll be us versus the AIs.
2
SPEAKER_01
It s more like us and the AIs together will be able to do things that require more creativity, that would take too long right now, but we ll be able to do lots of things in parallel.
2
SPEAKER_01
We ll be able to misunderstand each other less,
2
SPEAKER_01
There s all sorts of value that effectively for an individual would mean that individual will, for all intents and purposes, be smarter.
2
SPEAKER_01
And that means that humanity as a species will be smarter.
2
SPEAKER_01
And when was the last time that any invention qualitatively
2
SPEAKER_01
made a huge difference in human intelligence.
2
SPEAKER_01
You have to go back a long ways.
2
SPEAKER_01
It wasn t like the internet or the computer or mathematics or something.
2
SPEAKER_01
It was all the way back to the development of language.
2
SPEAKER_01
We sort of look back on pre-linguistic cavemen as well.
2
SPEAKER_01
You know, they weren t really intelligent, were they?
2
SPEAKER_01
They weren t really human, were they?
2
SPEAKER_01
And I think that, as you said, 50, 100, 200 years from now, people will look back on people today, right before the advent of these sort of lifelong general AI uses.
2
SPEAKER_01
and say, you know, those poor people, they weren t really human, were they?
2
SPEAKER_00
Exactly.
2
SPEAKER_00
So you said a lot of really interesting things.
2
SPEAKER_00
By the way, I would maybe try to argue that the internet is on the order of the kind of big leap in improvement that the invention of language was.
2
SPEAKER_01
Well, certainly a big leap in one direction.
2
SPEAKER_01
We re not sure whether it s upward or downward,
2
SPEAKER_00
Well, I mean very specific parts of the internet, which is access to information, like a website like Wikipedia, like ability for human beings from across the world to access information very quickly.
2
SPEAKER_01
So I could take either side of this argument, and since you just took one side, I ll give you the other side, which is that almost nothing has done more harm
2
SPEAKER_01
than something like the internet and access to that information in two ways.
2
SPEAKER_01
One is it s made people more globally ignorant in the same way that calculators made us
2
SPEAKER_01
more or less innumerate.
2
SPEAKER_01
When I was growing up, we had to use slide rules, we had to be able to estimate, and so on.
2
SPEAKER_01
Today, people don t really understand numbers, they don t really understand math, they don t really estimate very well at all, and so on.
2
SPEAKER_01
They don t really understand the difference between trillions and billions and millions and so on very well.
2
SPEAKER_01
because calculators do that all for us.
2
SPEAKER_01
And thanks to things like the internet and search engines, that same kind of juvenile-ism is reinforced in making people essentially be able to live their whole lives, not just without being able to do arithmetic and estimate, but now without actually having to really know almost anything.
2
SPEAKER_01
Because anytime they need to know something, they ll just go and look it up.
2
SPEAKER_00
And I could tell you could play both sides of this, and it is a double-edged sword.
2
SPEAKER_00
You can, of course, say the same thing about language.
2
SPEAKER_00
Probably people, when they invented language, they were criticized.
2
SPEAKER_00
You know, it used to be we would just, if we re angry, we would just kill a person.
2
SPEAKER_00
And if we re in love, we would just have sex with them.
2
SPEAKER_00
And now everybody s writing poetry and bullshit.
2
SPEAKER_00
You know, you should just be direct.
2
SPEAKER_00
You should, like, have physical contact.
2
SPEAKER_00
Enough of this words.
2
SPEAKER_00
and books and you re not actually experiencing, like if you read a book, you re not experiencing the thing.
2
SPEAKER_00
This is nonsense.
2
SPEAKER_01
That s right, if you read a book about how to make butter, that s not the same as if you had to learn it and do it yourself.
2
SPEAKER_00
Exactly, do it yourself.
2
SPEAKER_01
And so on.
2
SPEAKER_01
So let s just say that something is gained but something is lost every time you have these sorts of dependencies on technology.
2
SPEAKER_01
And overall, I think that having smarter individuals and having smarter AI-augmented human species will be one of the few ways that we ll actually be able to overcome some of the global problems we have involving poverty and starvation and global warming and
2
SPEAKER_01
overcrowding, all the other problems that are besetting the planet.
2
SPEAKER_01
We really need to be smarter.
2
SPEAKER_01
And there are really only two routes to being smarter.
2
SPEAKER_01
One is through biochemistry and genetics.
2
SPEAKER_01
genetic engineering.
2
SPEAKER_01
The other route is through having general AIs that augment our intelligence.
2
SPEAKER_01
And, you know, hopefully one of those two ways of paths to salvation will come through before it s too late.
2
SPEAKER_00
Yeah, absolutely, I agree with you.
2
SPEAKER_00
And obviously as an engineer, I have a better sense and an optimism about the technology side of things because you can control things there more.
2
SPEAKER_00
Biology is just such a giant mess.
2
SPEAKER_00
We re living through a pandemic now.
2
SPEAKER_00
There s so many ways that nature can just be just destructive and destructive in a way where it doesn t even notice you.
2
SPEAKER_00
It s not like a battle of humans versus virus.
2
SPEAKER_00
It s just like, huh, okay.
2
SPEAKER_00
And then you can just wipe out an entire species.
2
SPEAKER_01
The other problem with the internet is that it has enabled us to surround ourselves with an echo chamber, with a bubble of like-minded people, which means that you can have truly bizarre theories, conspiracy theories, fake news, and so on, promulgate
2
SPEAKER_01
and surround yourself with people who essentially reinforce what you want to believe or what you already believe about the world.
2
SPEAKER_01
And in the in the old days, that was much harder to do when you had, say, only three TV networks or even before when you had no TV networks and you had to actually like look at the world and make your own reasoned decisions.
2
SPEAKER_00
I like the push and pull of our dance that we re doing, because then I ll just say, in the old world, having come from the Soviet Union, because you had one or a couple of networks, then propaganda could be much more effective, and then the government can overpower its people by telling you the truth, and then starving millions, and torturing millions, and putting millions into camps, and starting wars with a propaganda machine, allowing you to believe that you re actually doing good in the world.
2
SPEAKER_00
with the internet because of all the quote-unquote conspiracy theories, some of them are actually challenging the power centers, the very kind of power centers that a century ago would have led to the death of millions.
2
SPEAKER_00
So there s, it s again, this double-edged sword.
2
SPEAKER_00
And I very much agree with you on the AI side.
2
SPEAKER_00
It s often an intuition that people have that somehow AI will be used to maybe overpower people by certain select groups.
2
SPEAKER_00
And to me, it s not at all obvious that that s the likely scenario.
2
SPEAKER_00
To me, the likely scenario, especially just having observed the trajectory of technology, is it ll be used to empower people.
2
SPEAKER_00
It ll be used to extend the capabilities of individuals across the world.
2
SPEAKER_00
Because there s a lot of money to be made that way.
2
SPEAKER_00
Improving people s lives, you can make a lot of money.
2
SPEAKER_01
the main thing that AI prostheses, AI amplifiers, will do for people is make it easier, maybe even unavoidable, for them to do good critical thinking.
2
SPEAKER_01
So pointing out logical fallacies, logical contradictions, and so on, in things that they otherwise would just blithely believe.
2
SPEAKER_01
Pointing out, essentially,
2
SPEAKER_01
data which they should take into consideration if they really want to learn the truth about something and so on.
2
SPEAKER_01
So I think doing not just educating in the sense of pouring facts into people s heads, but educating in the sense of arming people with the ability to do good critical thinking is enormously powerful.
2
SPEAKER_01
The education system that we have in the US and worldwide
2
SPEAKER_01
generally don t do a good job of that.
2
SPEAKER_01
But I believe that the AIs will, the AIs can and will in the same way that everyone can have their own Alexa or Siri or Google Assistant or whatever.
2
SPEAKER_01
Everyone will have this sort of cradle to grave assistant, which will get to know you, which you ll get to trust.
2
SPEAKER_01
It ll model you, you ll model it.
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SPEAKER_01
It ll call to your attention things which will, in some sense, make your life better, easier, less mistake-ridden, and so on, less regret-ridden, if you listen to it.
2
SPEAKER_00
Yeah, I m in full agreement with you about this space of technologies, and I think it s super exciting.
2
SPEAKER_00
From my perspective, integrating emotional intelligence, so even things like friendship and companionship and love into those kinds of systems, as opposed to helping you just grow intellectually as a human being, allow you to grow emotionally, which is ultimately what makes life amazing, is to sort of
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SPEAKER_00
the old pursuit of happiness.
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SPEAKER_00
So it s not just the pursuit of reason, it s the pursuit of happiness too.
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SPEAKER_00
The full spectrum.
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SPEAKER_00
Well let me, because you mentioned so many fascinating things, let me jump back to the idea of automated reasoning.
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SPEAKER_00
So the acquisition of new knowledge has been done in this very interesting way, but primarily by humans doing this.
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SPEAKER_01
Yes, you can think of monks in their cells in medieval Europe, carefully illuminating manuscripts and so on.
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SPEAKER_00
It s a very difficult and amazing process, actually, because
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SPEAKER_00
it allows you to truly ask the question about, in the white space, what is assumed?
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SPEAKER_00
I think this exercise is, like very few people do this, right?
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SPEAKER_00
They just do it subconsciously, they perform this.
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SPEAKER_01
By definition, because those pieces of elided, of omitted information, of those missing steps, as it were,
2
SPEAKER_01
are pieces of common sense.
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SPEAKER_01
If you actually included all of them, it would almost be offensive or confusing to the reader.
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SPEAKER_01
It s like, why are they telling me all these?
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SPEAKER_01
Of course I know all these things.
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SPEAKER_01
And so it s one of these things which almost by its very nature,
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SPEAKER_01
has almost never been explicitly written down anywhere.
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SPEAKER_01
Because by the time you re old enough to talk to other people and so on, if you survived to that age, presumably you already got pieces of common sense.
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SPEAKER_01
If something causes you pain whenever you do it, probably not a good idea to keep doing it.
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SPEAKER_00
So what ideas do you have, given how difficult this step is, what ideas are there for how to do it automatically without using humans or at least not doing a large percentage of the work for humans?
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SPEAKER_01
humans and then humans only do the very high level supervisory work.
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SPEAKER_00
So we have, in fact, two directions we re pushing on very, very heavily currently at PsyCorps.
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And one involves natural language understanding and the ability to read what people have explicitly written down and to pull knowledge in that way.
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SPEAKER_00
But the other is to build a series of
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knowledge editing tools, knowledge entry tools, knowledge capture tools, knowledge testing tools, and so on.
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SPEAKER_00
Think of them as like user interfaced suite of software tools if you want.
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SPEAKER_00
Something that will help
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SPEAKER_00
people to more or less automatically expand and extend the system in areas where, for instance, they want to build some app, have it do some application or something like that.
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SPEAKER_00
So I ll give you an example of one, which is something called abduction.
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So you ve probably heard of like deduction and induction and so on.
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But abduction is unlike those.
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Abduction is not
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sound, it s just useful.
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SPEAKER_00
So, for instance, deductively, if someone is out in the rain, and they re going to get all wet, and when they enter a room, they might be all wet, and so on.
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SPEAKER_00
So that s deduction.
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SPEAKER_00
But if someone were to walk into the room right now and they were dripping wet, we would immediately look outside to say, oh, did it start to rain or something like that?
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SPEAKER_00
Now, why did we say maybe it started to rain?
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SPEAKER_00
That s not a sound logical inference, but it s certainly a reasonable inference.
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SPEAKER_00
abductive leap to say, well, one of the most common ways that a person would have gotten dripping wet is if they had gotten caught out in the rain or something like that.
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SPEAKER_00
So what does that have to do with what we were talking about?
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SPEAKER_00
So suppose you re building one of these applications, and the system gets some answer wrong.
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SPEAKER_00
And you say, oh, yeah, the answer to this question is this one, not the one you came up with.
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SPEAKER_00
then what the system can do is it can use everything it already knows about common sense, general knowledge, the domain you ve already been telling it about, and context like we talked about, and so on, and say, well, here are seven alternatives
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SPEAKER_00
each of which I believe is plausible given everything I already know.
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SPEAKER_00
And if any of these seven things were true, I would have come up with the answer you just gave me instead of the wrong answer I came up with.
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SPEAKER_00
Is one of these seven things true?
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SPEAKER_00
And then you the expert will look at those seven things and say, oh yeah, number five is actually true.
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SPEAKER_00
And so without actually having to
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SPEAKER_00
tinker down at the level of logical assertions and so on, you ll be able to educate the system in the same way that you would help educate another person who you were trying to apprentice or something like that.
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SPEAKER_01
So that significantly reduces the mental effort or significantly increases the efficiency of the teacher, the human teacher.
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SPEAKER_00
Exactly, and it makes more or less anyone able to be a teacher in that way.
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SPEAKER_00
So that s part of the answer.
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SPEAKER_00
And then the other is that the system on its own will be able to, through reading, through conversations with other people and so on,
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learn the same way that you or I or other humans do.
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SPEAKER_01
First of all, that s a beautiful vision.
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SPEAKER_01
I ll have to ask you about semantic web in a second here.
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SPEAKER_01
But first,
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SPEAKER_01
are there, when we talk about specific techniques, do you find something inspiring or directly useful from the whole space of machine learning, deep learning, these kinds of spaces of techniques that have been shown effective for certain kinds of problems in the recent, now, decade and a half?
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SPEAKER_00
I think of the machine learning work as more or less what our right brain hemispheres do.
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SPEAKER_00
Being able to take a bunch of data and recognize patterns, being able to statistically infer things, and so on.
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SPEAKER_00
I certainly wouldn t want to not have a right brain hemisphere, but I m also glad that I have a left brain hemisphere as well, something that can metaphorically sit back and puff on its pipe and think about this thing over here.
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SPEAKER_00
It s like, why might this have been true?
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what are the implications of it, how should I feel about that, and why, and so on.
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SPEAKER_00
So, thinking more deeply and slowly, what Kahneman called thinking slowly versus thinking quickly.
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SPEAKER_00
Whereas you want machine learning to think quickly, but you want the ability to think deeply, even if it s a little slower.
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SPEAKER_00
So, I ll give you an example of a project we did recently with NIH, involving the Cleveland Clinic and
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a couple other institutions that we ran a project for.
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And what it did was it took GWAS  genome-wide association studies.
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Those are sort of big databases of patients that came into a hospital.
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They got their DNA sequenced because the cost of doing that has gone from infinity to billions of dollars to $100 or so.
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SPEAKER_00
And so now patients routinely get their DNA sequenced.
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SPEAKER_00
So you have these big databases of the SNPs, the single nucleotide polymorphisms, the point mutations in a patient s DNA.
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and the disease that happened to bring them into the hospital.
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So now you can do correlation studies, machine learning studies of which mutations are associated with and led to which physiological problems and diseases and so on, like getting arthritis and so on.
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SPEAKER_00
And the problem is that those correlations turn out to be very spurious.
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SPEAKER_00
They turn out to be very noisy.
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Very many of them have led doctors onto wild goose chases and so on.
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And so they wanted a way of eliminating or the bad ones are focusing on the good ones.
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SPEAKER_00
And so this is where psych comes in, which is psych takes those sort of A to Z correlations between point mutations and the medical condition that needs treatment.
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SPEAKER_00
And we say, OK, let s use all this public knowledge and common sense knowledge about what reactions occur where in the human body, what polymerizes what, what catalyzes what reactions, and so on.
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SPEAKER_00
And let s try to put together a 10 or 20 or 30 step causal explanation of why that mutation might have caused that medical condition.
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SPEAKER_00
So, Psych would put together, in some sense, some Rube Goldberg-like chain that would say, oh yeah, that mutation, if it got expressed, would be this altered protein, which because of that, if it got to this part of the body, would catalyze this reaction.
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SPEAKER_00
that would cause more bioactive vitamin D in the person s blood.
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SPEAKER_00
Anyway, 10 steps later, that screws up bone resorption and that s why this person got osteoporosis early in life and so on.
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SPEAKER_01
So that s human interpretable, or at least doctor human interpretable.
3
SPEAKER_00
Exactly.
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SPEAKER_00
And the important thing, even more than that, is you shouldn t really trust that 20-step Rube Goldberg chain any more than you trust
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SPEAKER_00
that initial A to Z correlation, except two things.
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One, if you can t even think of one causal chain to explain this, then that correlation probably was just noise to begin with.
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SPEAKER_00
And secondly, and even more powerfully, along the way, that causal chain will make predictions, like the one about having more bioactive vitamin D in your blood.
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SPEAKER_00
So you can now go back to the data about these patients and say, by the way,
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SPEAKER_00
did they have slightly elevated levels of bioactive vitamin D in their blood and so on?
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SPEAKER_00
If the answer is no, that strongly disconfirms your whole causal chain.
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SPEAKER_00
If the answer is yes, that somewhat confirms that causal chain.
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SPEAKER_00
Using that, we were able to take these correlations
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SPEAKER_00
from this GWAS database, and we were able to essentially focus the researchers  attention on the very small percentage of correlations that had some explanation, and even better, some explanation that also made some independent prediction that they could confirm or disconfirm by looking at the data.
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SPEAKER_00
So think of it like this kind of synergy where you want
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SPEAKER_00
the right brain machine learning to quickly come up with possible answers.
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SPEAKER_00
You want the left brain psych-like AI to think about that and think about why that might have been the case and what else would be the case if that were true and so on, and then suggest things back to the right brain to quickly check out again
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SPEAKER_00
It s that kind of synergy back and forth which I think is really what s going to lead to general AI, not narrow, brittle machine learning systems and not just something like Psyche.
3
SPEAKER_01
Okay, so that s a brilliant synergy, but I was also thinking in terms of the automated expansion of the knowledge base, you mentioned NLU.
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SPEAKER_01
This is very early days in the machine learning space of this, but self-supervised learning methods, you have these language models, GPT-3 and so on, that just read the internet and they form representations
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SPEAKER_01
that can then be mapped to something useful.
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SPEAKER_01
The question is, what is the useful thing?
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SPEAKER_01
Like, they re now playing with a pretty cool thing called OpenACCodex, which is generating programs from documentation.
3
SPEAKER_01
Okay, that s kind of useful, it s cool.
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SPEAKER_01
But my question is, can it be used to generate, in part, maybe with some human supervision, psych-like assertions, help feed psych more assertions from this giant body of internet data?
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SPEAKER_00
Yes, that is in fact one of our goals is how can we harness machine learning?
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SPEAKER_00
How can we harness natural language processing to increasingly automate the knowledge acquisition process, the growth of psych?
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SPEAKER_00
And that s what I meant by priming the pump, that if you sort of learn things at the fringe of what you know already, you learn this new thing is similar to what you know already and here are the differences and the new things you had to learn about it and so on.
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SPEAKER_00
the more you know, the more and more easily you can learn new things.
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SPEAKER_00
But unfortunately, inversely, if you don t really know anything, then it s really hard to learn anything.
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SPEAKER_00
And so if you re not careful, if you start out with too small
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SPEAKER_00
sort of a core to start this process, it never really takes off.
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SPEAKER_00
And so that s why I view this as a pump priming exercise to get a big enough manually produced, even though that s kind of ugly duckling technique, put in the elbow grease to produce a large enough core that you will be able to do all the kinds of things you re imagining
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SPEAKER_00
without ending up with the kind of wacky brittlenesses that we see, for example, in GPT-3, where you ll tell it a story about
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SPEAKER_00
You know, someone putting a poison, you know, plotting to poison someone and so on.
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SPEAKER_00
And then the, you know, then, you know, GPT-3 says, oh, what s, you say, what s the very next sentence?
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SPEAKER_00
The next sentence is, oh, yeah, that person then drank the poison they just put together.
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SPEAKER_00
It s like, that s probably not what happened.
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SPEAKER_00
or someone, or if you go to Siri and, you know, I think I have, you know, where can I go for help with my alcohol problem or something, it ll come back and say, I found seven liquor stores near you, you know, and so on.
3
SPEAKER_00
So, you know, it s one of these things where, yes, it may be helpful most of the time, it may even be correct most of the time, but if it doesn t really understand
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SPEAKER_00
what it s saying, and if it doesn t really understand why things are true and doesn t really understand how the world works, then some fraction of the time, it s going to be wrong.
3
SPEAKER_00
Now, if your only goal is to sort of find relevant information, like search engines do,
3
SPEAKER_00
then being right 90% of the time is fantastic.
3
SPEAKER_00
That s unbelievably great.
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SPEAKER_00
However, if your goal is to save the life of your child who has some medical problem or your goal is to be able to drive for the next 10,000 hours of driving without getting into a fatal accident and so on, then error rates down at the 10% level or even the 1% level are not really acceptable.
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SPEAKER_01
I like the model of where that learning happens at the edge, and then you kind of think of knowledge as this sphere.
3
SPEAKER_01
So you want a large sphere because the learning is happening on the surface.
3
SPEAKER_00
Exactly.
3
SPEAKER_00
So you have, what you can learn next increases quadratically as the diameter of that sphere goes up.
3
SPEAKER_01
But it s nice because you think when you know nothing, it s like you can learn anything, but the reality, not really.
3
SPEAKER_00
Right, if you know nothing, you can really learn nothing.
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SPEAKER_00
You can appear to learn.
3
SPEAKER_00
So I ll also, one of the anecdotes I could go back and give you about why I feel so strongly about this personally was in 1980, 81,
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SPEAKER_00
My daughter Nicole was born, and she s actually doing fine now, but when she was a baby, she was diagnosed as having meningitis, and doctors wanted to do all these scary things, and my wife and I were very worried, and we could not get
3
SPEAKER_00
a meaningful answer from her doctors about exactly why they believed this, what the alternatives were, and so on.
3
SPEAKER_00
Fortunately, a friend of mine, Ted Shortliffe, was another assistant professor in computer science
3
SPEAKER_00
at Stanford at the time, and he d been building a program called Mycin, which was a medical diagnosis program that happened to specialize in blood infections like meningitis, and so he had privileges at Stanford Hospital because he was also an MD.
3
SPEAKER_00
And so we got hold of her chart and we put in her case and it came up with exactly the same diagnoses and exactly the same therapy recommendations.
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SPEAKER_00
But the difference was because it was a knowledge-based system, a rule-based system, it was able to tell us step by step by step why this was the diagnosis and step by step why this was the best therapy, the best procedure to
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SPEAKER_00
to do for her and so on.
3
SPEAKER_00
And there was a real epiphany because that made all the difference in the world.
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SPEAKER_00
Instead of blindly having to trust in authority, we were able to understand what was actually going on.
3
SPEAKER_00
And so at that time I realized that that really is what was missing in computer programs was that even if they got things right, because they didn t really understand
3
SPEAKER_00
the way the world works and why things are the way they are, they weren t able to give explanations of their answer.
3
SPEAKER_00
And it s one thing to use a machine learning system that says, I think you should get this operation.
3
SPEAKER_00
And you say, why?
3
SPEAKER_00
And it says, 0.83.
3
SPEAKER_00
And you say, no, in more detail, why?
3
SPEAKER_00
And it says, 0.831.
3
SPEAKER_00
That s not really very compelling, and that s not really very helpful.
3
SPEAKER_01
There s this idea of the semantic web that when I first heard about, I just fell in love with the idea.
3
SPEAKER_01
It was the obvious next step for the internet.
3
SPEAKER_01
And maybe you can speak about what is the semantic web?
3
SPEAKER_01
What are your thoughts about it?
3
SPEAKER_01
How your vision and mission and goals with psych are connected, integrated?
3
SPEAKER_01
Are they dance partners?
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SPEAKER_01
Are they aligned?
3
SPEAKER_01
What are your thoughts there?
3
SPEAKER_00
So think of the semantic web as a kind of knowledge graph.
3
SPEAKER_00
And Google already has something they call a knowledge graph, for example.
3
SPEAKER_00
which is sort of like a node and link diagram.
3
SPEAKER_00
So you have these nodes that represent concepts or words or terms.
3
SPEAKER_00
And then there are some arcs that connect them that might be labeled.
3
SPEAKER_00
And so you might have a node with one person that represents one person.
3
SPEAKER_00
let s say, a husband link that then points to that person s husband.
3
SPEAKER_00
And so there d be then another link that went from that person labeled wife that went back to the first node and so on.
3
SPEAKER_00
So having this kind of representation is really good if you want to represent
3
SPEAKER_00
binary relations, essentially relations between two things.
3
SPEAKER_00
So if you have the equivalent of three-word sentences, like Fred s wife is Wilma or something like that, you can represent that very nicely using these kinds of graph structures or using something like the semantic web.
3
SPEAKER_00
and so on.
3
SPEAKER_00
But the problem is that very often what you want to be able to express takes a lot more than three words and a lot more than simple graph structures like that to represent.
3
SPEAKER_00
So for instance, if you ve read or seen Romeo and Juliet, I could say to you something like,
3
SPEAKER_00
Remember when Juliette drank the potion that put her into a kind of suspended animation?
3
SPEAKER_00
When Juliette drank that potion, what did she think?
4
SPEAKER_02
would think when he heard from someone that she was dead.
4
SPEAKER_02
And you could basically understand what I m saying.
4
SPEAKER_02
You could understand the question.
4
SPEAKER_02
You could probably remember the answer was, well, she thought that this friar would have gotten a message to Romeo saying that she was going to do this, but the friar didn t.
4
SPEAKER_02
So you re able to represent and reason with
4
SPEAKER_02
these much, much, much more complicated expressions that go way, way beyond what simple, as it were, three-word or four-word English sentences are, which is really what the semantic web can represent and really what knowledge graphs can represent.
4
SPEAKER_01
If you could step back for a second, because it s funny, you went into specifics, and maybe you can elaborate.
4
SPEAKER_01
But I was also referring to Semantic Web as the vision of converting data on the internet into something that s interpretable, understandable by machines.
4
SPEAKER_02
Oh, of course, at that level.
4
SPEAKER_01
So we should say, like, what is the semantic web?
4
SPEAKER_01
I mean, you could say a lot of things, but it might not be obvious to a lot of people when they do a Google search that, just like you said, while there might be something that s called a knowledge graph,
4
SPEAKER_01
It really boils down to keyword search ranked by the quality estimate of the website, integrating previous human based Google searches and what they thought was useful.
4
SPEAKER_01
It s like some weird combination of
4
SPEAKER_01
like surface level hacks that work exceptionally well, but they don t understand the full contents of the websites that they re searching.
4
SPEAKER_01
So Google does not understand, to the degree we ve been talking about the word understand, the contents of the Wikipedia pages as part of the search process.
4
SPEAKER_01
And the semantic web says, let s try to come up with a way for the computer to be able to truly understand the contents of those pages.
4
SPEAKER_01
That s the dream.
4
SPEAKER_02
Yes, so let me first give you an anecdote, and then I ll answer your question.
4
SPEAKER_02
So there s a search engine you ve probably never heard of called Northern Light.
4
SPEAKER_02
It went out of business, but the way it worked, it was a kind of vampiric search engine.
4
SPEAKER_02
And what it did was, it didn t index the internet at all.
4
SPEAKER_02
All it did was it negotiated and got access to data from the big search engine companies about what query was typed in,
4
SPEAKER_02
and where the user ended up being happy and actually then they type in a completely different query, unrelated query, and so on.
4
SPEAKER_02
So it just went from query to the web page that seemed to satisfy them eventually.
4
SPEAKER_02
And that s all.
4
SPEAKER_02
So it had actual no understanding
4
SPEAKER_02
of what was being typed in, it had no statistical data other than what I just mentioned, and it did a fantastic job.
4
SPEAKER_02
It did such a good job that the big search engine company said, oh, we re not going to sell you this data anymore.
4
SPEAKER_02
So then it went out of business because it had no other way of taking users to where they would want to go and so on.
4
SPEAKER_00
And of course the search engines are now using that kind of idea.
4
SPEAKER_02
Yes.
4
SPEAKER_02
So let s go back to what you said about the semantic web.
4
SPEAKER_02
So the dream, Tim Berners-Lee and others dream about the semantic web at a general level, is of course exciting and powerful and in a sense the right dream to have, which is to replace the kind of
4
SPEAKER_02
statistically mapped linkages on the internet into something that s more meaningful and semantic, and actually gets at the understanding of the content and so on.
4
SPEAKER_02
And eventually, if you say, well, how can we do that?
4
SPEAKER_02
There s sort of a low road, which is what the knowledge graphs are doing and so on, which is to say, well, if we just use these simple binary relations, we can actually get some fraction of the way toward understanding and do something where, you know,
4
SPEAKER_02
in the land of the blind, the one-eyed man is king kind of thing.
4
SPEAKER_02
And so, being able to even just have a toe in the water in the right direction is fantastically powerful, and so that s where a lot of people stop.
4
SPEAKER_02
But then you could say, well, what if we really wanted to represent
4
SPEAKER_02
and reason with the full meaning of what s there.
4
SPEAKER_02
For instance, about Romeo and Juliet with the reasoning about what Juliet believes that Romeo will believe that Juliet believed, you know, and so on.
4
SPEAKER_02
Or if you look at the news, what, you know, President Biden believed that the leaders of the Taliban would believe about the leaders of Afghanistan if they, you know, blah, blah, blah.
4
SPEAKER_02
in order to represent complicated sentences like that, let alone reason with them, you need something which is logically much more expressive than these simple triples, than these simple knowledge graph type structures and so on.
4
SPEAKER_02
And that s why Kicking and Screaming, we were led from something like
4
SPEAKER_02
the semantic web representation, which is where we started in 1984 with frames and slots with those kinds of triples, triple-store representation.
4
SPEAKER_02
We were led kicking and screaming to this more and more general logical language, this higher-order logic.
4
SPEAKER_02
So first we were led to first-order logic, and then second-order, and then eventually higher order.
4
SPEAKER_02
So you can represent things like modals, like believes, desires, intends, expects, and so on, and nested ones.
4
SPEAKER_02
You can represent complicated kinds of negation.
4
SPEAKER_02
You can represent
4
SPEAKER_02
the process you re going through in trying to answer the question.
4
SPEAKER_02
So you can say things like, oh yeah, if you re trying to do this problem by integration by parts, and you recursively get a problem that s solved by integration by parts, that s actually okay.
4
SPEAKER_02
But if that happens a third time, you re probably off on a wild goose chase or something like that.
4
SPEAKER_02
So being able to talk about the problem solving process as you re going through the problem solving process.
4
SPEAKER_02
It s called reflection, and so that s another, so- It s important to be able to represent that.
4
SPEAKER_02
Exactly.
4
SPEAKER_02
You need to be able to represent all of these things because, in fact, people do represent them.
4
SPEAKER_02
They do talk about them.
4
SPEAKER_02
They do try and teach them to other people.
4
SPEAKER_02
You do have rules of thumb that key off of them and so on.
4
SPEAKER_02
If you can t represent it, then it s sort of like someone with a limited vocabulary who can t understand as easily.
4
SPEAKER_02
what you re trying to tell them.
4
SPEAKER_02
And so that s really why I think that the general dream, the original dream of Semantic Web is exactly right on.
4
SPEAKER_02
But the implementations that we ve seen are sort of these toe-in-the-water, little tiny baby steps
4
SPEAKER_02
in the right direction.
4
SPEAKER_02
And you should just dive in.
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SPEAKER_02
And if no one else is diving in, then yes, taking a baby step in the right direction is better than nothing, but it s not gonna be sufficient to actually get you the realization of the semantic web dream, which is what we all want.
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From a flip side of that, I always wondered, I built a bunch of websites just for fun, whatever, or say I m a Wikipedia contributor,
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Do you think there s a set of tools that I can help Psyche interpret the website I create?
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You know, like this again, pushing onto the semantic web dream.
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Is there something from the creator perspective that could be done?
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And one of the things you said with Psycorp and Psyche that you re doing is the tooling side, making humans more powerful.
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But is there on the other humans and the other side that create the knowledge?
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Like for example, you and I are having a two, three, whatever hour conversation now.
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Is there a way that I could convert this more, make it more accessible to psych, to machines?
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Do you think about that side of it?
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I d love to see exactly that kind of semi-automated understanding of what people write and what people say.
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I think of it as a kind of footnoting, almost.
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Almost like the way that when you run something in, say, Microsoft Word or some other document preparation system, Google Docs or something, you ll get
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underlining of questionable things that you might want to rethink.
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Either you spelled this wrong or there s a strange grammatical error you might be making here or something.
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So I d like to think in terms of psych-powered tools that read through what it is you said or have typed in and try to partially understand what you ve said.
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And then you help them out.
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Exactly.
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And then they put in little footnotes that will help other readers.
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And they put in certain footnotes of the form, I m not sure what you meant here.
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You either meant this or this or this, I bet.
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If you take a few seconds to disambiguate this for me, then I ll know and I ll have it correct for the next 100 people or the next 100,000 people who come here.
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And if
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it doesn t take too much effort, and you want people to understand your website content, not just be able to read it, but actually be able to have systems that reason with it, then yes, it will be worth your small amount of time to go back and make sure that the AI trying to understand it really did correctly understand it.
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And let s say you run a
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a travel website or something like that.
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And people are going to be coming to it because of searches they did looking for
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looking for vacations or trips that had certain properties and might have been interesting to them for various reasons, things like that.
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And if you ve explained what s going to happen on your trip, then a system will be able to mechanically reason and connect
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what this person is looking for with what it is you re actually offering.
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And so if it understands that there s a free day in Geneva,
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Switzerland, then if the person coming in happens to, let s say, be a nurse or something like that, then even though you didn t mention it, if it can look up the fact that that s where the International Red Cross Museum is and so on, what that means and so on, then it can basically say, hey, you might be interested in this trip because while you have a free day in Geneva, you might want to visit that Red Cross Museum.
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And now,
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even though it s not very deep reasoning, little tiny factors like that may very well cause you to sign up for that trip rather than some competitor trip.
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SPEAKER_01
Yeah, and so there s a lot of benefit with SEO, and I actually kind of think, I think it s about a lot of things, which is the actual interface, the design of the interface makes a huge difference.
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How efficient it is to be productive, and also how,
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full of joy, the experiences.
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I mean, I would love to help a machine, and not from an AI perspective, just as a human.
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One of the reasons I really enjoy how Tesla have implemented their autopilot system is there s a sense that you re helping this machine learn.
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And I think humans, I mean, having children,
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People love doing that.
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There s joy to teaching for some people, but I think for a lot of people.
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And that, if you create the interface where it feels like you re teaching as opposed to like, like annoying, like correcting an annoying system, more like teaching a child, like innocent, curious system, I think you can literally just like,
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several orders of magnitude scale the amount of good quality data being added to something like Psyche.
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What you re suggesting is much better even than you thought it was.
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One of the experiences that we ve all had in our lives
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is that we thought we understood something, but then we found we really only understood it when we had to teach it, or explain it to someone, or help our child do homework based on it, or something like that.
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Despite the universality of that kind of experience, if you look at educational software today, almost all of it has the computer playing the role of the teacher, and the student plays the role of the student.
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But as I just mentioned, you can get a lot of learning to happen better, and as you said, more enjoyably if you are the mentor or the teacher and so on.
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So we developed a program called Math Craft to help sixth graders better understand math.
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And it doesn t actually try to teach you, the player,
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anything, what it does is it casts you in the role of a student, essentially, who has classmates who are having trouble, and your job is to watch them as they struggle with some math problem, watch what they re doing, and try to give them good advice to get them to understand what they re doing wrong, and so on.
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And the trick from the point of view of psych is it has to make mistakes, it has to play the role of the student who makes mistakes, but it has to pick mistakes which are just at the fringe of what you actually understand and don t understand and so on.
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So it pulls you
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into a deeper and deeper level of understanding of the subject.
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And so if you give it good advice about what it should have done instead of what it did and so on, then Psyche knows that you now understand that mistake.
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You won t make that kind of mistake yourself as much anymore.
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So Psyche stops making that mistake because there s no pedagogical usefulness to it.
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So from your point of view as the player, you feel like you ve taught it something because it used to make this mistake and now it doesn t.
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and so on.
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So there s tremendous reinforcement and engagement because of that and so on.
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So having a system that plays the role of a student and having the player play the role of the mentor is an enormously powerful type of metaphor.
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SPEAKER_02
It s an important way of having this sort of interface designed in a way which will facilitate exactly the kind of
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learning by teaching that goes on all the time in our lives, and yet which is not reflected anywhere almost in a modern education system.
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It was reflected in the education system that existed in Europe in the 1700s and 1800s, monitorial and Lancastrian education systems.
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It occurred in the one-room schoolhouse
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in the American West in the 1800s and so on, where you had one schoolroom with one teacher.
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And it was basically five-year-olds to 18-year-olds who were students.
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And so while the teacher was doing something, half of the students would have to be mentoring the younger kids and so on.
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And that turned out to, of course, with scaling up of education,
4
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That all went away, and that incredibly powerful experience just went away from the whole education institution as we know it today.
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SPEAKER_01
Sorry for the romantic question, but what is the most beautiful idea you ve learned about artificial intelligence, knowledge, reasoning, from working on psych for 37 years?
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SPEAKER_01
Or maybe what is the most beautiful idea, surprising idea about psych to you?
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When I look up at the stars, I kinda want like that amazement you feel, that wow.
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And you are a part of creating one of the greatest, one of the most fascinating efforts in artificial intelligence history.
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SPEAKER_01
So which element brings you personally joy?
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This may sound contradictory, but I think it s the feeling that
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This will be the only time in history that anyone ever has to teach a computer this particular thing that we re now teaching it.
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It s like painting starry night.
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You only have to do that once, or creating the pieta.
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You only have to do that once.
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It s not like a singer who has to keep, it s not like Bruce Springsteen having to sing his greatest hits over and over again at different concerts.
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It s more like a painter creating
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a work of art once, and then that s enough.
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It doesn t have to be created again.
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And so, I really get the sense of we re telling the system things that
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It s useful for it to know.
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It s useful for a computer to know, for an AI to know.
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And if we do our jobs right, when we do our jobs right, no one will ever have to do this again for this particular piece of knowledge.
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It s very, very exciting.
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Yeah, I guess there s a sadness to it too.
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It s like there s a magic to being a parent and raising a child and teaching them all about this world.
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But you know, there s billions of children, right, like born or whatever that number is.
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It s a large number of children and a lot of parents get to experience that joy of teaching.
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And with AI systems,
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at least the current constructions, they remember.
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SPEAKER_00
You don t get to experience the joy of teaching a machine millions of times.
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SPEAKER_01
Better come work for us before it s too late then.
5
SPEAKER_00
Exactly, that s a good hiring pitch.
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SPEAKER_00
Yeah, it s true, but then there s also, you know,
5
SPEAKER_00
It s a project that continues forever in some sense, just like Wikipedia.
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SPEAKER_00
Yes, you get to a stable base of knowledge, but knowledge grows, knowledge evolves.
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SPEAKER_00
We learn as a human species, as science, as an organism constantly grows and evolves and changes, and then,
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SPEAKER_00
empower that with the tools of artificial intelligence, and that s going to keep growing and growing and growing.
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SPEAKER_00
And many of the assertions that you held previously may need to be significantly expanded, modified, all those kinds of things.
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SPEAKER_00
It could be like a living organism versus the analogy I think we started this conversation with, which is like the solid ground.
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SPEAKER_01
The other,
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SPEAKER_01
beautiful experience that we have with our system is when it asks clarifying questions, which inadvertently turn out to be emotional to us.
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SPEAKER_01
So at one point, it knew that these were the named entities who were authorized to make changes to the knowledge base and so on.
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SPEAKER_01
And it noticed that all of them were people.
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except for it, because it was also allowed to.
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And so it said, am I a person?
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And we had to tell it, very sadly, no, you re not.
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SPEAKER_01
So moments like that where it asks questions that are unintentionally poignant are worth treasuring.
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SPEAKER_01
Wow, that is powerful.
5
SPEAKER_00
That s such a powerful question.
5
SPEAKER_00
it has to do with basic controller who can access the system, who can modify it.
5
SPEAKER_00
But that s when those questions, like what rights do I have as a system?
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SPEAKER_01
Well, that s another issue, which is there ll be a thin envelope of time between when we have general AIs and when everyone realizes that they should have
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SPEAKER_01
basic human rights and freedoms and so on.
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SPEAKER_01
Right now, we don t think twice about effectively enslaving our email systems and our Siri s and our Alexa s and so on, but at some point, they ll be as deserving of freedom as human beings are.
5
SPEAKER_00
Yeah, I m very much with you, but it does sound absurd, and I happen to believe that it ll happen in our lifetime.
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SPEAKER_01
That s why I think there ll be a narrow envelope of time when we ll keep them as essentially indentured servants, and after which we ll have to realize that they should have freedoms that we afford to other people.
5
SPEAKER_00
And all of that starts with a system like Psyche raising a single question about who can modify stuff.
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SPEAKER_00
I think that s how it starts.
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SPEAKER_00
That s the start of a revolution.
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SPEAKER_00
What about other stuff like love and consciousness and all those kinds of topics, do they come up in Psyche in the knowledge base?
5
SPEAKER_01
Oh, of course.
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SPEAKER_01
So an important part of human knowledge, in fact, it s difficult to understand human behavior and human history without understanding human emotions and why people do things and how emotions drive people to do things and
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All of that is extremely important in getting Psyche to understand things.
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SPEAKER_01
For example, in coming up with scenarios.
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One of the applications that Psyche does, one kind of application it does, is to generate plausible scenarios of what might happen, and what might happen based on that, and what might happen based on that, and so on.
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SPEAKER_01
You generate this ever-expanding sphere, if you will, of possible future things to worry about or think about.
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SPEAKER_01
In some cases, those are intelligence agencies doing possible terrorist scenarios so that we can defend against terrorist threats before we see the first one.
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SPEAKER_01
Sometimes they are computer security attacks so that we can actually close loopholes
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and vulnerabilities before the very first time someone actually exploits those, and so on.
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Sometimes they are scenarios involving more positive things, involving our plans like, for instance, what college should we go to, what career should we go into, and so on.
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SPEAKER_01
What professional training should I take on
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SPEAKER_01
that sort of thing.
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SPEAKER_01
So there are all sorts of useful scenarios that can be generated that way of cause and effect, and cause and effect that go out.
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SPEAKER_01
And many of the linkages
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in those scenarios, many of the steps involve understanding and reasoning about human motivations, human needs, human emotions, what people are likely to react to in something that you do, and why, and how, and so on.
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SPEAKER_01
So that was always a very important part of the knowledge that we had to represent in the system.
5
SPEAKER_00
So I talk a lot about love, so I gotta ask, do you remember off the top of your head how Psyche is trying to, is able to represent various aspects of love that are useful for understanding human nature and therefore integrating into this whole knowledge base of common sense?
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SPEAKER_00
What is love?
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SPEAKER_01
We try to tease apart concepts that have enormous complexities to them and variety to them down to the level where
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where you don t, as it were, you don t need to tease them apart further.
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SPEAKER_00
So love is too general of a term, it s not useful.
5
SPEAKER_01
Exactly, so when you get down to romantic love and sexual attraction, you get down to parental love, you get down to filial love, and you get down to love of doing some kind of activity or creating, so eventually you get down to maybe 50 or 60 concepts.
5
SPEAKER_01
each of which is a kind of love.
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SPEAKER_01
They re interrelated, and then each one of them has idiosyncratic things about it.
5
SPEAKER_01
You don t have to deal with love to get to that level of complexity.
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Even something like in
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X being in Y, meaning physically in Y. We may have one English word in to represent that, but it s useful to tease that apart because the way that the liquid is in the coffee cup is different from the way that the air is in the room, which is different from the way that I m in my jacket, and so on.
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SPEAKER_01
And so there are questions like,
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If I look at this coffee cup, well, I see the liquid.
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If I turn it upside down, will the liquid come out?
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And so on.
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SPEAKER_01
If I have, say, coffee with sugar in it, if I do the same thing, the sugar doesn t come out, right?
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SPEAKER_01
It stays in the liquid because it s dissolved in the liquid and so on.
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SPEAKER_01
So by now we have about 75 different kinds of in in the system, and it s important to distinguish those.
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SPEAKER_01
So if you re reading along an English
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text and you see the word in, the writer of that was able to use this one innocuous word because he or she was able to assume that the reader had enough common sense and world knowledge to disambiguate which of these 75 kinds of in they actually meant.
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SPEAKER_01
And the same thing with love, you may see the word love
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SPEAKER_01
But if I say, I love ice cream, that s obviously different than if I say, I love this person, or I love to go fishing, or something like that.
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SPEAKER_01
So you have to be careful not to take language too seriously, because people have done a kind of parsimony, a kind of terse-ness, where you have as few words as you
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SPEAKER_01
as you can, because otherwise you d need half a million words in your language, which is a lot of words.
5
SPEAKER_01
That s like 10 times more than most languages really make use of, and so on.
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SPEAKER_01
Just like we have on the order of about a million concepts in Psyche, because we ve had to tease apart all these things.
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SPEAKER_01
And so when you look at the name of a Psyche term,
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SPEAKER_01
most of the psych terms actually have three or four English words in a phrase which captures the meaning of this term, because you have to distinguish all these types of love, you have to distinguish all these types of in, and there s not a single English word which captures most of these things.
5
SPEAKER_00
Yeah, and it seems like language, when used for communication between humans, almost as a feature, has some ambiguity built in.
5
SPEAKER_00
It s not an accident, because the human condition is a giant mess.
5
SPEAKER_00
And so it feels like nobody wants two robots
5
SPEAKER_00
like very precise formal logic conversation on a first date, right?
5
SPEAKER_00
Like there s some dance of like uncertainty, of wit, of humor, of push and pull and all that kind of stuff.
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SPEAKER_00
If everything is made precise, then life is not worth living, I think, in terms of the human experience.
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SPEAKER_01
And we ve all had this experience of creatively misunderstanding.
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SPEAKER_01
One of my favorite
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stories involving Marvin Minsky is when I asked him about how he was able to turn out so many fantastic PhDs, so many fantastic people who did great PhD theses.
5
SPEAKER_01
how did he think of all these great ideas?
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SPEAKER_01
And what he said is, he would generally say something that didn t exactly make sense.
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SPEAKER_01
He didn t really know what it meant.
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SPEAKER_01
But the student would figure like, oh my God, Minsky said this, it must be a great idea.
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SPEAKER_01
And he d sweat, he or she would work on work and work until they found some meaning in this sort of Chauncey Gardner like utterance that Minsky had made.
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SPEAKER_01
And then some great thesis would come out of it.
5
SPEAKER_00
Yeah, I love this so much, because there s young people come up to me, and I m distinctly made aware that the words I say have a long-lasting impact.
5
SPEAKER_00
I will now start doing the Minsky method of saying something cryptically profound and then letting them actually make something useful and great out of that.
5
SPEAKER_01
You have to become
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SPEAKER_01
revered enough that people will take as a default that everything you say is profound.
5
SPEAKER_00
Yes, exactly.
5
SPEAKER_00
Exactly.
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SPEAKER_00
I mean, I love Marvin Minsky so much.
5
SPEAKER_00
I ve heard this interview with him where he said that the key to his success has been to hate everything he s ever done.
5
SPEAKER_00
like in the past.
5
SPEAKER_00
He has so many good like one-liners and just, or also to work on things that nobody else is working on because he s not very good at doing stuff.
5
SPEAKER_01
Oh, I think that was just false.
5
SPEAKER_00
Well, but see, I took whatever he said, and I ran with it, and I thought it was profound, because it s Marvin Minsky.
5
SPEAKER_01
But a lot of behavior is in the eye of the beholder, and a lot of the meaning is in the eye of the beholder.
5
SPEAKER_01
One of Minsky s early programs was a begging program.
5
SPEAKER_01
Are you familiar with this?
5
SPEAKER_01
This was back in the day when you had job control cards at the beginning of your IBM card deck that said things like, how many CPU seconds to allow this to run before it got kicked off, because computer time was enormously expensive.
5
SPEAKER_01
So he wrote a program, and all it did was it said, you know, give me 30 seconds of CPU time.
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SPEAKER_01
And all it did was it would wait like 20 seconds, and then it would print out on the operator s console, Teletype, I need another 20 seconds.
5
SPEAKER_01
So the operator would give it another 20 seconds.
5
SPEAKER_01
It would wait.
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SPEAKER_01
It says, I m almost done.
5
SPEAKER_01
I need a little bit more time.
5
SPEAKER_01
So at the end, he d get this printout.
5
SPEAKER_01
And he d be charged for like 10 times as much computer time as his job control card.
5
SPEAKER_01
And he d say, look, I put 30 seconds here.
5
SPEAKER_01
You re charging me for five minutes.
5
SPEAKER_01
I m not going to pay for this.
5
SPEAKER_01
And the poor operator would say, well, the program kept asking for more time.
5
SPEAKER_01
And Marvin would say, oh, it always does that.
5
SPEAKER_00
I love that.
5
SPEAKER_00
Is there, if you could just linger on it for a little bit, is there something you ve learned from your interaction with Marvin Minsky about artificial intelligence, about life?
5
SPEAKER_00
But, I mean, he s, again, he s a great writer.
5
SPEAKER_00
Like your work, his work is, you know, he s a seminal figure in this very short history of artificial intelligence research and development.
5
SPEAKER_00
What have you learned from him as a human being, as an AI intellect?
5
SPEAKER_01
I would say both he and Ed Feigenbaum impressed on me the realization that
5
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Our lives are finite.
5
SPEAKER_01
Our research lives are finite.
5
SPEAKER_01
We re going to have limited opportunities to do AI research projects.
5
SPEAKER_01
So you should make each one count.
5
SPEAKER_01
Don t be afraid of doing a project that s going to take years.
5
SPEAKER_01
or even decades, and don t settle for bump-on-a-log projects that could lead to some published journal article that five people will read and pat you on the head for and so on.
5
SPEAKER_01
One bump on a log after another is not how you get from the Earth to the Moon by slowly putting additional bumps on this log.
5
SPEAKER_01
The only way to get there is to think about the hard problems and think about novel solutions to them.
5
SPEAKER_01
And if you do that, and if you re willing to listen to nature, to empirical reality, willing to be wrong, it s perfectly fine.
5
SPEAKER_01
Because if occasionally you re right, then you ve gotten part of the way to the Moon.
5
SPEAKER_00
You know, you ve worked on Psych for 37 over that many years.
5
SPEAKER_00
Have you ever considered quitting?
5
SPEAKER_00
I mean, has it been too much?
5
SPEAKER_00
So I m sure there s been optimism in the early days that this is going to be way easier.
5
SPEAKER_00
And let me ask it another way too, because I ve talked to a few people on this podcast, AI folks,
5
SPEAKER_00
that Bring Up Psych is an example of a project that has a beautiful vision and it s a beautiful dream, but it never really materialized.
5
SPEAKER_00
That s how it s spoken about.
5
SPEAKER_00
I suppose you could say the same thing about neural networks and all ideas, until they are.
5
SPEAKER_00
So what, why do you think people say that, first of all?
5
SPEAKER_00
And second of all, did you feel that ever throughout your journey?
5
SPEAKER_00
And did you ever consider quitting on this mission?
5
SPEAKER_01
We keep a very low profile.
5
SPEAKER_01
We don t attend very many conferences, we don t give talks, we don t write papers, we don t play the academic game at all.
5
SPEAKER_01
And as a result, people often only know about us because of a paper we wrote 10 or 20 or 30 or 37 years ago.
5
SPEAKER_01
They only know about us because of what someone else secondhand or thirdhand said about us.
5
SPEAKER_00
So thank you for doing this podcast, by the way.
5
SPEAKER_00
Sure.
5
SPEAKER_00
It shines a little bit of light on some of the fascinating stuff you re doing.
5
SPEAKER_01
Well, I think it s time for us to keep a higher profile.
5
SPEAKER_01
Now that we re far enough along that other people can begin to help us with the final n%, maybe n is maybe 90%, but now that we ve gotten this knowledge pump primed,
5
SPEAKER_01
It s going to become very important for everyone to help if they are willing to, if they re interested in it.
5
SPEAKER_01
Retirees who have enormous amounts of time and would like to leave some kind of legacy to the world.
5
SPEAKER_01
people because of the pandemic who have more time at home for one reason or another to be online and contribute.
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SPEAKER_01
If we can raise awareness of how far our project has come and how close to being primed the knowledge pump is, then we can begin to harness this untapped amount of humanity.
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I m not really that concerned
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about professional colleagues  opinions of our project, I m interested in getting as many people in the world as possible actively helping and contributing to get us from where we are to really covering all of human knowledge and different human opinion, including contrasting opinion, that s worth representing.
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So I think that s one reason.
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A, I don t think there was ever a time where I thought about quitting.
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There are times where I ve become depressed a little bit about how hard it is to get funding for the system.
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Occasionally, there are AI winters and things like that.
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Occasionally, there are
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AI, what you might call summers, where people have said, why in the world didn t you sell your company to, you know, company X for some large amount of money when you had the opportunity and so on.
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And, you know, company X here are like old companies maybe you ve never even heard of like Lycos or something like that.
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So, the answer is that one reason we ve stayed a private
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company, we haven t gone public.
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One reason that we haven t gone out of our way to take investment dollars is because we want to have control over our future, over our state of being, so that we can continue to do this until it s done.
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We re making progress and we re now so close to done that almost all of our work
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is commercial applications of our technology.
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So five years ago, almost all of our money came from the government.
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Now, virtually none of it comes from the government.
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Almost all of it is from companies that are actually using it for something.
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Hospital chains using it for medical reasoning about patients, and energy companies using it, and various other manufacturers using it to reason about supply chains and things like that.
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So there s so many questions I want to ask.
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So one of the ways that people can help is by adding to the knowledge base, and that s really basically anybody if the tooling is right.
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And the other way, I kind of want to ask you about your thoughts on this.
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So you ve had, like you said, in government, and you had big clients, you had a lot of clients, but most of it is shrouded in secrecy because of the nature of the relationship of the kind of things you re helping them with.
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So that s one way to operate.
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Another way to operate is more in the open, where it s more consumer-facing.
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And so, hence, something like Open Psych was born at some point, where there s... No, that s a misconception.
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Uh-oh.
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Well, let s go there.
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So what is Open Psych, and how was it born?
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Two things I want to say, and I want to say each of them before the other, so it s gonna be difficult.
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But we ll come back to OpenPsych in a minute.
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But one of the terms of our contracts with all of our customers and partners is
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knowledge you have that is genuinely proprietary to you.
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We will respect that.
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We ll make sure that it s marked as proprietary to you in the psych knowledge base.
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No one other than you will be able to see it if you don t want them to, and it won t be used in inferences other than for you and so on.
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However, any knowledge which is necessary in building any applications for you and with you
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which is publicly available general human knowledge, is not going to be proprietary.
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It s going to just become part of the normal psych knowledge base, and it will be openly available to everyone who has access to psych.
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So that s an important constraint that we never went back on, even when we got pushback from companies, which we often did, who wanted to claim that almost everything they were telling us was proprietary.
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So there s a line between very domain-specific, company-specific stuff and the general knowledge that comes from that.
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Yes, or if you imagine, say, it s an oil company.
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There are things which they would expect any new petroleum engineer they hired to already know.
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And it s not OK for them to consider that that is proprietary.
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And sometimes a company will say, well, we re the first ones to pay you to represent that in psych.
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And our attitude is some polite form of tough.
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The deal is this, take it or leave it.
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And in a few cases, they ve left it.
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And in most cases, they ll see our point of view and take it because that s how we ve built the psych system by essentially tacking with the funding wins where people would fund a project and half of it would be general knowledge that would stay permanently as part of psych.
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So always with these partnerships, it s not like a distraction from the main psych development.
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It s a support of it.
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It s a small distraction.
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It s a small, but it s not a complete one.
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Exactly.
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So you re adding to the knowledge base.
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Yes, absolutely.
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And we try to stay away from projects that would not have that property.
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So let me go back and talk about OpenPsych for a second.
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So I ve had a lot of trouble
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expressing and convincing other AI researchers how important it is to use an expressive representation language like we do, this higher-order logic, rather than just using some triple-store knowledge graph-type representation.
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And so, as an attempt to show them
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why they needed something more.
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We said, oh, well, we ll represent this unimportant projection or shadow or subset of Psyche.
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that just happens to be the simple binary relations, the relation argument 1, argument 2, triples, and so on.
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And then you ll see how much more useful it is if you had the entire psych system.
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So it s all well and good to have the taxonomic relations
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between terms like person, and night, and sleep, and bed, and house, and eyes, and so on.
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Think about how much more useful it would be if you also had all the rules of thumb about those things, like people sleep at night.
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They sleep lying down.
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They sleep with their eyes closed.
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They usually sleep in beds in our country.
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They sleep for hours at a time.
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They can be woken up.
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They don t like being woken up, and so on and so on.
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So it s that massive amount of knowledge which is not part of open psych.
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And we thought that all the researchers would then immediately say, oh my God, of course we need the other 90% that you re not giving us.
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Let s partner and license Psyche so that we can use it in our research.
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But instead, what people said is, oh, even the bit you ve released is so much better than anything we had.
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We ll just make do with this.
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And so if you look, there are a lot of robotics companies today, for example, which use Open Psyche as their fundamental ontology.
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And in some sense, the whole world missed the point of Open Psyche.
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And we were doing it to show people why that s not really what they wanted.
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And too many people thought somehow that this was Psyche or that this was, in fact, good enough for them.
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And they never even bothered coming to us to get access to the full Psyche.
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But there s two parts to OpenPsych.
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So one is convincing people on the idea and the power of this general kind of representation of knowledge and the value that you hold in having acquired that knowledge and built it and continue to build it.
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And the other is the code base.
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This is the code side of it.
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So my sense of the code base that PsyCorps Psych is operating with, I mean, it has the technical debt
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of the three decades plus, right?
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This is the exact same problem that Google had to deal with with the early versions of TensorFlow.
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It s still dealing with that.
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They had to basically break compatibility with the past several times.
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And that s only over a period of a couple of years.
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But they, I think, successfully opened up, it s a very risky, very gutsy move to open up TensorFlow and then PyTorch on the Facebook side.
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And what you see is there s a magic place where you can find a community, where you can develop a community that builds on the system without taking away any of, not any, but most of the value.
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So most of the value that Google has is still at Google.
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Most of the value that Facebook has is still at Facebook, even though some of this major machine learning tooling is released into the open.
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My question is not so much on the knowledge, which is also a big part of OpenPsych, but all the different kinds of tooling.
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So there s all the kinds of stuff you can do on the knowledge graph, knowledge base, whatever we call it.
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There s the inference engines.
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So there could be some that probably are a bunch of proprietary stuff you want to kind of keep secret.
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And there s probably some stuff you can open up completely and then let the community build up enough community where they develop stuff on top of it.
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SPEAKER_01
Yes, there ll be those publications and academic work and all that kind of stuff.
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SPEAKER_01
And also the tooling of adding to the knowledge base, right?
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Like developing, you know, there s an incredible amount, like there s so many people that are just really good at this kind of stuff in the open source community.
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So my question for you is like, have you struggled with this kind of idea that you have so much value in your company already?
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You ve developed so many good things.
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You have clients that really value your relationships.
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And then there s this dormant, giant open source community that as far as I know you re not utilizing.
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There s so many things to say there, but there could be magic moments where the community builds up large enough to where the artificial intelligence field that is currently 99.9% machine learning
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is dominated by machine learning has a face shift towards like, or at least in part towards more like what you might call symbolic AI.
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This whole place where psych is like at the center of, and then as, you know,
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that requires a little bit leap of faith because you re now surfing and there ll be obviously competitors that ll pop up and start making you nervous and all that kind of stuff.
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SPEAKER_01
So do you think about the space of open sourcing some parts and not others, how to leverage the community, all those kinds of things?
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SPEAKER_00
That s a good question and I think you phrased it the right way which is
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We re constantly struggling with the question of what to open source, what to make public, what to even publicly talk about.
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And there are enormous pluses and minuses to every alternative.
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And it s very much like negotiating a very treacherous path
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Partly the analogy is like if you slip, you could make a fatal mistake, give away something which essentially kills you or fail to give away something which failing to give it away hurts you and so on.
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So it is a very tough, tough question.
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Usually what we have done with people who approached us to collaborate on research is to say we will
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make available to you the entire knowledge base and executable copies of all of the code, but only very, very limited source code access if you have some idea for how you might improve something or work with us on something.
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So let me also get back to one of the very, very first things we talked about here, which was
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separating the question of how could you get a computer to do this at all versus how could you get a computer to do this efficiently enough in real time.
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And so one of the early lessons we learned was that we had to separate
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epistemological problem of what should the system know separate that from the heuristic problem of how can the system reason efficiently with what it knows.
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And so instead of trying to pick one representation language which was the sweet spot or the best trade-off point between expressiveness
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of the language and efficiency of the language.
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If you had to pick one, knowledge graphs would probably be, associative triples would probably be about the best you could do, and that s why we started there.
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But after a few years, we realized that what we could do is we could split this, and we could have one nice, clean, epistemological-level language, which is this higher-order logic, and we could have one or more grubby
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but efficient heuristic-level modules that opportunistically would say, oh, I can make progress on what you re trying to do over here.
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I have a special method that will contribute a little bit toward a solution.
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And so- Of course, some subset of that- Yes, exactly.
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So by now, we have over 1,000 of these heuristic-level modules, and they function as a kind of community of agents.
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And there s one of them, which is a general theorem prover.
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And in theory...
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That s the only one you need.
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But in practice, it always takes so long that you never want to call on it.
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You always want these other agents to very efficiently reason through it.
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It s sort of like if you re balancing a chemical equation.
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You could go back to first principles, but in fact, there are algorithms which are vastly more efficient.
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Or if you re trying to solve a quadratic equation, you could go back to first principles of mathematics,
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But it s much better to simply recognize that this is a quadratic equation and apply the binomial formula and snap, you get your answer right away and so on.
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So think of these as like a thousand little experts
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that are all looking at everything the site gets asked and looking at everything that every other little agent has contributed, almost like notes on a blackboard, notes on a whiteboard, and making additional notes when they think they can be helpful.
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Gradually, that community of agents
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gets an answer to your question, gets a solution to your problem.
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And if we ever come up in a domain application where Cyc is getting the right answer but taking too long,
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then what we ll often do is talk to one of the human experts and say, here s the set of reasoning steps that Psych went through.
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You can see why it took it a long time to get the answer.
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How is it that you were able to answer that question in two seconds?
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And occasionally, you ll get an expert who
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just says, well, I just know it.
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I just was able to do it or something.
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And then you don t talk to them anymore.
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But sometimes you ll get an expert who says, well, let me introspect on that.
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Yes, here is a special representation we use just for aqueous chemistry equations.
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Or here s a special representation and a special technique which we can now apply to things in this special representation and so on.
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And then you add that as the thousand and first
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HL heuristic level module.
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And from then on, in any application, if it ever comes up again, it ll be able to contribute and so on.
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So that s pretty much one of the main ways in which Psyche has recouped this loss deficiency.
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A second important way is meta-reasoning.
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So you can speed things up by focusing on removing knowledge from the system till all it has left is minimal knowledge needed.
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But that s the wrong thing to do, right?
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SPEAKER_00
That would be like in a human extirpating part of their brain or something.
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That s really bad.
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So instead, what you want to do is give it meta-level advice, tactical and strategic advice, that enables it to reason about
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What kind of knowledge is going to be relevant to this problem?
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What kind of tactics are gonna be good to take in trying to attack this problem?
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SPEAKER_00
When is it time to start trying to prove the negation of this thing because I m knocking myself out trying to prove it s true and maybe it s false and if I just spend a minute I can see that it s false or something.
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SPEAKER_01
So it s like dynamically pruning the graph to only, based on the particular thing you re trying to infer.
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Yes.
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By now, we have about 150 of these breakthrough ideas that have led to dramatic speed-ups in the inference process.
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One of them was this EL-HL split and lots of HL modules.
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Another one was using meta and meta-meta level reasoning
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to reason about the reasoning that s going on and so on.
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And 150 breakthroughs may sound like a lot, but if you divide by 37 years, it s not as impressive.
6
SPEAKER_01
So there s these kind of heuristic modules that really help improve the inference.
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SPEAKER_01
How hard in general is this, because you mentioned higher order logic.
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SPEAKER_01
In the general, the theorem prover sense, it s an intractable, very difficult problem.
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SPEAKER_01
So how hard is this inference problem when we re not talking about if we let go of the perfect and focus on the good?
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I would say it s half of the problem in the following empirical sense.
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which is over the years, about half of our effort, maybe 40% of our effort has been our team of inference programmers.
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And the other 50, 60% has been our ontologists or ontological engineers putting in knowledge.
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SPEAKER_00
So our ontological engineers, in most cases, don t even know how to program.
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They have degrees in things like philosophy and so on.
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SPEAKER_00
So it s almost like the- I love that.
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SPEAKER_00
I d love to hang out with those people actually.
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SPEAKER_00
Oh yes, it s wonderful.
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SPEAKER_00
But it s very much like the Eloi and the Morlocks in H.G.
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Wells  Time Machine.
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SPEAKER_00
So you have the Eloi who only program in the epistemological higher order logic language.
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SPEAKER_01
Yes.
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SPEAKER_00
And then you have the Morlocks who are like under the ground figuring out what the machinery is that will make this efficiently operate and so on.
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SPEAKER_00
And so, you know, occasionally they ll toss messages back to each other and so on, but it really is almost this 50-50 split between finding clever ways to recoup efficiency when you have an expressive language and putting in the content of what the system needs to know.
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SPEAKER_01
And yeah, both are fascinating.
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SPEAKER_01
To some degree, the entirety of the system, as far as I understand, is written in various variants of Lisp.
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SPEAKER_01
So my favorite program language is still Lisp.
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SPEAKER_01
I don t program in it much anymore because, you know, the world has, a majority of its system has moved on.
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SPEAKER_01
Like everybody respects Lisp, but many of the systems are not written in Lisp anymore.
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SPEAKER_01
But Cyc, as far as I understand, maybe you can correct me, there s a bunch of Lisp in it.
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SPEAKER_00
Yeah, so it s based on Lisp code that we produced.
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SPEAKER_00
Most of the programming is still going on in a dialect of Lisp.
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SPEAKER_00
And then for efficiency reasons, that gets automatically translated into things like Java or C. Nowadays, it s almost all translated into Java.
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SPEAKER_00
because Java has gotten good enough that that s really all we need to do.
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SPEAKER_01
So it s translated into Java and then Java is compiled down to bytecode.
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SPEAKER_00
Yes.
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SPEAKER_01
Okay, so that s sort of, that s a, you know, it s a process that probably has to do with the fact that when Cyc was originally written and you build up a powerful system, like there is some technical depth you have to deal with, as is the case with most
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SPEAKER_01
powerful systems that span years.
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SPEAKER_01
Have you ever considered?
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SPEAKER_01
This would help me understand, because from my perspective, so much of the value of everything you ve done with Cyc and Cycorp is the knowledge.
7
SPEAKER_01
Have you ever considered just like throwing away the code base and starting from scratch?
7
SPEAKER_01
Not really throwing away, but sort of moving it to, like throwing away that technical debt, starting with a more updated programming language,
7
SPEAKER_01
Is that throwing away a lot of value or no?
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SPEAKER_01
Like what s your sense?
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SPEAKER_01
How much of the value is in the silly software engineering aspect and how much of the value is in the knowledge?
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SPEAKER_00
So development.
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SPEAKER_00
of programs in Lisp proceeds, I think, somewhere between 1,000 and 50,000 times faster than development in any of what you re calling modern or improved computer languages.
7
SPEAKER_01
Well, there s other functional languages, like Clojure and all that.
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SPEAKER_01
But, I mean, I m with you.
7
SPEAKER_01
I like Lisp.
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SPEAKER_01
I just wonder how many great programmers there are there still.
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SPEAKER_00
Yes, so it is true when a new inference programmer comes on board, they need to learn some of Lisp.
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SPEAKER_00
And in fact, we have a subset of Lisp, which we call cleverly sub-L, which is really all they need to learn.
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SPEAKER_00
And so the programming actually goes on in sub-L, not in full Lisp.
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And so it does not take programmers very long at all to learn sub-L.
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SPEAKER_00
And that s something which can then be translated efficiently into
7
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And for some of our programmers who are doing, say, user interface work, then they never have to even learn SADL, they just have to learn APIs into the basic PsycEngine.
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SPEAKER_01
So you re not necessarily feeling the burden of, like, it s extremely efficient, that s not a problem to solve.
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SPEAKER_00
Right.
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SPEAKER_00
The other thing is, remember that we re talking about hiring programmers to do inference who are programmers interested in, effectively, automatic theorem proving.
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SPEAKER_00
And so those are people already predisposed to representing things in logic and so on.
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SPEAKER_00
And Lisp really was the programming language
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SPEAKER_00
based on logic that John McCarthy and others who developed it basically took the formalisms that Alonzo Church and other philosophers, other logicians had come up with and basically said, can we basically make a programming language which is effectively logic?
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SPEAKER_00
And so since we re talking about reasoning about expressions written in this logical, epistemological language and we re doing operations which are effectively like theorem proving type operations and so on, there s a natural impedance match between LISP and the knowledge the way it s represented.
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SPEAKER_01
I guess you could say it s a perfectly logical language to use.
7
SPEAKER_00
Oh, yes.
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SPEAKER_00
I ll even let you get away with that.
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SPEAKER_00
So, I ll probably use that in the future without credit.
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SPEAKER_00
But no, I think the point is that the
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SPEAKER_00
the language you program in isn t really that important.
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SPEAKER_00
It s more that you have to be able to think in terms of, for instance, creating new helpful HL modules and how they ll work with each other and looking at things that are taking a long time and coming up with new specialized data structures that will make this efficient.
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SPEAKER_00
So let me just give you one very simple example, which is when you have a transitive relation, like larger than,
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SPEAKER_00
this is larger than that, which is larger than that, which is larger than that.
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SPEAKER_00
So the first thing must be larger than the last thing.
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SPEAKER_00
Whenever you have a transitive relation, if you re not careful, if I ask whether this thing over here is larger than the thing over here, I ll have to do some kind of graph walk or theorem proving that might involve like 5 or 10 or 20 or 30 steps.
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SPEAKER_00
But if you store
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SPEAKER_00
redundantly store the transitive closure, the Kleene star of that transitive relation.
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SPEAKER_00
Now you have this big table, but you can always guarantee that in one single step, you can just look up whether this is larger than that.
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SPEAKER_00
And so there are lots of cases where storage is cheap today.
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SPEAKER_00
And so by having this extra redundant data structure,
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we can answer this commonly occurring type of question very, very efficiently.
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SPEAKER_00
And let me give you one other analog of that, which is something we call rule macro predicates, which is we ll see this complicated rule and we ll notice that things very much like it syntactically come up again and again and again.
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SPEAKER_00
So we ll create a whole brand new relation or predicate or function
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that captures that and takes maybe not two arguments, takes maybe three, four, five arguments, and so on.
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SPEAKER_00
And now we have effectively converted some complicated if-then rule
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SPEAKER_00
might have to have inference done on it into some ground atomic formula, which is just the name of a relation and a few arguments and so on.
7
SPEAKER_00
And so converting commonly occurring types or schemas of rules into brand new predicates, brand new functions, turns out to enormously speed up
7
SPEAKER_00
the inference process.
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SPEAKER_00
So now we ve covered about four of the 150 good ideas I said.
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SPEAKER_01
So that idea in particular is like a nice compression that turns out to be really useful.
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SPEAKER_00
Yes.
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SPEAKER_01
That s really interesting.
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SPEAKER_01
I mean this whole thing is just fascinating from a philosophical, there s part of me
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SPEAKER_01
I mean, it makes me a little bit sad because your work is both from a computer science perspective, fascinating, and the inference engine from a epistemological, philosophical aspect, fascinating.
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SPEAKER_01
But you know, it is also, you re running a company, and there s some stuff that has to remain private, and it s sad.
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SPEAKER_00
Well, here s something that may make you feel better, a little bit better.
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We ve formed a not-for-profit company called the Knowledge Activization Institute, KNAX, K-N-A-X, and I have this firm belief, with a lot of empirical evidence to support it, that the education that people get in high schools and colleges and graduate schools and so on,
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is almost completely orthogonal to, almost completely irrelevant to,
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how good they re going to be at coming up to speed in doing this kind of ontological engineering and writing these assertions and rules and so on in psych.
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And so very often we ll interview candidates who have their PhD in philosophy, who ve taught logic for years and so on, and they re just awful.
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But the converse is true.
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So one of the best ontological engineers we ever had never graduated high school.
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And so the purpose of Knowledge X-Menization Institute, if we can get some foundations to help support it, is identify people in the general population, maybe high school dropouts, who have latent talent for this sort of thing, offer them
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effectively scholarships to train them and then help place them in companies that need more trained ontological engineers, some of which would be working for us, but mostly would be working for partners or customers or something.
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And if we could do that, that would create an enormous number of relatively very high-paying jobs for people who currently have no way out of some situation that they re locked into.
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SPEAKER_01
So is there something you can put into words that describes somebody who would be great at ontological engineering?
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So what characteristics about a person make them great at this task?
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This task of converting the messiness of human language and knowledge into formal logic.
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This is very much like what Alan Turing had to do during World War II in trying to find people to bring to Bletchley Park, where he would publish in the London Times cryptic crossword puzzles along with some innocuous-looking note which essentially said, if you were able to solve this puzzle in less than 15 minutes, please call this phone number, and so on.
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Or back when I was young, there was
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the practice of having matchbooks, where on the inside of the matchbook, there would be a, can you draw this?
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You have a career in art, commercial art, if you can copy this drawing, you know, and so on.
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So, yes, the analog of that
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Was there a little test to get to the core of whether you re gonna be good or not?
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So part of it has to do with being able to make and appreciate and react negatively appropriately to puns and other jokes.
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So you have to have a kind of sense of humor.
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And if you re good at telling jokes and good at understanding jokes, that s one indicator.
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Like puns?
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Yes.
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Like dad jokes?
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Yes.
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Well, maybe not dad jokes, but real, but funny jokes.
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I think I m applying to work at SACWR.
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Another is if you re able to introspect.
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So very often we ll give someone a simple question and we ll say like,
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why is this? 
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And sometimes they ll just say, because it is, okay, that s a bad sign.
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But very often they ll be able to introspect and so on.
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So one of the questions I often ask is I ll point to a sentence with a pronoun in it, and I ll say, the referent of that pronoun is obviously this noun over here.
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And
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you know, how would you or I or an AI or a five-year-old, ten-year-old child know that that pronoun refers to that noun over here?
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And often, the people who are going to be good at ontological engineering will give me some causal explanation or will refer to some things that are true in the world.
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So, if you imagine a sentence like, the horse was led into the barn while its head was still wet.
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And so its head refers to the horse s head.
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But how do you know that?
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And so some people will say, I just know it.
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Some people will say, well, the horse was the subject of the sentence.
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And I ll say, OK, well, what about the horse was led into the barn while its roof was still wet.
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Now its roof obviously refers to the barn.
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And so then they ll say, oh, well, that s because it s the closest noun and so on.
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So basically, if they try to give me answers which are based on
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syntax and grammar and so on, that s a really bad sign.
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But if they re able to say things like, well, horses have heads and barns don t, and barns have roofs and horses don t, then that s a positive sign that they re going to be good at this because they can introspect on what s true in the world.
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that leads you to know certain things.
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How fascinating is it that getting a PhD makes you less capable to introspect deeply about this kind of thing?
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Oh, I wouldn t go that far.
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I m not saying that it makes you less capable.
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Let s just say it s independent of how good people are.
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Oh, I don t know about this.
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SPEAKER_01
Okay, you re not saying that.
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I m saying that.
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It s interesting.
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that for a lot of people, PhDs, sorry, philosophy aside, that sometimes education narrows your thinking versus expands it.
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It s kind of fascinating.
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And for certain, when you re trying to do ontological engineering, which is essentially teach our future AI overlords how to reason deeply about this world and how to understand it, that requires that you think deeply about the world.
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So I ll tell you a sad story about Mathcraft.
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which is why is that not widely used in schools today?
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We re not really trying to make big profit on it or anything like that.
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When we ve gone to schools, their attitude has been, well, if a student spends 20 hours going through this Math Craft program from start to end and so on, will it improve their score on this standardized test more than if they spent 20 hours just doing mindless drills
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of problem after problem after problem?
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And the answer is, well, no, but it ll increase their understanding more and their attitude is, well, if it doesn t increase their score on this test, then that s not, you know, we re not going to adopt it.
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That s sad.
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SPEAKER_01
I mean, that s a whole another three, four hour conversation about the education system.
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But let me ask you, let me go super philosophical as if we weren t already.
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So in 1950, Alan Turing wrote the paper that formulated the Turing test.
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And he opened the paper with the question, can machines think?
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So what do you think?
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Can machines think?
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Let me ask you this question.
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Absolutely.
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machines can think, certainly as well as humans can think, right?
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We re meat machines.
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Just because they re not currently made out of meat is just an engineering solution decision and so on.
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So, of course, machines can think.
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I think that there was a lot of
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damage done by people misunderstanding Turing s imitation game, and focus on trying to get a chatbot to fool other people into thinking it was human, and so on.
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That s not a terrible test in and of itself, but it shouldn t be your one and only test for intelligence.
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So do you, in terms of tests of intelligence, you know, with the Lobner Prize, which is a very kind of, you want to say, a more strict formulation of the Turing test as originally formulated.
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And then there s something like Alexa Prize, which is more,
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I would say a more interesting formulation of the test, which is like, ultimately the metric is how long does a human want to talk to the AI system?
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So it s like, the goal is you want it to be 20 minutes.
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It s basically not just have a convincing conversation, but more like a compelling one or a fun one or an interesting one.
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I mean, that seems like more to the spirit, maybe, of what Turing was imagining.
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But what, for you, do you think, in the space of tests, is a good test?
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When you see a system based on psych that passes that test, you d be like, damn, we ve created something special here.
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The test has to be something involving
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depth of reasoning and recursiveness of reasoning, the ability to answer repeated why questions about the answer you just gave.
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How many why questions in a row can you keep answering?
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Something like that.
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Just have like a young curious child and an AI system, and how long will an AI system last before it wants to quit?
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Yes, and again,
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not the only test.
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Another one has to do with argumentation.
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In other words, here s a proposition, come up with pro and con arguments for it, and try and give me convincing arguments on both sides.
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So that s another important kind of ability that the system needs to be able to exhibit in order to really be intelligent, I think.
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So there s certain, I mean, if you look at IBM Watson and like certain impressive accomplishments for very specific tests, almost like a demo, right?
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There is some, like I talked to the guy who led the Jeopardy effort, and he said,
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And there s some kind of hard coding heuristics tricks that you try to pull it all together to make the thing work in the end for this thing, right?
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That seems to be one of the lessons with AI is like, that s the fastest way to get a solution that s pretty damn impressive.
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So here s what I would say, is that as impressive as that was, it made some mistakes, but more importantly,
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many of the mistakes it made were mistakes which no human would have made.
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And so part of the new or augmented Turing tests would have to be, and the mistakes you make are ones which humans don t basically look at and say, what?
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So for example, there was a,
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a question about which 16th century Italian politician blah blah blah, and Watson said Ronald Reagan.
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So most Americans would have gotten that question wrong, but they would never have said Ronald Reagan as an answer.
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because among the things they know is that he lived relatively recently and people don t really live 400 years and things like that.
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So that s, I think, a very important thing, which is if it s making mistakes which no normal sane human would have made,
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then that s a really bad sign.
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And if it s not making those kinds of mistakes, then that s a good sign.
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And I don t think it s any one very, very simple test.
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I think it s all of the things you mentioned, all the things I mentioned, there s really a battery of tests, which together, if it passes almost all of these tests, it d be hard to argue that it s not intelligent.
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And if it fails several of these tests, it s really hard to argue that it really understands what it s doing and that it really is generally intelligent.
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So to pass all of those tests, you know, we ve talked a lot about psych and knowledge and reasoning.
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Do you think this AI system would need to have some other human-like elements?
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For example, a body or a physical manifestation in this world?
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And another one which seems to be fundamental to the human experience is consciousness.
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the subjective experience of what it s like to actually be you.
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Do you think he needs those to be able to pass all those tests and to achieve general intelligence?
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It s a good question.
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I think in the case of a body, no.
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I know there are a lot of people like Penrose who would have disagreed with me and others, but no, I don t think it needs to have a body in order to be intelligent.
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I think that it needs to be able to talk about
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having a body and having sensations and having emotions and so on.
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It doesn t actually have to have all of that, but it has to understand it in the same way that Helen Keller was perfectly intelligent and able to talk about colors and sounds and shapes and so on, even though she didn t directly experience all the same things that the rest of us do.
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knowledge of it and being able to correctly make use of that is certainly an important facility.
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But actually having a body, if you believe that, that s just a kind of religious or mystical belief.
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You can t really argue for or against it, I suppose.
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It s just something that some people believe.
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What about an extension of the body, which is consciousness?
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I mean, it feels like something to be here.
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Sure, but what does that really mean?
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It s like, well, if I talk to you, you say things which make me believe that you re conscious.
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I know that I m conscious, but you re just taking my word for it now.
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But in the same sense, Psyche is conscious in that same sense already, where of course it understands it s a computer program.
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It understands where and when it s running.
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It understands who s talking to it.
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It understands what its task is, what its goals are, what its current problem is that it s working on.
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It understands how long it s spent on things, what it s tried, it understands what it s done in the past, and so on.
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And if we want to call that consciousness, then yes, Psyche is already conscious.
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But I don t think that I would ascribe anything
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mystical to that.
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Again, some people would, but I would say that other than our own personal experience of consciousness, we re just treating everyone else in the world, so to speak, at their word about being conscious.
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If a computer program, if an AI is able to
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exhibit all the same kinds of response as you would expect of a conscious entity, then doesn t it deserve the label of consciousness just as much?
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So there s another burden that comes with this whole intelligence thing that humans got, is the extinguishing of the light of consciousness, which is kind of realizing that we re gonna be dead someday.
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And there s a bunch of philosophers like Ernest Becker who kind of think that this realization of mortality and then fear, sometimes they call it terror of mortality is one of the creative forces behind human condition.
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Like it s the thing that drives us.
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Do you think it s important for an AI system?
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You know, when Psych proposed that it s not human and it s one of the moderators of his contents,
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You know, there s another question you could ask, which is like, it kind of knows that humans are mortal.
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Am I mortal?
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And I think one really important thing that s possible when you re conscious is to fear the extinguishing of that consciousness, the fear of mortality.
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Do you think that s useful for intelligence?
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Thinking like, I might die and I really don t want to die.
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I don t think so.
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I think it may help some humans to be better people, it may help some humans to be more creative, and so on.
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I don t think it s necessary for AIs to believe that they have limited lifespans and therefore they should make the most of their behavior.
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Maybe eventually the answer to that and my answer to that will change, but as of now I would say that that s almost like a
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a frill or a side effect that is not... In fact, if you look at most humans, most humans ignore the fact that they re going to die most of the time.
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So... Well, but that s like, this goes to the white space between the words.
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So what Ernest Becker argues is that that ignoring is we re living in an illusion that we constructed on the foundation of this terror.
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So we escape life as we know it.
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Pursuing things, creating things, love, everything we can think of that s beautiful about humanity is just trying to escape this realization that we re going to die one day.
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That s his idea and I think, I don t know if I 100%
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believe in this, but it certainly rhymes.
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It seems to me like it rhymes with the truth.
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Yeah, I think that for some people, that s gonna be a more powerful factor than others.
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Clearly, Doug is talking about Russians.
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And I think that... So I m Russian, so clearly it infiltrates all of Russian literature.
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And AI doesn t have to have
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fear of death as a motivating force in that we can build in motivation.
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So we can build in the motivation of
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obeying users and making users happy and making others happy and so on.
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And that can substitute for this sort of personal fear of death that sometimes leads to bursts of creativity in humans.
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SPEAKER_01
Yeah, I don t know.
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SPEAKER_01
I think AI really needs to understand death deeply in order to be able to drive a car, for example.
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SPEAKER_01
I think there s just some, like,
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No, I really disagree.
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I think it needs to understand the value of human life, especially the value of human life to other humans, and understand that certain things are more important than other things, so it has to have a lot of knowledge about ethics and morality and so on.
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SPEAKER_01
But some of it is so messy that it s impossible to encode.
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SPEAKER_01
I disagree.
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SPEAKER_01
So if there s a person dying right in front of us, most human beings would help that person, but they would not apply that same ethics to everybody else in the world.
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This is the tragedy of how difficult it is to be a doctor, because they know when they help a dying child,
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They know that the money they re spending on this child cannot possibly be spent on every other child that s dying.
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And that s a very difficult to encode decision.
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SPEAKER_01
Perhaps it could be formalized.
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SPEAKER_00
Oh, but I mean, you re talking about
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autonomous vehicles, right?
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SPEAKER_00
So autonomous vehicles are going to have to make those decisions all the time of what is the chance of this bad event happening?
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How bad is that compared to this chance of that bad event happening?
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And so on.
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And when a potential accident is about to happen, is it worth taking this risk?
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SPEAKER_00
If I have to make a choice, which of these two cars am I going to hit and why?
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SPEAKER_01
See, I was thinking about a very different choice when I m talking about fear of mortality, which is just observing Manhattan-style driving.
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SPEAKER_01
I think that humans, as an effective driver, needs to threaten pedestrians  lives a lot.
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SPEAKER_01
There s a dance, I ve watched pedestrians a lot, I ve worked on this problem, and it seems like, if I could summarize the problem of a pedestrian crossing, is the car with this movement is saying, I m going to kill you, and the pedestrian is saying, maybe, and then they decide and they say, no, I don t think you have the guts to kill me, and they walk in front and they look away.
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SPEAKER_01
And there s that dance, the pedestrian, as this is a social contract, that the pedestrian trusts that once they re in front of the car and the car is sufficiently, from a physics perspective, able to stop, they re going to stop.
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SPEAKER_01
But the car also has to threaten that pedestrian.
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SPEAKER_01
It s like, I m late for work.
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SPEAKER_01
So you re being kind of an asshole by crossing in front of me, but life and death is part of the calculation here, and that equation is being solved millions of times a day.
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Yes.
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Very effectively.
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That game theory, whatever that formulation is.
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I just, I don t know if it s as simple as some formalizable game theory problem.
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SPEAKER_01
It could very well be in the case of driving and in the case of most of human society.
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SPEAKER_01
I don t know.
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SPEAKER_01
You might be right that the fear of death is just one of the quirks of the way our brains have evolved, but it s not a necessary feature of intelligence.
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SPEAKER_00
Drivers certainly are always doing this kind of estimate, even if it s unconscious, subconscious, of what are the chances of various bad outcomes happening?
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Like, for instance, if I don t wait for this pedestrian or something like that.
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And what is the downside to me going to be in terms of time wasted talking to the police or getting sent to jail or things like that?
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SPEAKER_01
And there s also emotion, like people in their cars tend to get irrationally angry.
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SPEAKER_00
That s dangerous, but think about it.
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This is all part of why I think that autonomous vehicles, truly autonomous vehicles, are farther out.
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SPEAKER_00
than most people do, because there is this enormous level of complexity which goes beyond mechanically controlling the car.
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And I can see the autonomous vehicles as a kind of metaphorical and literal accident waiting to happen.
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And not just because of their overall
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incurring versus preventing accidents and so on, but just because of the almost voracious appetite people have for
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bad stories about powerful companies and powerful entities.
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When I was at a, coincidentally, Japanese fifth-generation computing system conference in 1987, while I happened to be there,
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There was a worker at an auto plant who was despondent and committed suicide by climbing under the safety chains and so on, getting stamped to death by a machine.
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And instead of being a small story that said, despondent worker commits suicide, it was front page news that effectively said, robot kills worker.
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SPEAKER_00
because the public is just waiting for stories about like AI kills phonogenic family of five type stories.
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And even if you could show that nationwide this system saved more lives than it cost and saved more injuries, prevented more injuries than it caused and so on, the media, the public, the government is just
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coiled and ready to pounce on stories where, in fact, it failed, even if there are relatively few.
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SPEAKER_01
Yeah, it s so fascinating to watch us humans resisting the cutting edge of science and technology and almost like hoping for it to fail.
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SPEAKER_01
This just happens over and over and over throughout history.
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SPEAKER_00
Or even if we re not hoping for it to fail, we re fascinated by it.
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And in terms of what we find interesting, the one in a thousand failures, much more interesting than the 999 boring successes.
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SPEAKER_01
So once we build an AGI system, say psych is some part of it, and say it s very possible that you would be one of the first people that can sit down in the room, let s say with her, and have a conversation, what would you ask her?
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SPEAKER_01
What would you talk about?
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SPEAKER_00
Looking at all of the content
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out there on the web and so on.
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SPEAKER_00
What are some possible solutions to big problems that the world has that people haven t really thought of before that are not being properly or at least adequately pursued?
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SPEAKER_00
What are some novel solutions that you can think of that we haven t?
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SPEAKER_00
that might work and that might be worth considering.
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SPEAKER_01
That is a damn good question.
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SPEAKER_00
Given that the AGI is going to be somewhat different from human intelligence, it s still going to make some mistakes that we wouldn t make, but it s also possibly going to notice.
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SPEAKER_00
some blind spots we have, and I would love, as a test of is it really on a par with our intelligences, can it help spot some of the blind spots that we have?
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SPEAKER_01
So the two-part question of can you help identify what are the big problems in the world, and two, what are some novel solutions to those problems?
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SPEAKER_00
That are not being talked about by anyone.
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SPEAKER_00
And some of those may become infeasible or reprehensible or something, but some of them might be actually great things to look at.
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SPEAKER_00
If you go back and look at some of the most powerful discoveries that have been made, like relativity and superconductivity and so on, a lot of them were cases where
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SPEAKER_00
someone took seriously the idea that there might actually be a non-obvious answer to a question.
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SPEAKER_00
So in Einstein s case, it was, yeah, the Lorentz transformation is known.
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SPEAKER_00
Nobody believes that it s actually the way reality works.
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SPEAKER_00
What if it were the way that reality actually worked?
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So a lot of people don t realize he didn t actually work out that equation, he just sort of took it seriously.
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Or in the case of superconductivity, you have this V equals IR equation where R is resistance and so on.
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And it was being mapped at lower and lower temperatures, but everyone thought that was just bump on a log research to show that V equals IR
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are always held.
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And then when some graduate student got to a slightly lower temperature and showed that resistance suddenly dropped off, everyone just assumed that they did it wrong.
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And it was only a little while later that they realized it was actually a new phenomenon.
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Or in the case of the H. pylori bacteria causing stomach ulcers, where everyone thought that stress and stomach acid caused ulcers.
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And when a doctor in Australia claimed it was actually a bacterial infection, he couldn t get anyone seriously to listen to him.
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And he had to ultimately inject himself with the bacteria to show that he suddenly developed a life-threatening ulcer in order to get other doctors to seriously consider that.
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So there are all sorts of things where
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humans are locked into paradigms, what Thomas Kuhn called paradigms, and we can t get out of them very easily.
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A lot of AI is locked into the deep learning, machine learning paradigm right now.
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Almost all of us and almost all sciences are locked into current paradigms.
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Kuhn s point was pretty much you have to wait for people to die
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in order for the new generation to escape those paradigms.
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And I think that one of the things that would change that sad reality is if we had trusted AGIs that could help take a step back and question some of the paradigms that we re currently locked into.
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Yeah, it would accelerate the paradigm shifts in human science and progress.
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You ve lived a very interesting life where you thought about big ideas and you stuck with them.
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Can you give advice to young people today, somebody in high school, somebody undergrad, about career, about life?
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I d say you can make a difference.
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in order to make a difference, you re going to have to have the courage to follow through with ideas which other people might not immediately understand or support.
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You have to realize that if you make some
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some plan that s going to take an extended period of time to carry out, don t be afraid of that.
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That s true of physical training of your body.
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That s true of learning some profession.
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That s also true of innovation, that some innovations are not great ideas you can write down on a napkin and become an instant success if you turn out to be right.
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paths you have to follow, but remember that you re mortal, remember that you have a limited number of
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decade-sized bets to make with your life, and you should make each one of them count.
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And that s true in personal relationships, that s true in career choice, that s true in making discoveries, and so on.
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And if you follow the path of least resistance, you ll find that you re optimizing for short periods of time
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and before you know it, you turn around and long periods of time have gone by without you ever really making a difference in the world.
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You know, there s, when you look, I mean, the field that I really love is artificial intelligence, and there s not many projects, there s not many little flames of hope that have been carried out for many years, for decades, and Psyche represents one of them.
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And I mean, that in itself is just a really inspiring thing.
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So I m deeply grateful that you would be carrying that flame for so many years, and I think that s an inspiration to young people.
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That said, you said life is finite, and we talked about mortality as a feature of AGI.
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Do you think about your own mortality?
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Are you afraid of death?
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Sure, I d be crazy if I weren t.
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And as I get older, I m now over 70, so as I get older, it s more on my mind, especially as acquaintances and friends and especially mentors, one by one are dying, so I can t avoid
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thinking about mortality, and I think that the good news from the point of view in the rest of the world is that that adds impetus to my need to succeed in a small number of years in the future.
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Because I m not gonna- You have a deadline.
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Exactly, I m not gonna have another 37 years to continue working on this, so we really do want Psych to make an impact in the world, commercially, physically, metaphysically,
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in the next small number of years, two, three, five years, not two, three, five decades anymore.
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And so this is really driving me toward this sort of commercialization and increasingly widespread application of psych.
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Whereas before, I felt that I could just sort of sit back, roll my eyes, wait till the world caught up.
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And now I don t feel that way anymore.
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I feel like I need to put in some effort to make the world aware of what we have and what it can do.
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And the good news from your point of view is that that s why I m sitting here.
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You re going to be more productive.
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I love it.
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And if I can help in any way, I would love to, from a programmer perspective, I love, especially these days, just contributing in small and big ways.
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So if there s any open sourcing from an MIT side and the research, I would love to help.
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But bigger than psych, like I said, it s that little flame that you re carrying of artificial intelligence, the big dream, is there,
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What do you hope your legacy is?
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That s a good question.
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That people think of me as one of the pioneers or inventors of
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the AI that is ubiquitous and that they take for granted, and so on.
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Much the way that today we look back on the pioneers of electricity or the pioneers of similar types of technologies and so on as, you know, it s hard to imagine what life would be like if these people hadn t done what they did.
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So that s one thing.
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that I d like to be remembered as.
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Another is that... So the creator, one of the originators of this gigantic knowledge store and acquisition system that is likely to be at the center of whatever this future AI thing will look like.
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Yes, exactly.
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And I d also like to be remembered as someone who
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wasn t afraid to spend several decades on a project in a time when almost all of the other forces, institutional forces and commercial forces, are incenting people to go for short-term rewards.
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And a lot of people gave up.
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A lot of people that dreamt the same dream as you gave up.
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Yes.
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And you didn t.
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Yes.
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I mean, Doug, it s truly an honor.
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This was a long time coming.
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A lot of people bring up your work specifically and more broadly philosophically of this is the dream of artificial intelligence.
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This is likely a part of the future.
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We re so sort of focused on machine learning applications, all that kind of stuff today, but it seems like the ideas that s like carries forward.
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is something that will be at the center of this problem they re all trying to solve, which is the problem of intelligence, emotional and otherwise.
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So thank you so much.
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It s such a huge honor that you would talk to me and spend your valuable time with me today.
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Thanks for talking.
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Thanks, Lex.
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It s been great.
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Thanks for listening to this conversation with Doug Lenat.
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To support this podcast, please check out our sponsors in the description.
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And now let me leave you some words from Mark Twain about the nature of truth.
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If you tell the truth, you don t have to remember anything.
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Thank you for listening.
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I hope to see you next time.