0
SPEAKER_01
Hi everyone and welcome back to the Towards Data Science podcast.
0
SPEAKER_01
And before we dive into things, a quick reminder that Towards Data Science is always looking for great submissions about machine learning, data science, and analytics.
0
SPEAKER_01
So if you ve been wondering about whether you should put together that blog post on your research project or your cool data visualization work, then please, please do.
0
SPEAKER_01
We d love to see it.
0
SPEAKER_01
Okay, on to the show here, and this is going to be a really fun one.
0
SPEAKER_01
Today, I got to speak to Ala Shabana and Jacob Steeves, two machine learning researchers with world-class pedigrees who decided to build a company that puts AI on the blockchain.
0
SPEAKER_01
Now, to most people, myself included, AI on the blockchain sounds like a winning entry in startup buzzword bingo.
0
SPEAKER_01
But what I discovered talking to Jacob and Ala was that they actually have good reason to combine those two ingredients together.
0
SPEAKER_01
At a high level, doing AI on a blockchain allows you to decentralize AI research and reward labs for building better models, not for publishing papers in flashy journals with often biased reviewers.
0
SPEAKER_01
And that s not all.
0
SPEAKER_01
As you ll see, Alaa and Jacob are taking on some of the thorniest current problems in AI with their decentralized approach to machine learning.
0
SPEAKER_01
Everything from the problem of designing robust benchmarks to rewarding good AI research, and even the centralization of power in the hands of a few large companies building powerful AI systems.
0
SPEAKER_01
These problems are all in their sights as they look to build out BitTensor, their AI on the blockchain startup.
0
SPEAKER_01
Alaa and Jacob joined me to talk about all these things and more on this episode of the Towards Data Science podcast.
0
SPEAKER_01
I m really excited for this chat.
0
SPEAKER_01
Um, and just for a little bit of background for our listeners here, um, cause there s a little bit more sort of backstory to this than the average podcast in a way.
0
SPEAKER_01
So Allah, like you were a researcher at four AI, which is a company that for listeners, it was co-founded by the co-inventor of the transformer architecture, which was Aiden Gomez.
0
SPEAKER_01
The author of that attention is all you need paper and friend of the show.
0
SPEAKER_01
I m pleased to say, and Jacob, you were at Google before this.
0
SPEAKER_01
which is a company co-founded by Eric Schmidt, also friend of the show, I m sure.
0
SPEAKER_01
Hi, Eric.
0
SPEAKER_01
Okay.
0
SPEAKER_01
So you guys came together somehow with those two different backgrounds and you co-founded this company, BitTensor, and we ll get to what BitTensor is and what it s doing.
0
SPEAKER_01
I think it s really cool.
0
SPEAKER_01
And there s a lot of stuff for everybody to learn on this, but can you just start us off with like the story of how you met and how two software engineer, ML engineer people started to work on something at the intersection of like machine learning and crypto.
0
SPEAKER_00
I can jump on this one, Jake, unless you want to take it.
0
SPEAKER_00
So actually, Jacob was also a researcher at 4AI.
0
SPEAKER_00
And we kind of met through Aiden Gomez on 4AI as well.
0
SPEAKER_00
So that was kind of the interesting part of it.
0
SPEAKER_00
I had joined 4AI right after finishing my tenure at VMware.
0
SPEAKER_00
I had been working on distributed computer problems and effectively, you know, something in the shadow of what BitTensor is.
0
SPEAKER_00
and Aiden thought it was a good idea for me to kind of just give a pitch of what I ve been working on and what I would love to kind of just jump onto and stuff like that.
0
SPEAKER_00
And I just joined and I decided to kind of pitch what I was doing and then Jake messages me and he s like, hey, I ve been working on this problem for a lot longer than you have.
0
SPEAKER_00
Come join me because I ve kind of got like a nice prototype running and let s see how this goes.
0
SPEAKER_00
And we kind of just hit it off from there and things kind of just ran quite well since then.
0
SPEAKER_00
And effectively just ended up leaving my full-time job to pursue this full-time.
0
SPEAKER_00
And yeah, things have been cracking since.
0
SPEAKER_00
Aidan actually introduced us, which is interesting.
0
SPEAKER_01
Oh, very cool.
0
SPEAKER_01
So the union was blessed by, by Aiden, by the godfather of Aiden, exactly.
0
SPEAKER_01
Excellent.
0
SPEAKER_01
All right.
0
SPEAKER_01
Glad to, glad you got that endorsement too.
0
SPEAKER_01
That s a very powerful thing.
0
SPEAKER_01
So I think one of the, the approach I was thinking about this podcast ahead of time, because like full disclosure, as I mentioned before we started recording, like I know very, very little, dangerously little,
0
SPEAKER_01
about the blockchain, about cryptocurrencies, blah, blah, blah.
0
SPEAKER_01
And so what I d love to do is just start with what you think are the basic ingredients that listeners should be aware of that we re going to build on as we start to explain the problem you re tackling and how you re tackling it.
0
SPEAKER_01
So maybe, Jacob, I ll turn to you.
0
SPEAKER_01
Can you introduce those ingredients?
0
SPEAKER_02
I mean, so if you look at if you look at blockchain, you can see a lot of different components there like there s decentralization and censorship resistance and that has a lot of like a libertarian aspect but but what was also invented by by Bitcoin was this concept of digital trust.
0
SPEAKER_02
and the ability for computers to reach consensus about this thing, about money, right?
0
SPEAKER_02
And that allows us to build incentive mechanisms or markets in a digital space that never could exist before.
0
SPEAKER_02
So blockchains are basically tech tools for having computers reach consensus about something.
0
SPEAKER_02
And in Bitcoin, that s the state of the blockchain.
0
SPEAKER_02
And it s one way to think about it, but this is kind of like a market for hashing power.
0
SPEAKER_02
So that s kind of the bridge between blockchains and what we do, is we go, hey, look, Bitcoin s this market for hashing power.
0
SPEAKER_02
It s the largest in the world.
0
SPEAKER_02
And we re going to take that same technique of building these global markets, which grew Bitcoin to this global size, and apply it to the other computational problem, which also has its own digital commodity, digital intelligence, and basically use markets to create it.
0
SPEAKER_02
Okay, so that s where blockchains come in to BitTensor.
0
SPEAKER_01
Sorry, do you mind if I just ask, explain it like I m 5 version of a couple of those concepts?
0
SPEAKER_01
Because I love that high-level thing and I think it s a great motivator, so we have the sense in which
0
SPEAKER_01
some measure of trust and reliability, the automation of trust, and so on, is going to be important to the story.
0
SPEAKER_01
But I d like to take a step back just for people who hear blockchain, hear decentralization, hear all these terms, and they re trying to connect it to what is the thing that s sitting on the database somewhere, or how does a transaction work, that kind of basic level, if possible.
0
SPEAKER_02
Right.
0
SPEAKER_02
Sure, that s good.
0
SPEAKER_02
I can explain it like that.
0
SPEAKER_02
So essentially, a blockchain is just an append only database, where it s distributed across many computers.
0
SPEAKER_02
And they effectively vote on what the next thing in the database is going to be.
0
SPEAKER_02
And in Bitcoin, they vote with computational power.
0
SPEAKER_02
And in a proof of stake system, they vote with monetary wealth.
0
SPEAKER_02
And that s essentially the core of the system.
0
SPEAKER_02
And now the reason why there needs to be this kind of voting is that it s hard to determine who is legitimate with inside of the internet.
0
SPEAKER_02
Like IPs are basically infinite, they can be spoofed.
0
SPEAKER_02
So what is the thing against you could vote in this decentralized way to determine the state of the system?
0
SPEAKER_02
And that s really important if you wanna build trust about who s good and who s bad, for instance,
0
SPEAKER_02
or which transactions came before which transactions in the case of Bitcoin.
0
SPEAKER_02
So that s sort of the concept of a blockchain.
0
SPEAKER_02
It s a block.
0
SPEAKER_02
Usually the way in which data is appended is in terms of blocks of data, not a single edit.
0
SPEAKER_02
So that s where the block comes from.
0
SPEAKER_02
And the fact that it s append only things get added one of the time in a forward manner makes it a chain where every piece of data locks itself to the previous.
0
SPEAKER_02
So you can t edit the history of the network.
0
SPEAKER_02
This allows us to create state which everybody can agree on in a decentralized way on the internet, which is the foundation for smart contracts and also what we do at the tensor.
0
SPEAKER_01
OK, perfect.
0
SPEAKER_01
So thank you, by the way, for that 101.
0
SPEAKER_01
I think it s just really helpful to have these ideas in mind.
0
SPEAKER_01
So OK, this database, there are copies of it, of course, stored on a whole bunch of different computers.
0
SPEAKER_01
In the case of some blockchains, parts of copies.
0
SPEAKER_01
In the case of others, I guess full copies, depending on, or that would be an option, I suppose.
0
SPEAKER_01
OK, so this is the starting point.
0
SPEAKER_01
How do we get from this to what you re doing at BitTensor?
0
SPEAKER_01
How does this connect to machine learning?
0
SPEAKER_02
Right, exactly.
0
SPEAKER_02
It s a really good question.
0
SPEAKER_02
So, I mean, I kind of touched on in the beginning, you know, as many of the viewers may know, the largest supercomputer in the world is Bitcoin.
0
SPEAKER_02
It s like 500 times bigger than all of Google s data centers.
0
SPEAKER_02
magnitudes larger.
0
SPEAKER_02
And that s astonishing.
0
SPEAKER_02
I worked at Google, and I remember when they finished building a data center, they would just start building another data center right beside it.
0
SPEAKER_02
That s the amount of power that this decentralized system has been able to extract.
0
SPEAKER_02
It s actually extracted this computational power from the world.
1
SPEAKER_01
Is this just from laptops like my laptop and so on like that, or GPUs who are mining?
1
SPEAKER_00
It started that way, but because of the fact that Bitcoin created a market
1
SPEAKER_00
which was very precise in what it needed.
1
SPEAKER_00
It created a very efficient market where anybody in the world could work on the arbitrage opportunities.
1
SPEAKER_00
They could find some energy that s not being used, or they could discover a different way of solving this hashing problem using a specific type of chip.
1
SPEAKER_00
And basically, you unlock the power of the hive mind to find these arbitrage opportunities across the globe, and therefore creates
1
SPEAKER_00
and creates this massive network.
1
SPEAKER_00
Now, today, nobody uses laptops because it s inefficient, where Bitcoin is really mined next to a freezing river in Finland or using geothermal energy in Iceland.
1
SPEAKER_00
And the reason is because that s just the most efficient place to put these computational tools.
1
SPEAKER_00
That s really what we re tapping into with BitTensor.
1
SPEAKER_00
We re going, hey, look,
1
SPEAKER_00
the power of markets, the power of arbitrage, the power of the hive mind, the global hive mind in a borderless decentralized system is incredibly powerful.
1
SPEAKER_00
We can get to Google and open AI size in terms of neural networks and in the production of machine intelligence if we can use the same tooling, the creation of markets and the ability for anybody, any engineer in the world to go, hey, look, I got a computer, I m going to run it
1
SPEAKER_00
you know, next to a freezing river in Finland or in a volcano in El Salvador, and it s just going to churn away solving a specific or a certain aspect of this larger problem, which is the understanding of the creation of intelligence.
1
SPEAKER_01
Okay, great.
1
SPEAKER_01
Here s my understanding so far and where I suspect you re going with this, and I m curious if I m completely wrong.
1
SPEAKER_01
Right now, the way I picture it, there are a bunch of GPUs in these giant clusters, like you say, next to geothermal vents or whatever in Iceland.
1
SPEAKER_01
Not yet on BitTensor, but potentially.
1
SPEAKER_01
Potentially.
1
SPEAKER_01
And currently, systems like that are mining Bitcoin.
1
SPEAKER_01
And the way they re doing that is by just guessing random numbers to see if they end up guessing the right random number to, for various convoluted reasons, lock in the next block in the blockchain.
1
SPEAKER_01
And that is part of the whole trust thing.
1
SPEAKER_01
That s great.
1
SPEAKER_01
But basically, these things are just guessing random numbers.
1
SPEAKER_01
And so my gut says these two guys are about to propose a way that maybe we can not just get these things to guess random numbers, but to spit out computations that are like more useful than that.
1
SPEAKER_01
So they re not just burning up our, our atmosphere with CO2 emissions by doing like random crap, but specifically human value generating crap.
1
SPEAKER_01
Is that fair?
1
SPEAKER_00
Right.
1
SPEAKER_00
Yeah, exactly.
1
SPEAKER_00
I mean, and so like, um,
1
SPEAKER_00
If you look at if you look at machine intelligence, you look at like a neural network right, you can break it apart into different components, and those components are adding something to the system right like, for instance, some of your viewers probably know what a mixtures of experts model or an ensemble model is.
1
SPEAKER_00
right?
1
SPEAKER_00
In an ensemble model, you have a specific part of the machine learning model that is producing a representation of the input.
1
SPEAKER_00
It s like, here s the features of the image, or this is the semantics of some text.
1
SPEAKER_00
And that representation is valuable.
1
SPEAKER_00
And so that s the digital commodity that we re working on.
1
SPEAKER_00
It s literally the representation of inputs.
1
SPEAKER_00
And so in Bitcoin, Bitcoin is designed in such a way that this digital commodity is very easy to validate, right?
1
SPEAKER_00
Hashes are immediate to validate and everybody can agree, whereas the value of a representation of input is not so obvious, right?
1
SPEAKER_00
So our consensus mechanism is designed to work with that sort of fuzzy data inside a machine learning system.
1
SPEAKER_00
But yeah, essentially, these GPUs are learning from, for instance, as an example, text to produce something like the representations created by GPT.
1
SPEAKER_00
When you query GPT, you get representational knowledge.
1
SPEAKER_00
So hey, what do you think of this text?
1
SPEAKER_00
And it gives you this represented in a tensor.
1
SPEAKER_00
And that s very useful for solving machine learning problems.
1
SPEAKER_00
And so that s the digital commodity that we focused on, because we think that that representational knowledge is the most foundational thing in AI.
1
SPEAKER_00
All of the other problems, specific supervised problems, derive themselves from, first, what is the meaning of this?
1
SPEAKER_00
Without an understanding of the task.
1
SPEAKER_00
So specifically to your viewers, you ll know what unsupervised learning is, and that s what we re working on.
1
SPEAKER_01
OK, great.
1
SPEAKER_01
And so just to double check, triple confirm here, just for listeners who maybe focus on the more classical side of data science and do less neural network stuff, in a neural network, as stuff propagates down the neural network, at a certain layer, fairly deep, you end up with a good representation of the thing that, like a list of numbers, that represents, in some sense, the meaning of the thing you ve sent in.
1
SPEAKER_01
And then that kind of gets
1
SPEAKER_01
Let s say the puck gets put in the net by a couple of layers sometimes, or maybe one layer just above, that kind of uses that representation to draw a conclusion.
1
SPEAKER_01
Like, I think this is an image of a dog, or a cat, or whatever.
1
SPEAKER_01
And now you re looking at that representation and saying, OK, all the values there, because we really just kind of do a little tweaking on top of it to get the thing we ultimately want.
1
SPEAKER_01
So maybe the main thing of interest is that.
1
SPEAKER_01
OK, so how do you convince people
1
SPEAKER_01
I m not even sure what the right question is.
1
SPEAKER_01
How does this work?
1
SPEAKER_01
Are you trying to get a bunch of people to agree on a particular representation to validate it?
1
SPEAKER_01
Or what s the value generation part of this?
1
SPEAKER_00
Yeah, really good question.
1
SPEAKER_00
So like, of course, in this decentralized system, we need to reach agreement, because functionally, we need to determine what is an incentive, who s going to get money, right, who s not.
1
SPEAKER_00
So we need to determine that.
1
SPEAKER_00
And that comes down to this problem of consensus, which is much more on the blockchain side.
1
SPEAKER_00
Like, how do we determine who s got useful information?
1
SPEAKER_00
And so the way that we do that is that we have essentially is, you could say we have two types of miners, those that are producing the knowledge producing those representations which are kind of like maps, you know, of the territory.
1
SPEAKER_00
And then you have computers that are validating that knowledge so they re going hey like well, was it useful.
1
SPEAKER_00
And concretely, we work on next-token prediction problems with basically a large unlabeled text corpora.
1
SPEAKER_00
And the validators are basically attempting to use that knowledge to solve the specific NT problems.
1
SPEAKER_00
That s the same problem that GPT is trained on, right?
1
SPEAKER_00
So they try to use representations to understand text.
1
SPEAKER_00
they take that in and use it and understand text.
1
SPEAKER_00
And while they re doing that, they can actually learn who s valuable.
1
SPEAKER_00
Because if you re not producing a representation of the text, which is useful for doing NTP, machine learning by its very nature sifts out signal from noise.
1
SPEAKER_00
So are you producing signal?
1
SPEAKER_00
I mean, and just to diverge a little bit, it s very interesting, because if you can think about it, the tensor is essentially a new type of benchmark, right?
1
SPEAKER_00
It s an incentivized benchmark for these types of representational models.
1
SPEAKER_00
But it s a very high resolution benchmark, where actually you re measuring the performance of the machine learning models in representational knowledge, where they can produce just a little niche
1
SPEAKER_00
inside of this large experts, it s essentially a mixtures of extras model.
1
SPEAKER_01
Sorry, can I test my understanding then of this so far?
1
SPEAKER_01
So like there s some person in Iceland who is going to go, hey, I want to be the one who gets to add the next block in the blockchain.
1
SPEAKER_01
And I get a reward for doing that because that s how blockchains work.
1
SPEAKER_01
That s the incentive that causes me to want to do this.
1
SPEAKER_01
And in order to do that, rather than guessing a random hash, I have to generate like some
1
SPEAKER_01
latent representation of a sentence or a token that I want to add.
1
SPEAKER_01
So this would be at the generation end.
1
SPEAKER_01
This is different from the validation end.
1
SPEAKER_01
I guess I m confused about who gets the money for what in that context.
1
SPEAKER_00
It s a really good question.
1
SPEAKER_00
Perhaps this is something that we should have described before.
1
SPEAKER_00
The mechanism for reaching consensus inside of BitTensor is done on top of a blockchain.
1
SPEAKER_00
So the way in which blocks are generated is using a pretty traditional mechanism from blockchain.
2
SPEAKER_02
system in order to determine the ordering of blocks and what information goes on those blocks and how the state is updated.
2
SPEAKER_02
Aside from that, we run basically a smart contractor on the blockchain.
2
SPEAKER_02
where the validators are syncing information.
2
SPEAKER_02
And that consensus in the system is actually run on that.
2
SPEAKER_02
So the concept of blocks is you could say below us, we re like layer two, right?
2
SPEAKER_02
But to answer your question more specifically, the way in which an incentive, once these validators are determining this informational significance,
2
SPEAKER_02
Obviously, there s a little bit of fuzziness there, right?
2
SPEAKER_02
Okay, well, he s valuable sometimes to me, but way more to you.
2
SPEAKER_02
So we need to reach a sort of agreement.
2
SPEAKER_02
Who s at the intersection of everybody?
2
SPEAKER_02
And once we know who s at the intersection, we can fairly trust that they are producing something of value.
2
SPEAKER_02
And that intersection is computed on the blockchain.
2
SPEAKER_02
And that s how we determine who gets inflation.
2
SPEAKER_02
And we just mint new tokens to them.
2
SPEAKER_01
Okay.
2
SPEAKER_01
And Ala, how do you make money from this?
2
SPEAKER_01
Like, is it, do you print a bunch of tokens and they go to BitTensor and then like they appreciate in value or is there some other strategy?
2
SPEAKER_00
So it s a little bit different.
2
SPEAKER_00
We decided to go with a, what s called a fair launch.
2
SPEAKER_00
So that means that any, our cryptocurrency is called Tau.
2
SPEAKER_00
Any Tau on the system has actually been mined fairly.
2
SPEAKER_00
So it s actually contributed to the general knowledge of the whole system.
2
SPEAKER_00
We didn t kind of do any kind of initial coin offering.
2
SPEAKER_00
We didn t really kind of offer anything.
2
SPEAKER_00
The idea behind it is, as Jake was mentioning, there are contributors and there are consumers, right?
2
SPEAKER_00
And then what happens is the contributors are effectively rewarded for their contributions to the system if they re contributing valid representations, representations that are generally considered useful by the validators.
2
SPEAKER_00
And what happens is they ll get a fraction of the tau that is being generated at each block at the first layer that Jake was describing.
2
SPEAKER_00
So what happens is every 12 seconds or so, there is a new block generated.
2
SPEAKER_00
And with that new block, there s one tau that is awarded to the highest performers, basically the ones who do the best in relation to their neighbors.
2
SPEAKER_00
And they ll effectively just get a portion of that tau as a result.
2
SPEAKER_00
So for example, one of the things that the blocks actually contain, information-wise, is a ranking.
2
SPEAKER_00
So it s basically just a matrix that contains the ranking of all of the contributors to the system and all the validators as well, and basically just to show who is the most useful, basically.
2
SPEAKER_00
It s an informational significance measurement.
2
SPEAKER_00
And effectively, the ones that rank the highest are the ones that get the largest portions, and the ones that rank lowest, they get the lowest portions as a result.
2
SPEAKER_01
OK, and how do you actually measure?
2
SPEAKER_01
This goes to Jacob s point of how things get verified and validated.
2
SPEAKER_01
How do you actually assess that, OK, this person has the more valuable system?
2
SPEAKER_01
Do you have a bunch of people running GPT-2 or whatever on their locals, and then they just kind of see how similar a representation is to theirs?
2
SPEAKER_01
What s the similarity metric?
2
SPEAKER_02
We use basically a technique called Fisher s information, which is just basically it s an approximation of how different or worse off the validator would be if a particular node was removed.
2
SPEAKER_02
So the peers are actually evaluated in a collective.
2
SPEAKER_02
You take all of the embeddings.
2
SPEAKER_02
You combine them at the validator, and you learn which ones are valuable.
2
SPEAKER_02
And as you re learning, you can actually determine these weights.
2
SPEAKER_02
So the specific techniques are like the categories called a salience technique.
2
SPEAKER_02
Imagine you had a neural network, and you had a specific neuron in the neural network.
2
SPEAKER_02
You can think of an individual neuron as producing value as one of our miners.
2
SPEAKER_02
And then you can go, hey, what happens if I remove this one?
2
SPEAKER_02
Is that good or bad, right?
2
SPEAKER_02
And this work has been going on in this problem for a very long time.
2
SPEAKER_02
Yann LeCun wrote a paper about this a long time ago where he used Fisher s information.
2
SPEAKER_02
And so we use that same technique to go and calculate the exact value of, okay, well here, if we remove that peer, what happens?
2
SPEAKER_02
How much worse are we off in the system?
2
SPEAKER_02
And that actual metric is essentially entropy if we re using a KL divergence.
2
SPEAKER_01
OK, so you re basically doing a kind of ablation study on the different peers, the different latent representations.
2
SPEAKER_01
OK, that makes sense.
2
SPEAKER_01
It s almost like a permutation feature importance measure.
2
SPEAKER_01
Anyway, it s got a lot of analogs.
2
SPEAKER_01
OK.
2
SPEAKER_01
Yeah, analogs.
2
SPEAKER_02
And the other one s like shapely values, similar, right?
2
SPEAKER_02
OK.
2
SPEAKER_01
Oh, no, very cool.
2
SPEAKER_01
OK, so practical question.
2
SPEAKER_01
I m about to, I don t know, I m about to launch a text autocomplete app or something.
2
SPEAKER_01
And I m wondering, should I fire up my own personal GPUs?
2
SPEAKER_01
Or should I go to Amazon Web Services?
2
SPEAKER_01
Or should I go to BitTensor and try to use their decentralized infrastructure?
2
SPEAKER_01
Why would I choose one over the other, let s say?
2
SPEAKER_01
What scenario would BitTensor really be most valuable in?
2
SPEAKER_00
One of the things that BitTensor actually provides, part of what we re building, is what is effectively a large decentralized dataset.
2
SPEAKER_00
There is an inherent dataset that BitTensor is sitting on, and it s just a giant text corpus that contains everything from books to emails to articles, abstracts, papers, and everything.
2
SPEAKER_00
And the idea is that we kind of place this into what is effectively a large decentralized system, whereas it s over something called the IPFS network, the Interplanetary File System.
2
SPEAKER_00
And it s like they just shard it all over the place.
2
SPEAKER_00
And what happens is every BitTensor node will download a SIP of that data set and start using it directly, right?
2
SPEAKER_00
So in addition to, really, if you re training a language model, you don t really need to bring your own data set.
2
SPEAKER_00
You can have the security data set they re already working on, and kind of just download that sort of BitTensor.
2
SPEAKER_00
You re also working with, you re effectively, in a lot of ways, learning from other systems, from other nodes in the system, right?
2
SPEAKER_00
You re not just learning on your own data set.
2
SPEAKER_00
You re also speaking to other nodes, and they re also exchanging information with you, and you re learning from them.
2
SPEAKER_00
So one of the biggest problems in AI that Jake and I both noticed independently during our careers is that AI itself, the research compounds every year, right?
2
SPEAKER_00
So we re always building on top of what we ve learned.
2
SPEAKER_00
But to reach the yard, we re always retraining models, right?
2
SPEAKER_00
So GPT-2 was trained over all the same data that GPT-1 had learned when really GPT-1 already knew all this, right?
2
SPEAKER_00
But it had to kind of surpass, basically learn the same thing to surpass it eventually.
2
SPEAKER_00
And then eventually GPT-1 was discarded, and now it s not really kind of obsolete in a way, even though it could provide some very interesting insight regardless of how well it s performing.
2
SPEAKER_00
So one of the things at BitTensor that we are actually actively trying to verify and actively working on, one of the biggest points, is that information itself on the network also compounds.
2
SPEAKER_00
You don t need to relearn things anymore because you re speaking to other models that might have already learned something that your model doesn t know yet.
2
SPEAKER_00
So not only are you dealing with models that know things that you don t, you re also kind of dealing with adversarial data sets.
2
SPEAKER_00
You re kind of models sending you stuff that your model might not have seen before.
2
SPEAKER_00
And you re kind of adapting and learning.
2
SPEAKER_00
So what you re doing is you re actually more creating a resilient generalist model as opposed to a very narrow specialist.
2
SPEAKER_00
So what s happening is your model becomes more
2
SPEAKER_00
I guess I want to say resilient again, just because it s actually dealing with stuff that hasn t seen before all the time.
2
SPEAKER_00
Whereas if you trained on, say, AWS or anything else, not only are you paying for the computer you re sitting on top of, and you re not really getting any of that back, you re also kind of training on one specific set that you have.
2
SPEAKER_00
You re not really seeing anything new, you re not really learning from other models at the same time.
2
SPEAKER_01
Interesting.
2
SPEAKER_01
And does this feed into the question of these, I keep thinking ablation studies, sorry, the strategy that you ve developed to measure importance or the value added by a particular peer in this network.
2
SPEAKER_01
If I have a peer that has the shard of the master data set that it has access to is just a
2
SPEAKER_01
it s a crappier shard, let s say.
2
SPEAKER_01
Does that unfairly disadvantage that particular peer?
2
SPEAKER_01
No matter how good the model is, no matter how much processing power it s invested, maybe the data is just kind of junk, at least for the particular task that it s working on.
2
SPEAKER_01
Is that an issue here of almost data fairness, like they all have access to equal quality data?
2
SPEAKER_02
Well, they all have access to the entire data set.
2
SPEAKER_02
That s not a problem.
2
SPEAKER_02
It may actually be very valuable to work on a smaller niche aspect of the problem, because then you can dominate that.
2
SPEAKER_02
I mean, because we re using conditional computation at the validators,
3
SPEAKER_01
the validators can learn that oh you re valuable for this particular type of data.
3
SPEAKER_01
So I m going to query this German model when I m thinking about German things and I m going to query this, this, you know, archive model when I m reading archive papers.
3
SPEAKER_01
The purpose here is to create essentially a corpus or a library of machine intelligence that is continually, as Alois was saying, is continually growing and sharing knowledge between the peers.
3
SPEAKER_01
It s like a continuous machine learning library.
3
SPEAKER_01
And that s what we re trying to produce.
3
SPEAKER_01
That s what we re trying to create as a value for our customers.
3
SPEAKER_01
Hey, look, you can combine.
3
SPEAKER_01
Hey, what if every epoch you also talk to this network?
3
SPEAKER_01
And maybe there s something in there that you can learn from.
3
SPEAKER_01
We have, we have models of very large variety, we have, we re essentially creating a protocol that is each each of our endpoints is like the company open AI, you know, it s, it s like, hey, you can talk to me I m going to give you my embeddings for the language model that sits behind my endpoint.
3
SPEAKER_01
we re the protocol that stitches together all of those AI companies so that they can share collectively what knowledge they re adding to this global corpus library, the neural internet, as we like to say, in a kind of a cliche way.
3
SPEAKER_01
But that s, I think, the real value here is to exponentiate the amount of machine intelligence that one can access
3
SPEAKER_00
Interesting.
3
SPEAKER_02
Just to add to what Jacob was saying, another thing is that we do have our own data set, but anybody joining the network are free to bring their own as well.
3
SPEAKER_02
They don t really need to abide by the one that we re using.
3
SPEAKER_02
It s just that we have the one that we currently use just because it covers quite a large corpus of different books and different sources and stuff.
3
SPEAKER_02
But we ve had folks bringing their own data as well.
3
SPEAKER_02
And as long as it s the same modality, in this case being text, it s fine.
3
SPEAKER_00
Okay.
3
SPEAKER_00
Very cool.
3
SPEAKER_00
Ali, you mentioned that these models would learn from each other.
3
SPEAKER_00
I m not sure if I misheard that or misunderstood it, but what would the mechanism be that facilitates that?
3
SPEAKER_00
How does one model benefit in robustness terms from exposure to others?
3
SPEAKER_02
In a sense.
3
SPEAKER_02
So learning from each other is almost, I don t want to say it s a misnomer, but it is kind of the closest representation of what is happening here.
3
SPEAKER_02
What happens is every model, as Jake mentioned, every epoch, will start basically trying to ping the network and trying to find other models that it can talk to.
3
SPEAKER_02
And what happens is, what BitTensor effectively is, is a protocol.
3
SPEAKER_02
That s really all we are.
3
SPEAKER_02
We re an API.
3
SPEAKER_02
And as a machine learning engineer, you don t really
3
SPEAKER_02
necessarily need to worry about the blockchain aspect.
3
SPEAKER_02
You can really just focus on the API aspect.
3
SPEAKER_02
And since we are effectively a gigantic decentralized mixture of experts, what happens is at the BitTensor layer, any model that you create is going to have, when it plugs into the BitTensor layer, it s going to have what is effectively, it s really a way to pick experts.
3
SPEAKER_02
But in another way to think about it, if you re looking more at it from a more software engineering point of view, it s a DNS lookup.
3
SPEAKER_02
And what you re doing is you re pinging other models out there that are the experts that you re talking to, and they re responding back with information.
3
SPEAKER_02
So what happens when you re pinging them in the first place is that you re kind of sending them a portion of data and you re saying, hey, can you run this through and give me back your results?
3
SPEAKER_02
And what happens is that model will basically run through and give you back a result.
3
SPEAKER_02
And what you do is you take the output of their last layer and kind of use it as your own input.
3
SPEAKER_02
our white paper goes into much more detail about this.
3
SPEAKER_02
I m kind of just like condensing audit all into like a few sentences.
3
SPEAKER_02
There s a lot more going on there, but what we re effectively doing is we re kind of stitching these models together using the BitTensor protocol.
3
SPEAKER_02
So what s really happening is that it s kind of almost a massive interconnected model over the internet with basically using a BitTensor protocol, it kind of act as like this mesh web between all of them.
3
SPEAKER_02
And what happens is if
3
SPEAKER_02
Say, for example, you took the information from this model, you ran it through your own model, and your loss function ended up suffering as a result, you might be less likely to talk to it next time, right?
3
SPEAKER_02
Because you didn t really optimize, you ended up doing a little worse.
3
SPEAKER_02
So their information or their architecture or whatever their output is, is maybe not that useful to what you re building.
3
SPEAKER_00
OK, that s actually really helpful.
3
SPEAKER_00
And it helps to also answer a question that I had, which was, is this a mixture of experts thing, or is it an ensembling thing?
3
SPEAKER_00
So for people who need a little bit more background here, ensembling is when you have a bunch of different models, and you get them to basically each vote on what the output, the prediction, or the generated product might be.
3
SPEAKER_00
And you pick the one that got the most votes, and so you get to benefit from the wisdom of the crowd.
3
SPEAKER_00
Whereas mixture of experts models usually involve an initial step where you decide which models in an intelligent fashion, you pick which models get to contribute to this particular prediction based on the kind of problem it is.
3
SPEAKER_00
It sounds more like a mixture of experts model than the ensemble because there s that initial assessment.
3
SPEAKER_00
Okay, very cool.
3
SPEAKER_02
Yeah, so what s effectively happening is each model is kind of picking and choosing, right?
3
SPEAKER_02
You re not kind of picking all of them at the same time, you re kind of picking a subset of the ones that you re in contact with, and you re kind of optimizing over time who that subset comprises of.
3
SPEAKER_00
OK, very cool.
3
SPEAKER_00
And so you ve both now talked about this as a sort of marketplace for intelligence, an API.
3
SPEAKER_00
A lot of this reminds me of a podcast that I recorded a couple of months ago with Ben Goertzel.
3
SPEAKER_00
So he s the founder of SingularityNet, and he s got this, like,
3
SPEAKER_00
open cog framework that he uses for that.
3
SPEAKER_00
But basically, it sounded very similar.
3
SPEAKER_00
It sounded like a marketplace for this sort of activity.
3
SPEAKER_00
I don t know if you know much about SingularityNet.
3
SPEAKER_00
I d just be curious, given that there are two different efforts in this domain, what differentiates them?
3
SPEAKER_00
What s different in the philosophy?
3
SPEAKER_02
I feel very, very passionate about this.
3
SPEAKER_02
This is one of the things that speaks to me truly about BitTensor.
3
SPEAKER_02
I mentioned one of the big inefficiencies of AI is that we re always retraining stuff over and over, right?
3
SPEAKER_02
The other kind of side that is also inefficient is
3
SPEAKER_02
the idea of contribution, right?
3
SPEAKER_02
How do I contribute to the system?
3
SPEAKER_02
Today, really, if I want to contribute to, let s say, an open AI or some massive GPT model that I m creating, I really kind of have to be either part of the Googles and the Facebooks of the world or the large research institutions, but I can t really contribute any other way, right?
3
SPEAKER_02
That s kind of the first end.
3
SPEAKER_02
The second end is that if I want to contribute to the research end of things,
3
SPEAKER_02
there s kind of a minefield to navigate, right?
3
SPEAKER_02
If you want to publish in the best papers, if you want to publish in the best journals, if you want to kind of reach out to the right audience and stuff like that.
3
SPEAKER_02
And a lot of the times, if you re not part of, again, these massive institutions, it can be fairly difficult for you to kind of contribute something meaningful.
3
SPEAKER_02
One of the ethos, one of the massive things that Jake and I are both very, very passionate about at BitTensor is this idea that why is this happening?
3
SPEAKER_02
Why can t anybody really contribute regardless of their stature in the community?
3
SPEAKER_02
And so at BitTensor, the idea is that
3
SPEAKER_02
you as a human have no input over what models you re talking to.
3
SPEAKER_02
It s your own model that s picking and choosing who to talk to, who is best for it.
3
SPEAKER_02
So we kind of remove this human bias part of it.
3
SPEAKER_02
There s no kind of, you know, I can go into, there s no kind of, let s say, marketplace where you can kind of walk into it, get a pre-trained model, download it, similar to like a hugging face setup, and then start using it, right?
3
SPEAKER_02
It s just, you have your own model,
3
SPEAKER_02
your model through the potential API is enabled to be able to speak to the web and speak to other models and realize for itself who it wants to rewards and who can t.
3
SPEAKER_02
So the system truly becomes a market, as Jake was describing.
3
SPEAKER_02
And this market is really driven by ingenuity and really how good of an engineer you actually are, as opposed to what journal you published in or what institution you re part of and so on and stuff like that.
3
SPEAKER_00
Very interesting.
3
SPEAKER_00
And sorry, Jacob, did you have something to tell us?
3
SPEAKER_01
Yeah, I think exactly the same thing Alois is saying is that I think SingularityNet is like a human mediated market.
3
SPEAKER_01
And I also don t think that there s very much collaboration between models.
3
SPEAKER_01
Do models compose?
3
SPEAKER_01
BitTensor is designed around the language torch, so everything is directly composable as if you were composing a normal machine learning system.
3
SPEAKER_01
But I think the major point here is that it s a machine to machine market.
3
SPEAKER_01
neural networks learning which other neural networks are valuable and rewarding them for such.
3
SPEAKER_01
So that s, I think, one of the unique aspects of what BitTensor is.
3
SPEAKER_01
I don t know too much about the technical aspects of what Ben is doing.
3
SPEAKER_01
I mean, I m sure it s pretty good.
3
SPEAKER_01
So I can t really speak too much on that comparison.
3
SPEAKER_01
I might also add that there s similarities.
3
SPEAKER_01
And I think there s a couple other teams out there that see this potential for
3
SPEAKER_01
Hey, we should take we should take neural networks into the internet era.
3
SPEAKER_01
Yeah.
3
SPEAKER_01
Are we in, are we in 1983 where it s going from mainframe
4
SPEAKER_02
frames to the internet.
4
SPEAKER_02
Right.
4
SPEAKER_02
And I think, I think that there s going to be many different approaches to this, to this task and you know we have our specific group but the thing that actually makes us very similar to Ben is that is that we see that vision.
4
SPEAKER_02
wow, in the future, artificial intelligences are going to be connected together.
4
SPEAKER_02
They re going to be talking across the web.
4
SPEAKER_02
What s that protocol going to be?
4
SPEAKER_02
What s the thing that they re going to be sharing?
4
SPEAKER_02
And is that going to allow us to scale from the 500 billion to the 100 trillion?
4
SPEAKER_02
And I think that is actually how we re going to scale, right?
4
SPEAKER_02
The only thing bigger than the open AIs and the Googles is all of them combined.
4
SPEAKER_02
And if you look at the largest, you know, beginning of the call, the largest supercomputer in the world is Bitcoin, right?
4
SPEAKER_02
And it gets to that size by being permissionless open over the internet.
4
SPEAKER_02
And so, you know, there s many people, including Ben, that are taking that step, you know, drawn by the vision, the potential of, you know, internet-based machine learning.
4
SPEAKER_01
It s a really interesting facet of this, too, is that kind of perspective.
4
SPEAKER_01
It s hard not to notice the quality of the people who are working on this problem.
4
SPEAKER_01
I ve seen that before, whether our time at YC or our time looking at some kinds of crypto investments.
4
SPEAKER_01
I remember talking, actually, this is going to sound like a name drop, but it s actually just to showcase my own stupidity here.
4
SPEAKER_01
At Y Combinator, one of our batchmates was Devin Finzer, who was the co-founder at the time of this little dinky company called OpenSea that seemed like a really dumb idea.
4
SPEAKER_01
And I had a bunch of conversations with Devin, where he was telling me about what he did, and he would tell me, like, oh, what you re working on sounds super cool.
4
SPEAKER_01
And I was like, well, yes, it does.
4
SPEAKER_01
And he d go on about his thing, and I d be like, yeah, sounds interesting.
4
SPEAKER_01
And you know, anyway, the quality of this guy, you could already tell him and his co-founder Alex were just like top-notch people.
4
SPEAKER_01
But the idea, I kind of have this problem where like I can t seem to grasp that like crypto vision, but I keep seeing people of such insane caliber going towards it that I ve sort of resigned myself to like, you know, I don t get NFTs, but I think if like Gary Tan, Initialized Capital, if YC, if Andreessen Horowitz,
4
SPEAKER_01
If the entire planet of super skilled people like you guys is working on this problem, it s very hard to be short that position.
4
SPEAKER_01
I do want to ask a question about scale.
4
SPEAKER_01
So you alluded to it, this idea that you re reaching a big scale.
4
SPEAKER_01
Bitcoin is the biggest supercomputer and so on.
4
SPEAKER_01
So how large is the network right now?
4
SPEAKER_01
How is it growing?
4
SPEAKER_01
How fast is it growing?
4
SPEAKER_01
Can you speak to that a little bit?
4
SPEAKER_01
Maybe I ll check in with Ella actually on that.
4
SPEAKER_00
We had our official launch back in November in 2021, and we have been kind of growing rather steadily since then.
4
SPEAKER_00
Don t want to kind of knock on wood, but we have been growing rather well in the sense that we kind of have set up what is effectively almost an active mining pool, where pool is probably not the right word, but it s an active mining set of BitTensor nodes that are allowed in the system.
4
SPEAKER_00
of approximately 2048 nodes.
4
SPEAKER_00
It s kind of similar to the idea of, you know, the mixture of experts paper where they use 2048 experts, so kind of use the same thing to kind of get a sense of how things are working, how the network is, kind of almost like a fish tank kind of manner.
4
SPEAKER_00
And interestingly, it has grown quite a bit, and
4
SPEAKER_00
kind of one bit of caveat to that is that these 2,000 are miners, not all individual users.
4
SPEAKER_00
So it could be somebody would say three or four miners or even a hundred perhaps.
4
SPEAKER_00
But the system is kind of growing steadily.
4
SPEAKER_00
We have been kind of researching within the system, kind of see how well we can perform, how well of a model we can build.
4
SPEAKER_00
What does the adversarial dataset look like?
4
SPEAKER_00
What does the adversarial performance look like?
4
SPEAKER_00
And so on.
4
SPEAKER_00
And kind of have been incrementally getting really promising results from this whole system.
4
SPEAKER_00
And what s happening now is that we ve actually set up a bit of a...
4
SPEAKER_00
we have a civil resistant, I wanna say algorithm.
4
SPEAKER_00
It s a bit of a proof of work that people have to do before they can join the network.
4
SPEAKER_00
And the more people try to join, the harder it becomes because we need to almost act like a funnel to kind of prevent somebody from deploying, let s say 10,000 miners basically joining at the same time.
4
SPEAKER_00
It kind of just becomes tighter and tighter the more people try to join.
4
SPEAKER_00
And it s getting to the point now where there s been almost a wait list in a lot of ways for people trying to join the network in the first place because a lot of people are trying to join at the same time.
4
SPEAKER_00
It s becoming rather difficult.
4
SPEAKER_00
So while we do have 2,048 active nodes in the system, there s likely the number of people trying to kind of use it and kind of grow into it has been much larger than that.
4
SPEAKER_00
So growth has been rather steady and rather well and kind of projecting to grow a little bit further this year to kind of reach the states that we kind of always talk about.
4
SPEAKER_00
From a kind of more of a machine learning view though,
4
SPEAKER_00
We have been kind of trying to measure, well, approximate the number of hyperparameters on the network right now, which is within those 748 nodes.
4
SPEAKER_00
And I think Jake, you might be able to speak better to that, but I think we re reaching GPT-2 levels and we ve only been launched for about four months though.
4
SPEAKER_00
So it s been rather promising at this point.
4
SPEAKER_00
I think we ve passed 110 billion parameters at this point and we re kind of growing further.
4
SPEAKER_00
Yeah.
4
SPEAKER_01
And so that s the total parameter count of like all the neural networks that are in these different peers.
4
SPEAKER_01
Okay.
4
SPEAKER_01
Exactly.
4
SPEAKER_00
Yeah, exactly.
4
SPEAKER_00
So it s happened, it s a little bit hard to kind of approximate the exact number just because a lot of people will use perhaps a pre-trained model they downloaded from Hugging Face.
4
SPEAKER_00
We have no visibility of that.
4
SPEAKER_00
We have no idea what it is that is actually running on the system.
4
SPEAKER_01
Oh, that s so interesting.
4
SPEAKER_01
That s right.
4
SPEAKER_01
You have no idea what the actual architectures are.
4
SPEAKER_01
So how do you even get a parameter count then if you don t know?
4
SPEAKER_02
It s a very conservative estimate.
4
SPEAKER_02
So 110 is based on basically the smallest model that people are likely to be running, which is actually about half the size of the default.
4
SPEAKER_02
But I mean, in reality, it could be much larger than we know.
4
SPEAKER_02
Like if somebody is just running one GPT-2 model on the network, we get 175 billion parameters right there.
4
SPEAKER_02
And potentially all 2,000 nodes could be running 175 billion parameter.
4
SPEAKER_02
The scaling, though, is difficult because to run a single forward pass of a GPT-2 model is actually incredibly slow if you re just running on a single computer, right?
4
SPEAKER_02
So one of the ways that we re scaling right now, and we re measuring this very closely, is how many requests can the system process?
4
SPEAKER_02
How large can the sequence lengths be?
4
SPEAKER_02
through the validators.
4
SPEAKER_02
And then as that number grows, we can go, OK, great.
4
SPEAKER_02
We re getting a lot more fidelity into this system.
4
SPEAKER_02
And we re kind of like growing out this network to be highly performant, right?
4
SPEAKER_02
And the one thing that we have that s kind of cool, right, so it s at the token price, we can calculate how much computational power we could be extracting in a perfect market.
4
SPEAKER_02
And so we re kind of growing up to that number into the hundreds of thousands of dollars per day of compute and making sure that the validators are doing a good job of asking the network to expend that energy in terms of what is it running more compute or having better internet connections, etc.
4
SPEAKER_02
So, again, it s hard for us to tell exactly the number of parameters, but a really conservative estimate is about 110 billion.
4
SPEAKER_01
Okay.
4
SPEAKER_01
That s really interesting.
4
SPEAKER_01
And that also makes me wonder, when you zoom out and you look at this entire network and what it s doing, you effectively have like, I don t know how many, like a dozen or hundreds maybe of instances of something like GPT-2 running, making predictions, comparing their predictions, blah, blah, blah.
4
SPEAKER_01
like what, each token, each sentence or something.
4
SPEAKER_01
So people often argue like, oh, this is terrible for the environment, you know, GPT-3, these large language models, like there s a lot of pollution.
4
SPEAKER_01
Does this exacerbate that?
4
SPEAKER_01
Do we have this just happening in parallel?
4
SPEAKER_01
And how does this ultimately make economic sense if I could turn to GPT-3 and get a, you know, one price per token, but then I go over to this system and economically it seems like it almost has to accommodate like a hundred times that price per token?
4
SPEAKER_01
I m sure I m missing something, but I m just curious what it is.
4
SPEAKER_02
Well, I mean, I think one of our fundamental arguments here is that there s a lot of waste in the machine learning world as is today, right?
4
SPEAKER_02
You have tons of researchers training the same models and publishing them to NIPS.
4
SPEAKER_02
And we re saying, hey, well, why don t we do it collaboratively?
4
SPEAKER_02
We don t need to train one system to make this work.
4
SPEAKER_02
And another thing is that we re saying, hey, look, there s efficiency in the way that we re measuring this digital commodity.
4
SPEAKER_02
We want informational significance.
4
SPEAKER_02
We want embeddings that produce value.
4
SPEAKER_02
So that s exactly what we want.
4
SPEAKER_02
We re going to measure that exact thing.
5
SPEAKER_01
And then we re going to build a market around that.
5
SPEAKER_01
And hopefully, the market will be efficient in producing that thing.
5
SPEAKER_01
Instead of papers, we re getting the commodity we want.
5
SPEAKER_01
And then also, there s other aspects.
5
SPEAKER_01
For instance, in Bitcoin, the computational power tends to arbitrage for cheap electrical sources.
5
SPEAKER_01
That s one thing.
5
SPEAKER_01
And I think it s hard to say exactly what the token price will be.
5
SPEAKER_01
And I wouldn t want to speculate.
5
SPEAKER_01
But we re aiming to produce something that is state of the art.
5
SPEAKER_01
And I think that our competitors are using a lot of electrical value already.
5
SPEAKER_01
75% of Google s energy costs go to machine learning.
5
SPEAKER_01
So I think that we re definitely going to be using electricity.
5
SPEAKER_01
There s no question about it.
5
SPEAKER_01
But hopefully, we re going to use it in an effective way, in the most effective way that we can.
5
SPEAKER_01
And what else can you really hope for?
5
SPEAKER_00
Yeah, no, I mean, to be clear, I find most of the arguments for that kind of thing, the kind of environmental arguments around large language models, like profoundly uncompelling when you look at just the scale of things and like, what does it mean?
5
SPEAKER_00
What is the marginal value of a token generated in terms of just like human thought time and how that would translate into like food consumption and
5
SPEAKER_00
And there s a whole other side of the ledger that is completely unexplored and unexplorable.
5
SPEAKER_00
OK, cool.
5
SPEAKER_00
I want to ask one last question that is about the sort of should we, shouldn t we side of this, too.
5
SPEAKER_00
There are arguments that are interesting, at the very least, when it comes to the malicious use of AI.
5
SPEAKER_00
OpenAI, for example, famously decided they re not going to release GPT-3, citing, in part, concerns over malicious use.
5
SPEAKER_00
Same with DALI, certainly same with DALI 2 and so on.
5
SPEAKER_00
And there are other firms that are kind of making similar noises.
5
SPEAKER_00
Starting with malicious use, there s also obviously the kind of alignment story that we ve been exploring a lot on the podcast long term.
5
SPEAKER_00
Where do you go with systems that get more and more human capable?
5
SPEAKER_00
How do you ensure that these systems are developed safely and aligned and don t have side effects?
5
SPEAKER_00
that they produce when they solve problems in dangerously creative ways.
5
SPEAKER_00
So I guess those two things, like malicious use and alignment risk, superficially seem like they re exacerbated when you decentralize and democratize access like this, but I d be really curious to hear you both opine.
5
SPEAKER_01
So I think that the fair use and the problems of malicious users tend to be things that centralized organizations
5
SPEAKER_01
focus on because the more glaring problem is the fact that they re deciding who gets to decide what is good and where AI should be moving and who owns it.
5
SPEAKER_01
And I think that the decentralization is really about that.
5
SPEAKER_01
It s about making sure that the question of what can or cannot be done with AI is answered collectively in a democratic way, instead of the ivory tower, people saying, I don t like this and I don t like that.
5
SPEAKER_01
caveat here, right?
5
SPEAKER_01
Ivory Tower people are really smart and they maybe have a really good view from their tower.
5
SPEAKER_01
But I think that one of, I think that our thesis here is that the bigger issue for AI is not the AI fired the nukes and they take over the world.
5
SPEAKER_01
It s more that like a small group of people come to own it and use it for themselves rather than that collectively it s aligned with humanity.
5
SPEAKER_01
decentralization solves that problem very well because decentralization is all about centralization of power, right?
5
SPEAKER_01
And so one of the things that we think we can do with tokenization and building these open protocols is allow for that kind of quality to happen.
5
SPEAKER_01
So there s not just five big players, there s tens of thousands of people that control AI and get to decide what it learns and who gets to use it and who gets to talk to them and all these things.
5
SPEAKER_01
That s how we re going to, as humanity, make sure that AI is working for us by making sure that the AI s bottom line is us and not some small group of people.
5
SPEAKER_01
Interesting.
5
SPEAKER_02
So effectively, Jake, you ve touched on the final last bit of ethos for BitTensor, right?
5
SPEAKER_02
We talked initially about the inefficiency.
5
SPEAKER_02
We talked about the issue of access.
5
SPEAKER_02
And now we re talking about centralization.
5
SPEAKER_02
You kind of nailed it on the head.
5
SPEAKER_02
But the one last bit is that
5
SPEAKER_02
The problem of, I think one of the things that people always talk about is AGI, artificial intelligence, which is the holy grail really of AI.
5
SPEAKER_02
The problem itself requires a lot more ingenuity, teamwork, people really generally just a lot more work than can be done by a single company.
5
SPEAKER_02
And to be completely honest with you, I find it a little bit more terrifying that a single company like, I don t want to mention, would own AGI compared to all of us owning a piece of it.
5
SPEAKER_02
Right.
5
SPEAKER_02
That s a company will always use it for their profit margins, whereas if we all own a piece of it, we might just be able to do something more interesting than that with it.
5
SPEAKER_00
OK, very interesting.
5
SPEAKER_00
So I think I find myself temperamentally very much in agreement with the thesis and a lot of the things that underpin it because I have a perspective that s a little different when it comes to long-term AI risk.
5
SPEAKER_00
And I view AGI as basically intrinsically dangerous.
5
SPEAKER_00
I think that we currently have not solved the alignment problem.
5
SPEAKER_00
And I expect the default outcome from strong AI to be catastrophic.
5
SPEAKER_00
In that worldview, which you know, it s totally cool.
5
SPEAKER_00
You guys don t agree with it And then there there s a lot of disagreement with that perspective.
5
SPEAKER_00
Obviously, there s far from consensus So so would you say that there is so there really isn t much?
5
SPEAKER_00
Consolation for for somebody from my perspective in this approach from that from that perspective at least is that fair to say?
5
SPEAKER_01
I m perhaps I mean I think maybe it comes down to the conception of what AGI does what it looks like right and and what is it tethered to and
5
SPEAKER_01
And I think, you know, what is it working for right what is it if it s going to be a traditional machine learning system doesn t have an objective.
5
SPEAKER_01
We re tying that objective to tokenization and decentralization we re saying hey, it s got to be tied to the holders of these tokens.
5
SPEAKER_01
And so then that s its leash for AGI.
5
SPEAKER_01
But I think maybe that s not, maybe it s that perhaps where you disagree.
5
SPEAKER_00
So the default argument for AGI risk is that the moment you specify any kind of optimization function, optimization objective, a sufficiently advanced AI system will come up with dangerously creative ways of making that number go up.
5
SPEAKER_00
So whether that s the stock market or the categorical cross-entropy of a classifier or whatever, the ways that cause that number to go to the moon have as a side effect, almost invariably, the destruction of all human value on the planet.
5
SPEAKER_00
And so while I couldn t necessarily describe the specific way in which this system could get hacked to blazes and cause the world to turn into a pile of paperclips, the argument would be that this is something that a sufficiently capable AI system would end up doing.
5
SPEAKER_00
It s a question of how rather than of if from that perspective.
5
SPEAKER_00
But it just depends ultimately on the extent to which you buy that argument.
5
SPEAKER_00
And again, a lot of people don t.
5
SPEAKER_00
And so it s not like this is just the way things are.
5
SPEAKER_01
No, I do actually buy that argument.
5
SPEAKER_01
I believe it.
5
SPEAKER_01
But for instance,
5
SPEAKER_01
Some people think that one of the ways that you can, for instance, save the environment like hey capitalism is that AI were this AI that all the while we care about is you know GDP number goes up, and so we cut down on the forests.
5
SPEAKER_01
And some ideas from economic environmentalism is to make sure that you can t, that there s this alignment between the two systems.
5
SPEAKER_01
That, hey, the bottom line of the company is able to see that it needs to maintain the forest.
5
SPEAKER_01
And so there s an alignment between those two things.
5
SPEAKER_01
And I think that if we want AGI to be in alignment with humanity while it tries to solve this problem,
5
SPEAKER_01
We don t want it to just kill humans to make the number go up well we need to make sure that the humans are owning the thing that it wants, so it doesn t doesn t kill the humans, right.
5
SPEAKER_01
It s aligned it s aligned with us, so it goes hey you know that that that avenue is not actually making the number go up.
6
SPEAKER_01
go up, right?
6
SPEAKER_01
So it s about crafting that objective function in such a way that this AGI doesn t go that direction, right?
6
SPEAKER_01
Number go up means actually help all those people that are holding these tokens that it needs to help.
6
SPEAKER_01
And that symbiosis is a really difficult problem.
6
SPEAKER_01
And we don t really have the answer to that.
6
SPEAKER_01
So I mean, we re not claiming, but we have the perfect answer.
6
SPEAKER_01
But we think that tokenization allows us a lot of maneuverability in the way that we can represent that alignment problem.
6
SPEAKER_00
Yeah, it certainly is a new axis on which to define loss functions that feels more fuzzy, which I mean that in a good way.
6
SPEAKER_00
So one of the challenges with well-formed objective functions is that they tend to be more intrinsically side-effecty.
6
SPEAKER_00
You can really nail the crap out of this one specific number.
6
SPEAKER_00
And because it s such a specific number, it s not tied to a process.
6
SPEAKER_00
It s, for various reasons anyway, more vulnerable to this.
6
SPEAKER_00
I guess I just tend to assume that something like this in the limit, though, remains a problem just because an AI would find itself incentivized to think
6
SPEAKER_00
find ways to convince people to say that its outputs were valuable or things like that.
6
SPEAKER_00
The metagame becomes the issue.
6
SPEAKER_00
But again, I think one of the challenges is for people like me, I don t honestly know what I m proposing as an alternative.
6
SPEAKER_00
This stuff is going to continue.
6
SPEAKER_00
So there is the question of what happens in the meantime, even if I end up being right and everything goes to shit.
6
SPEAKER_00
Do we want the world to go to shit in the hands of a specific company or other?
6
SPEAKER_01
I feel like we could have a much longer conversation about this particular point.
6
SPEAKER_00
Absolutely.
6
SPEAKER_00
I mean, that s, yeah, that s the problem with alignment.
6
SPEAKER_00
This is a beer problem.
6
SPEAKER_00
We need to sit down, have a beer and talk about it.
6
SPEAKER_00
Absolutely.
6
SPEAKER_00
No, look, I super appreciate you guys, especially being patient with my very limited understanding of blockchain stuff and giving me that explain it like I m five version.
6
SPEAKER_00
Where can people go to follow BitTensor?
6
SPEAKER_00
Where can they go to follow your work and dive deeper into it?
6
SPEAKER_00
So we re a fully open source project.
6
SPEAKER_02
Our entire stuff is on GitHub, and you re free to go to bittenture.com, check out our network.
6
SPEAKER_02
There s a network tab to kind of show you what the progress of the network looks like right now.
6
SPEAKER_02
Check out our GitHub, check out our white paper, as well as the light paper that s coming out very soon.
6
SPEAKER_02
And we also have a Discord link that I m happy to share with you as well.
6
SPEAKER_00
Awesome.
6
SPEAKER_00
Actually, if you can, I ll also share that on the blog post that ll come with the podcast.
6
SPEAKER_00
And if you re listening to this and you re really excited about this, do check that out because we ll have all those links there.
6
SPEAKER_00
Alaa, Jacob, thanks so much for joining me for this.
6
SPEAKER_00
This is a ton of fun.
6
SPEAKER_00
Thanks, Will.
6
SPEAKER_00
I appreciate it.
6
SPEAKER_00
A lot of fun.
6
SPEAKER_00
Yeah, thank you so much.