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Welcome to the Metaverse podcast, episode two.
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My name is Noah Kravitz.
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Coming up on the show, a conversation with Jacob Steeves and Ala Shabana from BitTensor.
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BitTensor is creating an open decentralized peer-to-peer network that functions as a market system for the development of artificial intelligence.
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That s a mouthful, but they re all really meaningful words.
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There s a lot packed in there.
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Jake and Ala do a great job of explaining what they re doing, why they re doing it, what it s all about, all the technical details.
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But in essence, what they re doing is they re building an online community, a network for engineers, people creating machine intelligence, artificial intelligence, to come together for a few purposes.
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One is to increase the efficiencies in the way that AI is created these days.
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Jake and Ala identified a problem with just inefficiencies in the way that artificial intelligence is created, the way that advances are shared and built upon by the community.
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They think they have a better solution in BitTensor.
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So we get into that.
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And then the other thing is something that gets into the concept of a DAO, D-A-O, or Decentralized Autonomous Organization.
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This essentially is a structure for building a community online that has ownership and governance baked right into the structure.
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So you can use a DAO theoretically to build anything from a social club to a company with a sort of alternative form of governance to actually in theory, a government itself.
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There s a whole lot in all of that.
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Jake and I, again, we have a great conversation about it.
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So stick around, even if you re not interested
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in machine intelligence and this idea of building a better artificial intelligence marketplace.
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These core concepts of building a decentralized organization on the blockchain are really interesting and really vital to why people are excited about all this stuff.
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So stick around.
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Also, there s a great article in the New York Times from March 8th.
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I ll put a link in the show notes so you can check it out.
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But the article is called Reality Intrudes on a Utopian Crypto Vision
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And it gets into this whole idea of DAOs, what they re being used for, you know, for good purposes and for scams for various purposes.
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So check that out as well.
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Another thing about BitTensor, what I really like about what this group is doing is that
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They re also really interested in AI itself and what it is now and eventually might be used for, and trying to find a way to keep that aligned with human interests, the greater good and the well-being of people, first and foremost.
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So great conversation, stick around for that.
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First, a couple of quick notes of housekeeping.
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Episode one of Metaverse is live.
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It s a conversation with Nenea Reeves from Trip about the mindful metaverse.
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Check that out if you haven t already.
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Nenea is a great guest, it s a great conversation.
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I m thrilled that that turned out to be the first episode.
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So take a listen, watch on YouTube if you like, it s good stuff.
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Also, I mentioned YouTube.
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You can get the podcast audio only wherever you get your podcasts, Apple Podcasts, Spotify, wherever else.
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So subscribe, share it with your friends.
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If you like it, leave a review.
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Also, the podcast is available in video format on YouTube.
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So check that out.
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And then also there s a substack metaverse.substack.com.
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There s a newsletter blog post newsletter kind of thing.
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We re going to be posting once a week for now.
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So you can get a little more in depth if you prefer to read.
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then listen, or if you want to do both and just get a little more depth and a whole bunch of links to go learn more on your own, sign up for the Substack.
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Again, episode, the first post rather is up, second post coming this week.
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Right now we re aiming for a Tuesday podcast, Wednesday newsletter update schedule.
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So, you know, that may get refined as we go, but that s what we re on right now.
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Podcasts on Tuesdays.
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newsletter updates on Wednesdays.
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So Substack will get you all that or just subscribe to the podcast wherever you like.
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And we re on social media, of course, who s not on Twitter and on Instagram right now at Metaverse Pod, all one word.
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And finally,
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If you wanna leave a comment, if you have a question, a suggestion, just wanna say hi, you can do so on the socials, or if you re old school and you like email, you can email us metaaversepod at gmail.com.
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Meta, M-E-T-A, averse, A-V-E-R-S-E, pod, P-O-D, at gmail.com.
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All right, I think that s it.
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Let s get to it.
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Jake and Ala from BitTensor, enjoy the episode.
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All right, today we are talking to the guys from BitTensor.
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I m here with Jacob Steves, Jake and Ala Shabana, the co-founders of BitTensor.
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Gentlemen, thank you so much for coming on the show and welcome.
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Thank you.
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Thanks for having us.
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So, all right, who wants to go first and tell us, there s a lot to get into, obviously, and we were talking before I hit record about how the different areas of all of this stuff, Metaverse, Web3, blockchain, everything that we re trying to explore in the show and how the work you re doing kind of fits all of the buckets and then some.
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So why don t we start by just giving us an overview of what BitTensor is and what you guys are working on.
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Sure, no problem.
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Yeah, so we hit a lot of buckets, right?
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So AI and blockchain, how does that make any sense, right?
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The ideas for BitTensor came around in 2015 by just noticing the computational scale of the Bitcoin network, right?
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It s the largest supercomputer in the world.
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It s like 500 times larger than all of Google s data centers combined, you know, hashing out and many times larger than that now.
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Right.
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And for the uninitiated, I know there ll be at least a few folks listening who are like, what is all this?
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There s one, there s a bigger computer.
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This is spread out.
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These are nodes spread out.
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Yeah, they re spread out.
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They re working on a common problem, though, right?
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You could say that it s a supercomputer working on hashing the next block of the Bitcoin blockchain.
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And it raises this question like, why and how?
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Those are the two big questions.
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Yeah, like, how does this massive network come into be when there s nobody running it, really?
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It s decentralized.
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And, you know, we think the fundamental reason is that Bitcoin, along with inventing this digital trust with digital currencies, allowed for the first creation of these digital markets, right, where you can build like this commodity markets, global commodity market for the creation of hashes.
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And that s stimulated the advancements in chips, ASIC chips for hashing the Bitcoin blockchain next hashes.
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And also stimulate this hive mind, this global hive mind of engineers that are putting on their desktop computers.
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And obviously it s evolved to be really sophisticated.
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Now you have these massive data centers in China and Kazakhstan and across the United States where you rows upon rows upon rows of ant miners, you know, hashing up.
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At the time, both me and Ala were machine-learned engineers, and AI is also fundamentally a competition problem.
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It s the intersection between algorithm
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compute and data.
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And it s building this thing, you could call it a commodity in digital intelligence.
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And so we were like, well, why don t we apply this new technique, this new invention of digital trust and digital currencies to create this commodity, which is incredibly valuable, right?
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It s valued in the billions of dollars by companies like Google and OpenAI.
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And so we, you know, around that time, we started working on, you know, building a mechanism.
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What does that mechanism look like?
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How could we possibly measure machine intelligence and then get computers from all across the
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globe to build it and contribute it to this network.
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And so that s what BitTensor is.
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Basically, it s just a protocol for measuring and incentivizing the production of machine intelligence.
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Decentralization is part of it, right?
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So AI is part of it.
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Blockchain is part of it.
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But you would say that we re not trying to create the same killer app as Bitcoin.
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Bitcoin wants to be decentralized because they want to be censorship resistant.
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We re taking advantage of
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decentralization because we have engineers around the world and connecting them together.
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We re taking advantage of decentralization also potentially because we want to approach this alignment problem in AI.
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You may have heard this, right?
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How can we align the development of artificial intelligence with humanity?
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And we think decentralization, which is about power and control, is also an answer to that.
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So yeah, that s a really high level explanation of where BitTensor is and what we re about.
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Right, so there s a lot in there to unpack that I think is important.
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I was going to say, I don t wanna put you on the spot and ask you to explain the blockchain, except I think I m gonna wind up doing that with every guest, at least for the first however long of the show.
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So a couple of things in here.
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You guys are BitTensor, I m saying guys, because you re two guys, but I don t mean to be gender specific.
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BitTensor is building out a network
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Is that a fair, okay.
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And the idea is to bring people together, bring folks together who are working on an artificial intelligence.
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So machine learning engineers, let s say, there are other job titles too, from across the globe, working on their own computers, workstations, wherever they are, right?
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to share intelligence, to share sort of the output of their own work, to maybe work collaboratively on larger problems, that kind of thing?
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Exactly.
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Okay.
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Yeah.
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So I guess
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before getting into some of the stuff like, wait, what s hashing and decentralization, all that, maybe just to keep it in the realm of software engineering and that kind of thing, how does this differ from open source?
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How does it differ from other ways that people have been and are collaborating, you know, working remotely, whatever it is?
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Right, I think, so, sorry, go ahead, Olive.
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No, I was just gonna basically,
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I want to say, bring it back down to a lower level.
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Essentially, as it stands today, as Jake was essentially saying, is that the field of AI is problematic.
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Right.
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So first of all, AI production itself is inefficient.
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There s thousands of engineering firms worldwide.
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There s all kinds of websites like Kaggle and so on that really enable you to create these models and kind of try to either monetize them in some way or have them kind of published and have people use them.
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But what you re really doing is you re just building on the existing research.
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But your actual model that you re building is actually probably learning stuff that other models have already learned.
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So there s a bit of an inefficiency there, right?
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Kind of retraining all over again every single time.
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And this is because we don t have the ability to measure intelligence as a commodity today.
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Can I ask you real quick just to break down that process of training a model, feeding it with data, that kind of thing.
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Can you maybe on a very high level just kind of explain for folks not in the field sort of what that entails?
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Yeah, yeah, yeah.
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So I think on the most basic level, you essentially have three working groups when you re training a model.
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You have your data set, which could be anything.
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So let s say for the sake of example, you are trying to train a model that learns what buses look like on pictures.
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And then you have your model.
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You read my mind.
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I was like, captures, captures.
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Captures, yeah.
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So then you have your model, which is essentially just a, it s effectively a computer program that will optimize its own internal coefficients to understand what
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information that s coming out of the data set that will help you identify what a bus looks like, for example, like it has wheels and so on.
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And finally, you have your loss function, right?
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All the loss function is, it sounds like a scary term, but what it really is, is it s just an indicator for the model of what kind of mistakes it made, right?
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How incorrect was it by the time that it learned whatever a bus should look like?
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Right.
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And that s essentially really the most basic form of machine learning.
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You just have your data.
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It could be labeled or unlabeled.
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And if it is labeled, then you want to basically just check how close you are to the right ground truth.
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So then if I m working by myself and I have trained a model and I m happy with how well it s able to identify bus versus not bus.
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Yeah.
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How would somebody like, say yourself,
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use my work.
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So let s say, for example, I m trying to train a model that is working on something similar.
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I m trying to identify what cars look like, not buses.
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So one of the most infamous problems of machine learning is something called catastrophic forgetting, where, for example, if you flip a single pixel on an image, it s going to think it s a giraffe, not even a bus anymore.
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So one of the things that, for example, I could do if I m training this model to learn what cars look like, is I can maybe speak to yours and say, hey, I m looking at this kind of picture,
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what is yours learning?
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What kind of insight can you give me from that?
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And what s going to happen is yours is going to give me back some information and it s going to say, okay, maybe this isn t this, and this is what I think it looks like.
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And that s kind of what I can learn from you and to kind of make myself a little bit better.
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Now, the difference is, obviously, it s kind of maybe a little bit down the line, but some models might not be compatible, right?
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Maybe what information you give me is going to make my model a little bit dumber, maybe not smarter.
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So in this case, what I would do is maybe I would look for someone else to speak to and learn from them.
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And that s where kind of the concept of the network that Jake was describing comes in.
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Perfect.
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That was great.
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Okay.
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Sorry to interrupt, but that was good, good ground setting.
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So.
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Yeah, absolutely.
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Yeah.
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So like, if you look at a lot of these other projects that are, they re sharing models, like Hugging Face is a really good example, right?
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You re sharing the, you re sharing the weights of a train model, but you re not actually,
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sharing the knowledge itself.
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So when you check out a hugging face model, you look at its performance on a data set.
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You say, okay, how well did it do on glue, for instance, which is a benchmark, like it s a test that you can put models through, right?
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And so we re measuring and searching the space of machine knowledge using human-defined explanations of its intelligence, right?
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But the beauty of digital currencies and using computers to measure something like a commodity is that it s high resolution.
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So we can actually measure
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how valuable this model, the information that this model is producing, using entropic methods like Fisher s information, which is like a measure of the informational significance of the model to another.
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And before we couldn t do that, we couldn t really measure the performance or the value of one computer to another because we didn t have the technology to do that, right?
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And Bitcoin, that was the invention of Bitcoin, right?
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Digital trust, the ability for computers to have a conception of trust and value that they can transfer between each other.
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And so how has BitTensor taken advantage of that?
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So we built this network where it s a valid, it s like Bitcoin.
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I mean, so your users might not know too much about the way that Bitcoin works, but essentially Bitcoin is a set of computers that are validating the work done by the other computers in the system.
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Proof of work, you ve heard that before, right?
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So BitTensor is a proof-of-work network also, where the computers are validating whether or not the other computers in the system are producing information that is valuable to the collector.
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So hey, they re producing some numbers.
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We call them tensors, the things that are being passed between the computers.
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Tensors being the root word of languages like TensorFlow, for instance, which you may have heard of that language.
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having done many podcasts on NVIDIA.
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So they re passing tensors and these validators are validating whether or not that information is valuable.
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And so, you know, fundamentally that s all that the tensor is.
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It s a mechanism for computers to reach consensus about which computers are producing information that s valuable and then paying them out accordingly.
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Got it.
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And so to ask a very sort of genuine, but also naive sounding question,
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What is happening in the network that couldn t be done?
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Is it just a matter of the network can do it so much faster than humans could validate?
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Or is there something happening that
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And you were talking to, let me rephrase that.
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Or is there something happening that s just beyond the sphere of what even that most highly trained human could do?
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Well, I think so because, and it was mentioned at the beginning of the podcast, right?
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This amazing quality of the Bitcoin network that you have people across the globe, you know, fighting tooth and nail to build computers that are faster and, you know,
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finding energy sources which are free and available to mine this cryptocurrency.
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And so we have this exact same competitive system where, hey, I m an engineer in India, and I couldn t get into Google because I didn t go to the right school, but I m actually really, really, really good
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machine learning engineering.
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And so I can come into BitTensor and I can find, oh, there s some cute computing in my dad s laptop, but whatever.
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And I can build a special neural network that adds just this niche thing to this global market, right?
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And so we re really, we re leveraging the power of markets to make the system of machine intelligence, of artificial intelligence, much more efficient.
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And I don t think there s any company in the world that it can claim to be not just their own compute, but the stitching together of everybody else s compute at the same time.
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That s going to let us scale.
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Just to add to what Jake was saying as well.
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As he kind of mentioned the word market, right?
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So a lot of these other applications that have kind of tried to do something similar, what s happened is there s always a human bias in there, right?
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Even in academic research, there s human bias.
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We re more likely to pick, you know, research done by the most famous researcher as opposed to something done by someone maybe from a different corner of the world that might not be as famous, but still just as valid, right?
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So what we ve done with BitTensor is effectively it s
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a few lines of code that ll enable whatever model that you have currently even, doesn t even have to be a new model, to learn for itself and to choose for itself whom to reward and whom to learn from.
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So we re removing this aspect of human bias and we re actually letting the models themselves choose who s using what.
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There must be some human bias initially with the design of the network or the design of the model at first.
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Does that get filtered out over time to the point where, for all intents and purposes, the models really are choosing the best other models?
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The design of the network itself is really defined by the problems they re trying to solve.
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So if you re trying to solve a very specific problem, you re going to have a very specific architecture to it.
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So that s kind of the only bias you re going to have is towards the problem you re solving, but not towards other models.
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And then over time, really, it s going to be, as you said, it s not really going to be that relevant anymore.
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It sounds to me, and if I m way off, tell me, but it sounds to me almost like the Silicon Valley ideal of meritocracy, just, you know, hard coded and writ large and unleashed on the whole Internet.
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Yeah, exactly.
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Basically, we re in this kind of long tradition in artificial intelligence.
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I think you can situate us inside, which is, as the clockmakers of these incredible complex systems, we are backing away from
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how much control we put into them.
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So to give you an example, like in the early days of machine intelligence, they tried to actually like encode a specific quality into the visual language model, right?
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Okay, detect edges and then detect cylinders, right?
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But as the field has progressed, the methods that have been the most successful were the ones that have just stepped back and let computational
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adaptive system find the solution.
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And so we re kind of going, hey, let s step back and instead of defining these problems really explicit, let s build markets, which are like meta systems and let the network evolve itself towards the solution, you know, through that adaptive ability.
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Right.
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So let s talk about the market, the word market.
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What s the incentive?
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How does it actually work if I m
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you know, if we finished talking and I hop on my imaginary machine with my imaginary models that I m not smart enough to build, right?
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I put them into Bit, I joined BitTensor, I help work on a problem, I m identified as providing some valuable intelligence, then what happens?
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So the question is like, how do we determine if it s valuable?
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Or how am I rewarded?
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How are you rewarded?
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Yeah, how does the market aspect of it work?
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Yeah.
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Right, exactly.
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So we have a form of consensus.
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We have a set of validators on the network.
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It s proof of stake, essentially.
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To your listeners, proof of stake just means your weight in validating others, how many votes you have, is a function of how many tokens you have attached to your computer.
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And I, and I, not to derail you, but I get tokens by doing good work.
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Exactly.
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Okay.
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Exactly.
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Yeah.
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That s what I was going to go to.
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So it s okay.
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So they, they validate the knowledge that your computer is producing.
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Um, so Alan mentioned this thing called catastrophic forgetting, which is this issue where your neural network, you know, trained on one problem.
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when you try to train on another problem, you forget the previous one.
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And the solution to that is actually to look at the weights of your neural network and go, hey, this one s super important for this previous problem, let s saturate it.
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And so there s this way, a statistical technique really, of determining whether or not a particular component of a neural network
2
SPEAKER_02
these massive multi-hyperparameter functions that we train in neural networks in machine intelligence, how valuable that thing is, right?
2
SPEAKER_02
And so these validators are doing the exact same thing.
2
SPEAKER_02
They re going, hey, how valuable is this parameter?
2
SPEAKER_02
In this case, it s a computer producing tensors.
2
SPEAKER_02
And goes, okay, well, it s worth five.
2
SPEAKER_02
not five, but it s all normalized and statistical, say five out of 10.
2
SPEAKER_02
And then we tally that up and that becomes a scoring, a global scoring, which then advises the new and the inflation of the digital currency.
2
SPEAKER_02
So it s very similar to say Bitcoin, where the incentive is based on inflation.
2
SPEAKER_00
Okay.
2
SPEAKER_00
And so BitTensor has its own token?
2
SPEAKER_00
That s right, the tensor Tau.
2
SPEAKER_00
Tau, right, okay.
2
SPEAKER_00
And what is, you guys are rolling with me, so I ll just ask them in sort of plain speak.
2
SPEAKER_00
What is Tau worth?
2
SPEAKER_00
Let s say I get five Tau.
2
SPEAKER_00
What can I do with five Tau?
2
SPEAKER_00
Al, do you wanna answer that?
2
SPEAKER_01
So, yeah, we re kind of delving into legal territory here.
2
SPEAKER_01
We actually can t divulge the actual- If you wanna read a safe harbor statement, you know, go for it.
2
SPEAKER_01
We can t divulge those specifics, but effectively,
2
SPEAKER_01
The only thing we can tell you is that the TAO itself is not being sold on exchange or anything like that right now.
2
SPEAKER_01
We kind of adopted a fair market launch, so there is no ICO, no pre-mine.
2
SPEAKER_01
Every TAO in existence today has been fairly mined and has actually contributed to the knowledge of neural networks in the past.
2
SPEAKER_02
But Al, I might mention that the real value of Tau is based on the value of the network that we re creating, right?
2
SPEAKER_02
Correct, yes.
2
SPEAKER_02
And so because Tau is the key that you use to get access to the system.
2
SPEAKER_02
And so it s like a utility token, right?
2
SPEAKER_02
Right.
2
SPEAKER_02
Killer App is the intelligence that s produced by the network.
2
SPEAKER_02
It s unlike, again, to use Bitcoin as an example.
2
SPEAKER_01
Yeah, no, sure.
2
SPEAKER_01
Full legal mode there.
2
SPEAKER_02
Bye-bye.
2
SPEAKER_00
You know, on a sort of very abstract level, this notion of decentralized everything, right, and this notion of
2
SPEAKER_00
rebuilding, you know, as I m learning more about all of this stuff, which is really just, I m just scratching the surface.
2
SPEAKER_00
But this notion of decentralized currency and this notion of, you know, sort of democratizing access to different things, and in this case,
2
SPEAKER_00
I m a highly skilled machine learning engineer who knows how to do stuff, but for whatever reasons, doesn t have a job that either, you know, allows me access to work on certain kinds of problems or to work with certain kinds of colleagues or to get compensated at, you know, the market rate of a Google engineer in Silicon Valley for that matter, right?
2
SPEAKER_00
I guess I m kind of wondering the idea of, you know,
3
SPEAKER_02
or we, you, are making it easier for folks to contribute to and collaborate on this intelligence that could theoretically, you know, benefit the whole world, right?
3
SPEAKER_02
The flip side is sort of, I still have to eat and food costs money.
3
SPEAKER_02
is that, and it might be a thing that over time I learned to decouple in my head, right?
3
SPEAKER_02
But I m thinking of, you know, I ve earned a token.
3
SPEAKER_02
The token gives me more weight in the community.
3
SPEAKER_02
That s worth something.
3
SPEAKER_02
I get it.
3
SPEAKER_02
But sort of from at the other end, like if my contributions to machine intelligence are ultimately going to be used by some company who s making a ton of money,
3
SPEAKER_02
How does that happen?
3
SPEAKER_02
Or not how does that happen, but sort of like, what s in it for me, right?
3
SPEAKER_01
So you got the TAO by contributing intelligence.
3
SPEAKER_01
Yes.
3
SPEAKER_01
They need the TAO to access it.
3
SPEAKER_01
Got it.
3
SPEAKER_01
Okay.
3
SPEAKER_01
They re your customer.
3
SPEAKER_01
Got it.
3
SPEAKER_01
Okay.
3
SPEAKER_02
So then, I mean, can I sell them the Tau?
3
SPEAKER_02
Can I trade them the Tau for, I don t know, Dogecoin or, you know, whatever that I could potentially convert into food?
3
SPEAKER_00
That is the eventual idea, yeah, is that you ll be able to kind of sell in your Tau after you ve done it.
3
SPEAKER_02
And I m not, again, I m not trying to get at your secret sauce or, you know, hold any feet to the fire.
3
SPEAKER_02
I m just trying to understand on a sort of more global level.
3
SPEAKER_02
I had a conversation yesterday with,
3
SPEAKER_02
Well, somebody working in the VR, XR space, but we got to talking about tokens.
3
SPEAKER_02
And I lost my train of thought, I apologize.
3
SPEAKER_02
And talking about, right, talking about the idea with NFTs, for instance, that a musician could record something and, or an artist is a better example, right?
3
SPEAKER_02
And the whole idea of the reselling of art is a huge global marketplace.
3
SPEAKER_02
Under fiat currency systems, the original artist doesn t necessarily see a cut of sales when the third owner sells to the fourth owner.
3
SPEAKER_02
One of the things about NFTs is the idea of changing that by baking that, you know, royalty, so to speak, baking it in.
3
SPEAKER_02
So is there a similar function if we re looking at the use of tokens to make markets in, you know?
3
SPEAKER_01
Yeah.
3
SPEAKER_01
I mean, so in modern AI discourse, a lot of the conversation has been around access, open access, open AI.
3
SPEAKER_01
But we think that the bigger issue is ownership.
3
SPEAKER_01
AI ownership, who actually controls the AI, who gets to say what it s trained on, that kind of thing.
3
SPEAKER_01
And with the way that we ve tokenized BitTensor, as an engineer, by holding Tau, you also own the network, right?
3
SPEAKER_01
So that s ownership of the system, not just the ability to access it, which is also very important, right?
3
SPEAKER_01
It s also an open network, but the ability to access it and own it.
3
SPEAKER_01
But if you did sell your Tau, all of it, then yeah, you d lose your
3
SPEAKER_01
you lose your tokenized ownership.
3
SPEAKER_01
And maybe we could build something in it where you get some sort of echo of your previous ownership, but that would be essentially like just selling a few tokens less.
3
SPEAKER_02
You know what I mean?
3
SPEAKER_02
What led you to make this your mission and go after building it this way?
3
SPEAKER_01
Yeah, I mean, we both have our own approach to this.
3
SPEAKER_01
I ll let Ala tell his story.
3
SPEAKER_01
But we met up, so we both have our own reasons.
3
SPEAKER_02
Yeah, let s get the origin story.
3
SPEAKER_00
Yeah, sounds good, actually.
3
SPEAKER_00
So, both Jake and I kind of are ex-Silicon Valley folks.
3
SPEAKER_00
I worked at VMware for quite a bit.
3
SPEAKER_00
Jacob worked at Google for quite a bit.
3
SPEAKER_00
At least that s on my side.
3
SPEAKER_00
I worked on decentralized AI for quite a while, especially in VMware systems, where it s basically hyper-converged infrastructure.
3
SPEAKER_00
we have many computers kind of trying to speak to each other and kind of learn from each other in a certain sense.
3
SPEAKER_00
Ended up kind of working a little bit of research and development on that.
3
SPEAKER_00
And in that time, I kind of
3
SPEAKER_00
arrived at a similar conclusion to Jacob.
3
SPEAKER_00
That was before we met.
3
SPEAKER_00
We ended up with, actually, sort of after we met, we ended up with a similar kind of outlook on AI, which is basically the three problems I described.
3
SPEAKER_00
Inefficiency, centralized ownership, and effectively the idea that general intelligence is not really attainable by a single person.
3
SPEAKER_00
We ended up meeting in a research collaboration that was kind of online.
3
SPEAKER_00
It was called 4AI.
3
SPEAKER_00
And I had
3
SPEAKER_00
presented something that was akin to what BitTensor was doing and you kind of ended up reaching out and saying, hey, I m like three years ahead of you doing this.
3
SPEAKER_00
You can just join me and we ll work on this.
3
SPEAKER_00
Yeah, that s really well said, Alex.
3
SPEAKER_00
We
3
SPEAKER_01
You know, I forget the concept, but it s like this idea that ideas are in the noosphere and you just kind of like pick them up.
3
SPEAKER_01
So like, there s people that have them at the same time.
3
SPEAKER_01
And so that was very much what happened with me and Alice.
3
SPEAKER_01
So like, but I kind of have, my story was before I was at Google, I was working for DARPA building what are called neuromorphic chips.
3
SPEAKER_01
You probably heard that term.
3
SPEAKER_01
Super cool company.
3
SPEAKER_02
I ve heard it and I nod along to it and I m not really 100% sure what it means.
3
SPEAKER_01
Yeah.
3
SPEAKER_01
Basically the idea, I mean, you could have a podcast just on this concept.
3
SPEAKER_01
I m sure.
3
SPEAKER_01
Really, really cool.
3
SPEAKER_01
But neuromorphic basically just means like, like the brain, you know?
3
SPEAKER_01
So chips that are like the brain.
3
SPEAKER_01
And I was working for this company that was building synapses
3
SPEAKER_01
like out of these things called memristors, which were like resistors, but you could change the resistance.
3
SPEAKER_02
Okay.
3
SPEAKER_01
Really, really cool idea.
3
SPEAKER_01
And so they re like apps, so you can put them on a chip.
3
SPEAKER_01
You could build the brain, a brain s a bunch of synapses and you could train it a billion times faster than the video can.
3
SPEAKER_01
worth an episode of your podcast, definitely.
3
SPEAKER_01
But the problem with this, and I was working for this absolute genius, his name was Alex Nugent, absolute genius.
3
SPEAKER_01
But the problem was kind of like, there s no direct market for me to plug this super fast chip into.
3
SPEAKER_01
Right?
3
SPEAKER_01
I m this genius.
3
SPEAKER_01
I got this great idea, but I have to go and work for IBM.
3
SPEAKER_01
I have to go work for NVIDIA for my idea to actually, you know, gain from its, you know, ability to beat this market.
3
SPEAKER_01
Right?
3
SPEAKER_01
And so that was, you know, that s one of the explanations for how I came to BitTensor was like, look, thinking of that, what if my boss could plug his awesome, you know, super genius chip into a market?
3
SPEAKER_01
that actually could reward and incentivize the production of these things, he d be able to build this company a lot faster.
3
SPEAKER_02
And so how is BitSensor structured as a, well, I guess it s a project that is under the umbrella of OpenTensor, is that right?
3
SPEAKER_00
Yeah, so the OpenTensor Foundation maintains the projects.
3
SPEAKER_00
BitSensor itself is fully open source, blockchain and AI.
3
SPEAKER_00
And we kind of have a Discord community that s full access.
3
SPEAKER_00
And there s, I believe, correct me if I m wrong, Jake, we have about 10 developers at this point, including you and me.
3
SPEAKER_00
Yeah, we re pretty small.
3
SPEAKER_00
Very small, yeah, very scrappy team.
3
SPEAKER_00
And yeah, we launched the network out in November last year, November 3rd, and we ve been maintaining it since.
3
SPEAKER_02
Right, we re taping beginning of March, so about four months.
3
SPEAKER_02
And how many, is it minors the right term?
3
SPEAKER_02
How many participants focus on the network?
3
SPEAKER_01
Oh, go for it, Jake.
3
SPEAKER_01
Yeah, I was just going to say miners.
3
SPEAKER_01
Yeah, exactly.
3
SPEAKER_01
So we measure the number of active computers, which is like the computers that are responding.
3
SPEAKER_01
And that s just over, what was it? $1,500?
3
SPEAKER_01
Yeah, we just passed 200 last week.
3
SPEAKER_02
Nice, congrats.
3
SPEAKER_02
And so I have to ask, you mentioned, you know, a little bit about the difference between a proof of stake network and a proof of work.
3
SPEAKER_02
A lot s been made about the environmental impact of these, you know, giant crypto mining data centers, if you will, crypto farms, mining farms, all the electricity is being used, where the electricity comes from.
3
SPEAKER_02
Obviously there s too many things wrong with the world to list, right?
4
SPEAKER_02
now.
4
SPEAKER_02
But, you know, global warming, climate change, all of that is near the top of the list.
4
SPEAKER_02
Right.
4
SPEAKER_02
What s your, I guess, first of all, what the BitTensor, do I call it the BitTensor blockchain?
4
SPEAKER_02
What s the right way to... Sure.
4
SPEAKER_00
You could call it the BitTensor blockchain.
4
SPEAKER_00
Okay.
4
SPEAKER_00
Yeah.
4
SPEAKER_02
So what s the environmental impact right now, so far as you know, and then what s kind of your take, you know, both in terms of what your companies do or your projects doing, but more globally about
4
SPEAKER_02
you know, blockchain becoming green enough that it s, yeah, you get where I m going.
4
SPEAKER_00
Yeah, no, I get it.
4
SPEAKER_00
And the argument against proof of work, blockchains, you know, I think a lot of the time it stems from a misunderstanding of the fact that the POW is actually doing something productive.
4
SPEAKER_00
Oh, it s just doing something stupid.
4
SPEAKER_00
It s just burning computers.
4
SPEAKER_02
There s all of these computers trying to solve the same math problem.
4
SPEAKER_02
Nobody needs the answer to.
4
SPEAKER_02
Yeah, no one needs the answer to.
4
SPEAKER_00
So what s the value?
4
SPEAKER_00
And then, so you could, you know, and I think that obviously the value is the fact that it secures
4
SPEAKER_00
Bitcoin network.
4
SPEAKER_00
But if you don t value Bitcoin, then it doesn t seem very valuable, right?
4
SPEAKER_00
But we re in a luckier position because we re actually producing something that people automatically understand the value of that thing.
4
SPEAKER_00
You re producing artificial intelligence.
4
SPEAKER_00
It s a commodity.
4
SPEAKER_00
It s valuable.
4
SPEAKER_00
People want it, right?
4
SPEAKER_00
So it s very easy for us to go, well, look, no, actually, this is something that people want.
4
SPEAKER_00
It s not wasted compute cycles.
4
SPEAKER_00
I mean, on top of it,
4
SPEAKER_00
the artificial intelligence system is very inefficient.
4
SPEAKER_00
As Ala was mentioning at the beginning of the call, there s a lot of repeat work going on.
4
SPEAKER_00
The next big, best model in the world, GPT-N, had to relearn everything that GPT-3 learned.
4
SPEAKER_00
And so if we can move into a world where
4
SPEAKER_00
machine intelligence, there s protocols for sharing machine intelligence that are really sophisticated.
4
SPEAKER_00
The corpus of global AI is something that people can build off in the same way that a human doesn t just learn everything.
4
SPEAKER_00
I don t have to reinvent mathematics every time I come.
4
SPEAKER_00
I m born, right?
4
SPEAKER_00
If we think that we re actually solving efficiencies in the traditional sense of AI, so we re making it more green.
4
SPEAKER_00
But to be honest, we can t make that claim.
4
SPEAKER_00
All we can say is point to it and go, look, let s try to fix this aspect.
4
SPEAKER_02
Right.
4
SPEAKER_02
Real quick, proof of stake versus proof of work in 30 seconds.
4
SPEAKER_00
Sure.
4
SPEAKER_00
In proof-of-work, you re measuring votes in a consensus mechanism.
4
SPEAKER_00
You re reaching consensus about who thinks of what, and so you need to vote.
4
SPEAKER_00
In proof-of-work, you re voting based on computational power, usually a proof-of-work algorithm like SHA-256, which is just producing hashes.
4
SPEAKER_00
And in a proof of stake algorithm, the number of votes is based on a tokenization of those votes.
4
SPEAKER_00
So the number of tokens you have.
4
SPEAKER_00
And so that ties the finite element to tokens rather than proof of work, which is computation.
4
SPEAKER_00
And they have their ups and downs.
4
SPEAKER_00
They have their various pros and cons.
4
SPEAKER_02
So as I m thinking about the closing minutes of our conversation here, I ve got kind of two threads, but also want to open it up to stuff that you guys might want to talk about that we haven t hit upon yet.
4
SPEAKER_02
So how do you want to do this?
4
SPEAKER_02
Are there things on your cliff notes, so to speak, that you wanted to hit, things you want that BitTensor s doing that you want to talk about?
4
SPEAKER_02
And if you want to back burner that, I ll hit you with one of my two questions.
4
SPEAKER_00
Allo, what do you think?
4
SPEAKER_01
That s a very good question.
4
SPEAKER_01
So the one thing that we wanted to kind of maybe emphasize a little bit, I m not sure if that s really worth digging into, it s that everybody at the project, including myself and Jacob, are all AI engineers.
4
SPEAKER_01
So our main kind of focus has been mostly on the AI bits to kind of make sure that everything s validated properly, everything is working properly.
4
SPEAKER_01
And one of the milestones that we
4
SPEAKER_01
are trying to hit is eventually to beat the size of GPT-3.
4
SPEAKER_01
So at the moment now, we ve reached, Jake, I believe it s 225 billion parameters.
4
SPEAKER_02
That s a low estimate.
4
SPEAKER_02
And again, I m sorry to interrupt you, Mitt Thompson, but for folks who don t know what GPT-3 is, we ve thrown it around a couple of times.
4
SPEAKER_01
Yeah, absolutely.
4
SPEAKER_01
So GPT-3 is kind of OpenAI s flagship language model at this point.
4
SPEAKER_01
So it s kind of state-of-the-art, it s the latest and greatest, and that s what we re using.
4
SPEAKER_01
And so GPT-3 s parameter count, I think, is 175 billion.
4
SPEAKER_01
So we re kind of approaching that number rapidly.
4
SPEAKER_01
And the next thing they want to do at BitTensor eventually is kind of beat that benchmark and kind of beat that state-of-the-art.
4
SPEAKER_01
And so that s kind of what most of our efforts have been at, and that s what they re going to continue to be at this year.
4
SPEAKER_02
And so along those lines, and this is one of my two questions, is BitTensor the project?
4
SPEAKER_02
Are you growing in terms of staff numbers or is the focus on, we ve got what we need and really the power is in the network and it s all about recruiting more folks to come on and take ownership and contribute?
4
SPEAKER_01
We are doing both, actually.
4
SPEAKER_01
So we are growing in terms of staff numbers.
4
SPEAKER_01
We are actually actively hired.
4
SPEAKER_01
You can plug the job openings.
4
SPEAKER_01
Absolutely.
4
SPEAKER_01
Yeah, please.
4
SPEAKER_01
That means we are wrapping up our Series A as well.
4
SPEAKER_01
And we re looking to expand the team out quite a bit to maybe 30 people by the end of the year and hiring all kinds of folks.
4
SPEAKER_02
Cool.
4
SPEAKER_02
Okay.
4
SPEAKER_02
So here s what we ll land on.
4
SPEAKER_02
the singularity, sentient AI, you know, where is this?
4
SPEAKER_02
Are we headed toward, is this inevitable or the compute, you know, I ask all the cliche questions, but you guys are in this and you re in this also in a way that, I mean, I think anybody working in AI and machine learning is thinking about the future, it goes without saying, but there s something about your approach that s maybe even a step, you know, sort of beyond, you know,
4
SPEAKER_02
There s the race for the raw horsepower, to put it in probably not the right terms.
4
SPEAKER_02
But then there s also the thoughts about, well, how are we doing it and how as a, you know, there s a community building aspect that is probably similar to to corporations or companies in some ways, but obviously quite different.
4
SPEAKER_02
Either where do you see this headed or maybe a better question is kind of, you know, what s your goal or what s your kind of utopian end state for, you know, machine learning and AI and how it relates to human beings?
4
SPEAKER_02
Right.
4
SPEAKER_01
So can I lay the foundation down, Jake, and then you can kind of take off of it?
4
SPEAKER_01
So this actually tackles the third problem of our thesis, which is effectively that intelligence itself is not generalized.
4
SPEAKER_01
So the holy grail for any machine learning engineer is artificial intelligence, AGI.
4
SPEAKER_01
So the idea behind it is that
4
SPEAKER_01
What we, you know, what I think everyone is really kind of touching on lately is that it s not really attainable by a single team or a single company.
4
SPEAKER_01
There s just no way.
4
SPEAKER_01
It requires way too much manpower, ingenuity, compute, and time to actually reach.
4
SPEAKER_01
So, you know, you ve got companies like Facebook that are, sorry, Meta, that are coming up with, you know, 16,000 GPU powered supercomputers trying to achieve this, but really they re still dwarfed by things like the computing power of Bitcoin.
4
SPEAKER_01
So the only team that can really, the only way that we can actually come close to achieving something like AGI or something resembling AGI is through all of us working together combined, right?
4
SPEAKER_01
Man didn t get on the moon through the combination of, you know, a small team or like a small company.
4
SPEAKER_01
It was a combination of
4
SPEAKER_01
countless scientists and engineers.
4
SPEAKER_02
It wasn t one guy in a cowboy hat with a rocket.
4
SPEAKER_01
Absolutely not.
4
SPEAKER_02
Okay.
4
SPEAKER_01
And, uh, yeah, so that s kind of, uh, where that s kind of the third thesis, the third bit of our thesis that I want to kind of touch on.
4
SPEAKER_01
And then Jake, I ll leave the rest to you.
4
SPEAKER_00
Yeah.
4
SPEAKER_00
I mean, yeah, exactly.
4
SPEAKER_00
So we re, we re, we re hoping to scale.
4
SPEAKER_00
We believe that, that the problem is actually a lot bigger than people claim, you know, uh, and you know, we re not always is.
4
SPEAKER_00
Yeah.
4
SPEAKER_00
I mean, there s, there s actually this funny thing where it s like, uh, uh, people have thought that AGI was 15 years away since the 1950s.
4
SPEAKER_00
Like they always, they always say it s 15 years away because, because you re dealing with something that s so unknown.
4
SPEAKER_00
So I guess that actually just tells you what the average, you know, unknown amount of timing for people.
4
SPEAKER_00
Um, uh, so, so we, we don t really know if we re going to be able to create that, but we certainly
5
SPEAKER_00
think that creating this community of machines or culture of machines mediated by a market global Bitcoin scale is how we re going to reach the level of compute that was required.
5
SPEAKER_00
And also the timescale.
5
SPEAKER_00
We re training a single network over a very long period of time.
5
SPEAKER_00
And that s actually unique.
5
SPEAKER_00
So we re moving in that dimension.
5
SPEAKER_00
But I mean, the answer is, we don t know if it s going to create AGI.
5
SPEAKER_00
But I think the important thing is, if we do create AGI, the question is, who s going to own it?
5
SPEAKER_00
And I think that s actually way more important than, is it going to launch the nukes?
5
SPEAKER_00
I think it s whether or not AI is going to be controlled by a small group of people.
5
SPEAKER_00
Is it going to be centralized into the hands of those people?
5
SPEAKER_00
And I think that the solution to these problems is to work with tokenization, to build technologies for ownership of AI, where it s aligned with humanity, right?
5
SPEAKER_00
It s just our token for our DAO is distributed into the hands of many people.
5
SPEAKER_00
And there s mechanisms in place to stop that thing from getting out of control and unaligned from humanity.
5
SPEAKER_00
And I think that s maybe the more interesting question and the much more difficult problem that we re working on.
5
SPEAKER_00
We don t have a solution.
5
SPEAKER_00
That s not our first technical challenge, but it s something that we want to draw people in with potentially finding a solution to.
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SPEAKER_01
Excellent.
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I think that s it.
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That s the alignment.
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The alignment of problems, so to speak, is a great place to land here.
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Guys, for people listening or watching, we re a video on YouTube, audio wherever you get your podcasts.
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There s my little plug.
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For folks who want to find out more about BitTensor, about OpenTensor, I know there s a white paper, a blog, job listings.
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Give us some URLs.
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Say them out loud.
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We ll put them in the show notes.
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Yeah, so you can find us at BitTensor.com.
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That s kind of the most straightforward way.
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There s our blog, our network visualizer, and everything is there.
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We also have a Discord channel, and we also have, I think it was a ghost blog setting.
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So yeah, honestly, you ll find all that information on the BitTensor.com website.
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Please feel free to jump on there.
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And we also have a LinkedIn website as well.
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Excellent.
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Well, Ala, Jake, thank you guys so much for taking the leap of faith to be one of the first guests on the show.
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But more importantly, for the conversation, there s a lot in there for folks to unpack.
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And, you know, maybe we can do it again, whether here or on the other channel, and dig in a little bit more to the stuff you guys are doing.
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We d love to.
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Thanks, Don.
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All right, that ll do it for this episode of Metaverse.
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Again, thanks to Jake and Ala from BitTensor for a great conversation.
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Special thanks to Jacqueline from BitTensor for making the conversation possible.
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You can check them out on bittensor.com.
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We ve got a lot
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in the works for you.
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There s so much to explore in the metaverse.
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We re just getting started.
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Did I say metaverse?
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In the metaverse on the Metaverse podcast.
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We re just getting started.
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We ve got episodes lined up covering everything, covering fashion and sneakers, covering cybersecurity and fraud, how to stay safe in the metaverse, covering the science behind emotions.
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How do you detect and process and deal with emotions when you re an avatar in the metaverse?
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There s so much
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to explore.
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I m excited about it.
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Thanks for coming on the journey.
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Make sure to subscribe, leave a rating if you re so inclined, and hit us up.
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Say hi on the socials or via email metaaversepod at gmail.com.
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You can let us know what you think.
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Whatever the case, we appreciate you.
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Thanks for listening.
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Again, my name is Noah Kravitz, and we ll see you next time on Metaverse.
5
you.