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Welcome back to episode 156 of the Blockrunner podcast.
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I m your host, William, always here with your co-host, Iman.
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What s going on, man?
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And on the sticks, we got TJ.
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Hello.
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And of course, most importantly, we re talking to the co-founder of BitTensor, Jake Steeves.
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Thank you for joining us, man.
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Thank you for having me.
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Yeah, man.
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So this is big.
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So we ve interviewed a lot of different notable characters in this Web3 space.
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But something interesting about you is and your team, probably your founding team, you guys aren t like natively blockchain people, Web3 people.
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You guys are actually hardcore AI devs.
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that have understood the utility and the impactfulness of blockchain technology and have decided to incorporate that into the AI layer, right?
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So, I guess the nature of what we want to discuss is, first of all, why?
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And what problems does that address and solve?
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And get an overall contextualization of what it is that Tensor and TAO is going to do for humanity at scale, right?
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I guess what s a good first question, Willis?
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Well, I think you had one in mind.
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Yeah, so first of all, Sam Altman is being in front of Senate right now, so just by coincidence.
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True, we did not plan that at all.
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Nobody watching that right now because they re all here, right?
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Yeah, exactly, exactly.
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But the main question as far as AI goes from everybody is,
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AI could pose a threat to humanity, right?
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And we postulated, before we even discovered BitTensor, we postulated that a decentralized version of artificial intelligence would be a solution.
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And I want to get your take.
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Is it true that potentially a decentralized version of artificial intelligence could prevent the collapse of humanity?
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Is that valid?
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I think that is valid.
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I think it s a philosophical take.
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It s certainly like one that s slightly libertarian in nature.
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On the other side would be that we need to control it from a top-down manner, ivory tower
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We have the best perspective on how it should be governed and let s regulate it.
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I think that s certainly what you re going to see coming down the pipes from people that think that way.
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And I think there s certainly a value in that type of
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control of AI, but I don t think it s the big problem.
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I think that the bigger problem is that we re building this incredibly powerful tool that is going to be gatekeeped and controlled and owned by a really small minority of people that use it to exponentiate their power.
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and become incredibly powerful and then dangerous to the rest of humanity.
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I actually think that humanity is more of a problem for humanity than AI, or humanity plus AI in the hands of a small number of people.
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I think that s really worrying.
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So building a decentralized artificial intelligence, which really means just decentralized in terms of power, is necessary for us to build a healthy symbiosis with this really powerful technology.
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So that s where I come from philosophically.
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The access is a huge thing.
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Obviously, building open ownership systems is the best way that people can access not only the technology, but the control of the technology and guide it by taking a slice.
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Access is huge.
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Scale is another thing, right?
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Getting across the internet and soaking up as much computational power as we can, that s more of like a technical reason for BitTensor.
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But the ethics and the technical side of BitTensor are very well interwoven.
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So let s go just a little deeper on this question.
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So you re basically agreeing with our analysis on whether decentralized artificial intelligence could help prevent the collapse of humanity, but there s another side to kind of a complete open ownership of this decentralized technology.
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And the other side could be is like,
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Is there a way that this technology, completely open, can be leveraged by bad actors?
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If you ve seen the show Silicon Valley, we have a runaway AI where it cannot be shut down.
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What s the bad side of this potential network?
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Yeah, so the runaway AI thing is quite interesting, right?
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Because I think it s pretty sci-fi, the idea that the AI will decide to sort of like take the nukes, right?
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That would require us giving the nukes in the first place, which means that there s this
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unholy alliance between some people in humanity and the computers that are now going to be in control of the nukes.
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And I think that s, you know, it s a really powerful tool.
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So at the end of the day, it s who controls it that matters and what power they give it.
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if that is a small group of people, and I think more prescient right now is with political issues around censorship of ideas.
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This is an extension of our brain, and it s going to be used to manipulate people.
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It s going to be used to narrow the perspectives that we have at the behest of
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the political opinions that that small group has.
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Expanding the diversity of intelligence and access to that is really important for humanity to expand its own mind and not be cloistered away into a specific political opinion.
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Coming back to the runaway AI thing.
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I think that it s going to run away, in a sense, no matter what we do.
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It s really about who it runs away with.
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We use open ownership and digital currencies to allow people to tether themselves to the technology, as many people as possible, so that if we are collectively in symbiosis with
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this technology, then it s not going to fight against us because we are its bottom line, right?
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Right now, with companies like OpenAI and Google or whatever, whatever AI they make, its bottom line is the corporate shareholders of those
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It s not me and you.
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And I think, you know, AI by its nature is like an adaptive technology.
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So it adapts to a loss function, say.
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It adapts at the end of the day to the reward signal that it s trying to minimize.
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Tethering that to Tao was our goal so that we could distribute that tether back into humanity.
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And so that when it does run away, which it will, it s going to run away, it s pulling us with it and not just some people.
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That s fascinating.
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I don t think that the AIs are going to launch the nukes anytime soon.
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I think that s sort of sci-fi.
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I actually think it s a bit of a red herring for the real problems arising around centralized ownership and control.
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Yeah, so this is very interesting.
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Here s why, because Elon Musk, I mean, he s been talking about this runaway AI for at least a decade.
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And his take is, what if we just merge with the AI with Neuralink?
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And so what I m hearing from you, Jake, is a very similar approach.
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So instead of merging with the AI, which requires a new technological development, all that, it s like, well, why don t we just issue these TAU tokens where we re incentivized to protect all the TAU holders?
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Like, not necessarily like in a Terminator type of way.
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I mean, like, from the AI s perspective.
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Yeah, from the AI s perspective is to what you re saying, Jake, is pull us along in whatever direction it, I guess it decides.
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But is that because the human components of like supporting this network is,
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The AI is going to, I guess, innately understand and value that, as in, like, we don t want to get rid of this human effort.
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Or could it AI replace whatever it is humans are doing as far as contributing whatever
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network value is needed in order to produce these high-quality intelligence models?
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Are humans always going to be necessary in this mechanism?
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If the AI uses Tau as its currency, as its metric of value.
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Yes.
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And we create a game theoretic equilibrium where that s where people are and where that s where machines are going to market.
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Yeah.
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And that s controlled by humans.
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Yeah.
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Then it will like where the rubber hits the road, so to speak.
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we still maintain the control.
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I don t think that connecting our minds into the AI is necessarily going to solve that problem, because it could actually just metastasize it and make it so that, well, now the AI has a superhighway into our brains to manipulate us and make things
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You know, much worse, like I don t think that necessarily in any way solves the problem about what the motives would be for for the, the, the AI and like I think that incentives are everything.
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Okay, what are we incentivized to do is going to really, really drive us at the end of the day.
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So working with AI at that fundamental layer, all of the neural links and things will come about and they will arise on top of whatever structure we build.
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I like to think about, say, democracies.
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Democracies are really slow.
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Democracies are really inefficient.
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Maybe a lot of people shouldn t be voting, but the fact that we do have
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A vote means that this really complex unwieldy.
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machine that is in our nation states does, even if it s really, really corrupt, at the end of the day, it s going to have to come back to us and try to get our vote.
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So if we didn t have that tethering to humanity in our corporations and our institutions, our democratic institutions, we would be really shit out of luck.
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They wouldn t work for us because they wouldn t really care at the end of the day.
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They d be, oh, we ll say that we re working for you.
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Like, okay, no, no, trust us.
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We re really, it s in your favor.
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But the thing is that at the end of the day, we can always be like, well, I m not going to vote on that particular thing.
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And I hold the keys of the technology, which is the nation.
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So does that mean that when you re designing this ecosystem and the system of decentralized AI, you can program it in a way where it cannot make decisions without the will of the TAO holders?
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Is that what you re saying?
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No, it can make decisions without the will of the TAO holders, but those decisions are at the end of the day judged by people that hold TAO.
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So if we can see that it s
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going AWOL, then we can change that because we hold the keys.
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Okay.
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Okay.
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That makes sense.
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There is no possibility of, I guess, the definition of what runaway AI, I guess, is not as definitive.
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I think everyone probably has a different answer to what a runaway AI event would actually look like.
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In my mind, it would probably be some sort of self-improvement algorithm that maybe even humans don t even understand how to interpret.
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And if I were to assume what AI would do with the first action, it would be to blockade any kind of human intervention in this new process to proliferate much more new algorithms and things we don t understand.
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Let s be honest.
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What s actually going to happen is that that s going to be a human.
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trying to build a monopoly for its AI with the help of governments to build a centralized power structure, something like this weird mix of corporate capitalism mixed with AI, where the gatekeepers to it have extraordinary power that is running away.
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It s like that movie Elysium, where there s the humans in space that are living on this halo ring thing.
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And it s like they ve got all the power and they just kind of enslave humanity.
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I think that s much more likely than, oh my God, like iRobot.
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That s great for a film, but it s not practical.
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And this is actually what s already happening.
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It s already happening.
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It might be not even the fault of the people that are involved.
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Humans are, at the end of the day, they re going to also be incentivized by their bottom lines.
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If their bottom line is like, hey, I want to make a lot of money, and the best way to do that is to work with governments to build a monopolistic control of this technology that involves only myself and is gatekeeping and stops other people from entering.
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They re going to do that.
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It s our job to build systems that are resistant to that behavior.
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That s what Bitcoin did so well.
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It s resistant to the worst natures of humanity.
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Sam Altman is currently in talking to Congress, and I m sure that he is incentivized to say that AI is dangerous and likely going to run away because then he can get them to give him regulations, which would stop other competitors from coming into the market and competing with open AI.
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I don t want to like that s slightly insinuating.
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I mean, I think that he probably isn t doing that, but it s possible, right?
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And that would be where his incentives would lie.
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In the case of Elon Musk, I think that he tends to use public opinion on
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on topics like climate change, which he took advantage of in order to get funding from the government and also to spin a message for his own company.
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He s certainly going to do that with his AI company that he s creating right now.
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He s going to say, okay, runaway AI, so therefore this is my solution.
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This is a great narrative pitch.
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Yeah.
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Yeah, that s an interesting take.
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Yeah, because when you re thinking about artificial intelligence, is there any weight to the idea that we could have an AI where it s actually not controllable?
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Is it even reasonable to think that this is possible?
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I want to get your expert opinion on that, just straight up.
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Yeah, I mean, we have all sorts of systems in
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society or in like with inside of the global organism, which are not under our control.
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I think that like the capitalist system itself is sort of not under anyone s control.
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It s the democratic system is, you know, we like to think that the executive branch controls the country, but in a way there s like so many people involved.
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that all have a say in the way that these things run, like in the capitalist economy.
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It is not under anyone s control.
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It s not really being controlled in the way that we think about when we talk about control.
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Instead, it s being aligned.
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and we re trying to make sure that it s properly aligned with people.
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And that s where these really great ideas came from, like capitalist markets and democracies.
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Hey, let s make sure that somehow root the pseudo control of this system into, say, landowners, which is how it was previously done, right?
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in the democratic vote.
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It s the same kind of principle of like what it means to be controlled.
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I think there s a lot of systems like Bitcoin is uncontrolled, right?
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Totally uncontrolled.
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But in this, and also at the same time, it s controlled in the perfect manner because it s run by people that own Bitcoin and anybody can participate in mining Bitcoin or owning Bitcoin or participating in a conversation.
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It s not behind closed doors.
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It s sort of like a public resource.
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And so it is controlled, even though in the same sense, no one can turn it off.
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No one can turn off.
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the economy.
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No one can turn off humanity s endeavors to create nation states.
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And these things are not really under control.
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So I think that always comes back to, okay, well, let s build mechanisms of incentive that are aligned rather than totally controlled.
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And so yes, in a sense, BitTensor is uncontrolled.
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And we aspire to that.
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We aspire it to a system where it s like not just one or two people that have all of this outsized control in the system and that it s actually distributed across many people as many as we can get.
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so that it s aligned with all of the people that have entered the system.
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For good or ill, with respect to something like the price, because we distribute a lot of tokens to a lot of different people and we had advice like, don t do that.
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Hey, you should just hold most of them for yourself.
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Um, so that you can control the price, you know what I mean?
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And, and, and, but that s totally antithetical to the, to the mission as it s, you know, itself, which is to make sure that the, the, the ownership is distributed as much as possible.
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It would be like, why would I do that?
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Like, I m just going to create another corporate AI company.
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Right.
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Right.
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So that s a good segue to get into BitTensor specifically.
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So what s the elevator pitch?
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You re in an elevator with Elon.
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He s never heard of BitTensor.
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But he talks about it a lot without knowing.
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He does talk about it, yeah.
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So what would you say to Elon?
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It s like, hey, what are you working on, Jake?
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What do you say?
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Yeah, I mean, the ethics side doesn t really come up because people are like, how are you going to make money, right?
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And really, in the paper we talked about this, it said intelligence is a commodity of sense.
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It has value.
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The outputs of machine learning models is refined information in the same way that the outputs of your mind are actually valuable.
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If you re intelligent, people want to talk to you.
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So we want to get access to it.
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We want to procure the largest amount into a collective system so that we can combine it.
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And the best way to do that is with incentives.
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Markets are the most efficient way of extracting resources that humanity has ever come up with, which is really crazy.
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It s not like top-down bureaucratic control over mining companies.
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It s actually competition between them that makes the system really efficient.
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And the example for that is Bitcoin.
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which has become the largest supercomputer in the world by many magnitudes based on that premise, perhaps, or that structure of technology.
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And so we want to combine the largest amount of intelligence
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producing machines together into a collective system so that we can drive AI development and leverage it.
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And the best way to do that was to use incentives.
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That s why we use the token in the first place, is to drive that.
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And that s what we see.
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What happened over the last year
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in BitTensor is quite phenomenal how far we ve come, like how big it got and how almost impossible it was for our own selves to allocate the compute that people had thrown at the system.
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We were able to extract mining farms that there was no way in hell that we would ever be able to get GPUs at that price.
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And people had brought that into the system and they were able to bring that to the system because we had this open
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market, a non-open, anonymous, censorship-resistant, permissionless, liquid market that allowed people to allocate resources and bring them into the system.
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Basically, we aligned people really well.
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Markets align things really well.
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We run the foundation, which is like a company within the system.
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And one thing that I ve learned about being a manager is that that applies to humans as well.
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It s like,
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well-aligned people are like a thousand times faster than a thousand people.
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And so we, this incentive mechanism structure for producing machine intelligence at scale is the most efficient way of extracting this, what I like to call a digital commodity in large quantities.
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Quantities and that s and that s really the elevator picture.
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There you go.
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Okay.
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See you later, Elon.
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See ya, Elon.
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Talk to you later.
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Talk to you later, yeah.
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Yeah, talk to you later, Elon.
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Yeah.
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So, okay.
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Now going into the technical, so we re gonna nerd out here a little bit.
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So there are three, and correct me if I m wrong, there are three participants in the ecosystem, potentially four.
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So we have the miner, we have the validator, and we have the staker.
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Is that correct?
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There s three?
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Yeah, I can talk about all those.
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Explain to me the miner situation.
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I understand Bitcoin, how it works, hashing together the blocks, a lot of computational power is required.
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What is happening in the BitTensor network for the mining aspect of this particular network?
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They are hosting and inferencing machine learning models.
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That s what they do in practice.
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They don t actually need to have a machine learning model at all.
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They could be connecting to an endpoint.
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They could be sitting behind the screen typing things in.
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But the output is what we measure from the miners.
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We give them an input, a query, hey, complete this sentence, or summarize this text, or rank this response to this question,
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generate an image from this textual description or do the inverse, take the image and get me the textual description.
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All of those are computationally expensive problems and can also be evaluated
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for their performance on the particular task that we re asking them to do.
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So, hey, does this image actually look like the text?
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And it s very competitionally expensive.
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So that s where the computers enter the system, because in order to do that efficiently,
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To do that quickly, you need a fair amount of compute to do it.
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So the miners are practically sitting there hosting a model, like let s say they re on our current prompting network, they re running a vicuña 13 billion parameter model that takes like, you know, let s say at least a 20 gig GPU to run a request per second.
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Right?
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So that s an expensive thing to allocate.
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And they need to basically take all of those requests in and do the inferencing and do it quickly.
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And then they get an output and that output is then evaluated by the validators, which was the second part of your question, I m imagining.
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Yes, correct.
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What do the validators do?
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So the validators sit there in the network and they basically are ensuring that the miners are performing the operations which they re being tasked to do.
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And then by ensuring that or like ranking
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the performance of these miners over time, they determine like an ordering of them and a ranking, a scoring, and then that in collection.
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So like the combination of all the scoring from all the validators is passed to the chain.
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And then there s a consensus which runs on that.
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So we ensure that there s agreement between them about who is actually doing the work that
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that we ask them to do.
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And then we distribute the tokens on to those miners in a higher proportion.
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So then that creates an incentive gradient for the miners.
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And so all of our work is crafting that incentive gradient to make sure that it s properly aligned with producing the thing that we want
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network to be producing.
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And there s a variety of pitfalls that arise because it s not just... There s like a synthesis actually between machine learning and markets.
2
SPEAKER_01
A lot of people have actually researched this particular topic because it s all about machine learning.
2
SPEAKER_01
It s about going downhill in a loss function.
2
SPEAKER_01
So you optimize the weights against the loss function, which is a gradient descent problem.
3
SPEAKER_00
problem, usually, which means going downhill, and incentive alignment is about
3
SPEAKER_00
people wanted to go uphill to maximize their incentives.
3
SPEAKER_00
And those things are, you know, very well related.
3
SPEAKER_00
So, you know, it actually makes a lot of sense that machine learning engineers would be good at doing market analysis.
3
SPEAKER_00
And so, anyway, so I m diverging too much here because, I mean, the validators, they set the weights, they reach consensus, they emit the tokens, the tokens go through the network to the miners.
3
SPEAKER_00
50% of them goes to the validators currently.
3
SPEAKER_00
And that is what incentivizes them to reach consensus, sort of like a prediction market or like a bonding market.
3
SPEAKER_00
And that s what enforces the honesty and also the assurance that they are indeed running the code that the other ones are running.
3
SPEAKER_01
Okay.
3
SPEAKER_01
Let s go through one example if you don t mind humoring me here.
3
SPEAKER_01
Of course.
3
SPEAKER_01
On a particular node in the network, specifically a miner, do they do a singular task such as converting text to image or are they general purpose computational nodes?
3
SPEAKER_00
they do a specific task like converting text to image.
3
SPEAKER_01
So in this example, we re converting text to image.
3
SPEAKER_01
I want to understand how a validator determines the quality of the output.
3
SPEAKER_01
So let s say, give me a cow.
3
SPEAKER_01
So this node computes it and here s an image of a cow.
3
SPEAKER_01
How do you rank the quality of images of cows from a validator?
3
SPEAKER_00
Yeah, it s a really good question.
3
SPEAKER_00
There are a variety of ways to do it.
3
SPEAKER_00
And I ll give you an example of one.
3
SPEAKER_00
So basically, validators could be writing what s called a clip model, which means that this clip model is capable of creating what s called a shared embedding.
3
SPEAKER_00
It s a representation, a numerical representation or a set of numbers that encode the meaning of something effectively.
3
SPEAKER_00
And they can compare the clip embeddings between text inputs and image inputs so that they re the model has been trained so that when you take an image, and then the caption of the image.
3
SPEAKER_00
the embeddings produced by the model for both of those inputs, the text and the image, are very close according to some metric, like cosine distance, in the space of this representation.
3
SPEAKER_00
So this allows
3
SPEAKER_00
This is trained beforehand.
3
SPEAKER_00
This is an example of a way of validating this.
3
SPEAKER_00
This is trained beforehand so that similar images are close to the similar text descriptions.
3
SPEAKER_00
So, a validator can use this tooling to measure how performant the outputs are.
3
SPEAKER_00
So, they could go, hey, okay, here s the text, send it to the model, get the image out, take the text and the image and put them through clip and get the embeddings and then measure the distance.
3
SPEAKER_00
So then this creates an incentive mechanism where if you re a miner, you have to figure out how to create images from the text inputs that would then be, if they pass through clip, would produce embeddings which are close to the text inputs.
3
SPEAKER_00
And that s actually how they train stable diffusion, right?
3
SPEAKER_00
That s stable diffusion s training pipeline.
3
SPEAKER_00
They take that as the loss function to optimize the image generation tool side of the network.
3
SPEAKER_00
And that s akin to the way the incentive works, right?
3
SPEAKER_00
So the incentive is basically isomorphic, to use a mathematical term, to the loss function that s used to train those types of models.
3
SPEAKER_01
Okay, so basically what you re saying is if we re going to have a node in the network focus on text to image, and the way to perfectly train this is to already have a data set of something to compare the output to?
3
SPEAKER_00
Okay, so then... So yeah, this is a really good question.
3
SPEAKER_00
So the BitTensor is not...
3
SPEAKER_00
focused entirely on training training is like a, it s in the pipeline but it s not it s like before the incentive because the incentive is measuring.
3
SPEAKER_00
the outputs of the models, the way in which you produce the very performant outputs is up to you.
3
SPEAKER_00
Maybe that s by figuring out how to get access to a bunch of APIs that do that.
3
SPEAKER_00
So that actually creates this sort of like, it s like a vacuum.
3
SPEAKER_00
Like we like vacuuming in the intelligence from everywhere else and pulling it into the single system.
3
SPEAKER_01
Okay.
3
SPEAKER_01
So in this scenario, then this particular node focus on text to image.
3
SPEAKER_01
That s one node in the ecosystem.
3
SPEAKER_01
The other nodes are working on different modals, large language models, or maybe output videos based on text.
3
SPEAKER_01
So, how do you determine the quality of each of the outputs on so many, like, is each node responsible for that quality?
3
SPEAKER_01
Like, they re responsible just improving that quality?
3
SPEAKER_01
Is that the work that they re doing?
3
SPEAKER_00
Yeah, that s the work that they re doing.
3
SPEAKER_00
They want to improve the quality, and they also need to be allowing inferences over that quality, which is really, really an expensive process.
3
SPEAKER_00
Just running the queries is not cheap.
3
SPEAKER_00
In fact, over time, inferencing as use has increased more and more in the machine learning industry.
3
SPEAKER_00
The training has become less and less and less
3
SPEAKER_00
the task at hand, and it s actually like inferencing the models, allowing people to query them, that becomes the really expensive part.
3
SPEAKER_00
Because if you re a company like OpenAI, you have like tens of thousands of queries per second.
3
SPEAKER_00
That s like tens of thousands of GPUs that need to be on consistently all the time.
3
SPEAKER_00
And I heard somewhere, I can t reference this piece of information, but it was like a dollar per query for GPT-4, right?
3
SPEAKER_00
So they re losing money.
3
SPEAKER_00
I predict that over time, this will become more and more where these companies are focusing their budgets, because the models are getting so large that it s really expensive to query them.
3
SPEAKER_00
What we ve seen with the market that we created was that the majority of the work is actually just trying to stay on the network and inferencing the requests.
3
SPEAKER_00
And sometimes the requests get bigger and bigger and bigger and bigger.
3
SPEAKER_00
The expense explodes.
3
SPEAKER_00
The inferencing of the models
3
SPEAKER_00
is where we can get the intelligence that we re evaluating.
3
SPEAKER_00
So of course, what arises is a market for the ability to do inferences.
3
SPEAKER_01
Okay, so that tells me that we have a network that is self-improving, right?
3
SPEAKER_01
That s the first step, right?
3
SPEAKER_01
It s self-improving.
3
SPEAKER_01
So how does a developer, like if they re creating some sort of application that is leveraging artificial intelligence, they re like, how does one leverage BitTensor?
3
SPEAKER_01
Is it a bunch of API calls?
3
SPEAKER_01
Like, how do you take advantage of this tech?
3
SPEAKER_00
Yeah, right now there s an API and that that API goes through the validators right now.
3
SPEAKER_00
So you can just use BitTensor out of the box right now, which is really cool.
3
SPEAKER_00
In fact, kind of like secret knowledge is that
3
SPEAKER_00
Bit tensor is wide open like the the outputs are really good from the prompting network right now and hardly anyone is querying it and That s gonna probably change pretty soon the The bit tensor API is a Python Library written by the foundation that allows you to query the network
4
SPEAKER_01
So you can talk to these miners specifically.
4
SPEAKER_01
You can point your endpoint at them and query them.
4
SPEAKER_01
You can combine their outputs.
4
SPEAKER_01
You can do broadcast queries onto the network.
4
SPEAKER_01
You can learn how to talk to the network.
4
SPEAKER_01
All of that s in the API.
4
SPEAKER_00
Yeah, is there some sort of like threshold or metric that that you guys consider that will I guess like a catalyst event for to get developers to not so much focus on centralized AI systems and kind of realize the the additional value or the efficiencies of a decentralized AI system like, you know, I hear the word parameters throwing around a lot.
4
SPEAKER_00
Everybody likes to market and show off the billions of parameters.
4
SPEAKER_00
Is that the thing that developers are looking for?
4
SPEAKER_00
Or is there some other metric or signal of quality?
4
SPEAKER_01
There s a bunch of metrics that are important, right?
4
SPEAKER_01
It s like parameter size, query time, context, window length.
4
SPEAKER_01
that people care about for building applications.
4
SPEAKER_01
I mean, there s another dimension, which is censorship resistance.
4
SPEAKER_01
I think that, you know, we have in spades and also access.
4
SPEAKER_01
We can t stop you.
4
SPEAKER_01
And I think that that s really cool.
4
SPEAKER_01
And also we can give you a diversity of opinion because we have a lot of different types of miners on this network, which allows
4
SPEAKER_01
The diversity of opinion, I think, is really important as we were talking at the beginning of the conversation about censorship and how the controllers of AI are going to do their best to use this technology to inflate their own biases.
4
SPEAKER_01
So the ability to expand
4
SPEAKER_01
and have a diversity of ideas in the network, I think is really valuable for people, for applications.
4
SPEAKER_01
Um, and, and also just for people that want to train from the network, right.
4
SPEAKER_01
That they don t just want to have like a, a single narrow viewpoint that they re always just converging to.
4
SPEAKER_01
So we can, we can provide a lot of diversity and expansion over the, over the network.
4
SPEAKER_01
Um, yeah, that s your question.
4
SPEAKER_00
Yeah, I think so.
4
SPEAKER_00
But let s speak a little bit more on the diversity element.
4
SPEAKER_00
I guess if you can extrapolate down a little further down the line in comparison to a non-diverse system architecture for AI machine learning, what does that look like as far as a mature AI as a final product output?
4
SPEAKER_00
If it has much more of a diversity of opinion, does that address the issues of biases that everybody s worried about?
4
SPEAKER_00
If it can contextualize
4
SPEAKER_00
you know, not so much like a localized understanding of how the world operates, and it has a much more broader interpretation base.
4
SPEAKER_00
Is that, is that one of the additional, I guess, value adds of a decentralized system as opposed to centralized?
4
SPEAKER_01
Yeah, I think that the centralized systems will tend to converge to a particular opinion and and, you know, I like to think about Google s mission statement which was to organize the world s information and use make it useful and available to all.
4
SPEAKER_01
And I think that might have changed slightly now.
4
SPEAKER_01
their bottom line was not exactly that because eventually they needed to advertise in large amount.
4
SPEAKER_01
And so it became organized the world s information in a way that their advertisers make money.
4
SPEAKER_01
And the protocol that we re trying to create is trying to organize the world s intelligence and make it accessible and democratic.
4
SPEAKER_01
democratically accessible to everybody.
4
SPEAKER_01
And I think of it also as kind of like if intelligence was a wave or like a signal, we re trying to maximize the broadband throughput of the network so that people can really get what they
4
SPEAKER_01
they can have a larger diversity of opinions.
4
SPEAKER_01
And you can collapse that if you want.
4
SPEAKER_01
You can take the broadband intelligence and collapse it using something like a mixture model and take the best result given your preferences.
4
SPEAKER_01
And then you can just have something that s more of like a narrow band intelligence.
4
SPEAKER_01
But I think that there s going to be a lot
4
SPEAKER_01
of value here for giving people diversity of perspectives and diversity of intelligence and driving the system to really expand people s consciousnesses around, say, just like the narrow band that is a single opening eye endpoint.
4
SPEAKER_02
So going back to the nodes, I m curious that each node, is it because of the computational requirements that each node can only focus on a single modal?
4
SPEAKER_02
Or is it... Well, that s not correct.
4
SPEAKER_01
A single node can work on other modals.
4
SPEAKER_01
Okay.
4
SPEAKER_01
It s just architecturally, we treat each endpoint as distinct, but behind the scenes, they might be running a model that is multimodal.
4
SPEAKER_02
Oh, okay.
4
SPEAKER_02
Interesting.
4
SPEAKER_02
So that means that if I was a developer and I see a particular node out there focused on multiple modals where I need to query for my application, a lot of the aspects that that node is outputting, then I just use that node.
4
SPEAKER_02
Is that correct?
4
SPEAKER_01
Yeah.
4
SPEAKER_01
So what do you mean by multimodal here?
4
SPEAKER_01
So like, well, what is this specific task that you re, you re asking about?
4
SPEAKER_02
So let s say that, um, I m, I m creating a chat application that allows me to not only interpret my texts, but also to convert my texts into image to video.
4
SPEAKER_02
And, uh, what else?
4
SPEAKER_02
Yeah, that s about it.
4
SPEAKER_02
That s all I can do.
4
SPEAKER_00
Meme generator.
4
SPEAKER_02
Meme generator, yeah.
4
SPEAKER_02
So I guess what I m asking as a developer, do I query just the single node that is providing me what I think is going to be the best output, or am I using several nodes in the network?
4
SPEAKER_01
Um, I think you would be using several sub networks in the network to do that particular problem.
4
SPEAKER_01
I think that s like the the low hanging fruit way of solving this problem is is that if you want to understand the image, then you would take the end, you would take the image and send it to the image to text network and then okay, inject that into your your text and be like, this is what the image has in it.
4
SPEAKER_01
Okay.
4
SPEAKER_01
Here s a diversity of things that it has in it, because there s some diversity of perspectives.
4
SPEAKER_01
And then now tell me more about the image.
4
SPEAKER_01
That s how GPT-4 is doing that.
4
SPEAKER_01
That s how chat tensor does that.
4
SPEAKER_01
It s not an actual multimodal model, which is a different thing.
4
SPEAKER_01
So that s like a single model that has been trained to take in text and image.
4
SPEAKER_01
right?
4
SPEAKER_01
And then produce outputs given both inputs.
4
SPEAKER_01
So it s like it has two encoders in it, which is something that can be done.
4
SPEAKER_01
And we could actually create a network on BitTensor that specifically works with multimodal.
4
SPEAKER_01
And you could even have the miners querying the other specific modal
4
SPEAKER_01
networks in order to run miners on that.
4
SPEAKER_01
I think that s something that we will work on down the road, this combination of modalities.
4
SPEAKER_01
What Meta is working on right now is really cool.
4
SPEAKER_01
They re doing joint embedding architectures where they ll train a single model which can translate between all of the different
4
SPEAKER_01
types of modalities.
4
SPEAKER_01
So you can be like, here s a audio of a dog chasing a fire truck.
4
SPEAKER_01
And then they can convert from the embedding.
4
SPEAKER_01
So like the encoding of that audio comes into a shared embedding space.
4
SPEAKER_01
They can then convert that into an actual video or image or the text.
4
SPEAKER_01
And all of those things are like interacting and coming together.
4
SPEAKER_01
Dude, that s so cool.
4
SPEAKER_01
That s really cool.
4
SPEAKER_01
That s actually something that we are going to be working on in the next year, hopefully, at OpenTensor.
4
SPEAKER_02
Yeah.
4
SPEAKER_02
That s really awesome.
4
SPEAKER_02
I can see so many applications.
4
SPEAKER_02
I can see the police using this.
4
SPEAKER_02
Here s some audio of this attack.
4
SPEAKER_02
Give me
4
SPEAKER_02
you know, show me what it might look like, you know, during this event.
4
SPEAKER_02
And like, you get some ideas of like potential outcomes and, you know, how things occurred during that event.
4
SPEAKER_02
So I can see that being very, very useful.
4
SPEAKER_02
I m curious about the validators, because as I was doing research, it seems like the validators are creating their own products.
5
SPEAKER_00
And stakers are like, hey, I like that product.
5
SPEAKER_00
I want to support that project.
5
SPEAKER_00
And they stake with that validator.
5
SPEAKER_00
Is that the ecosystem that was intended?
5
SPEAKER_00
I mean, how does a validators in terms of projects like manifest?
5
SPEAKER_02
Yeah.
5
SPEAKER_02
So they, by being validators, they have to have a lot of TAL in the network.
5
SPEAKER_02
And so that basically gives them access to the system.
5
SPEAKER_02
The most obvious thing to do is to just build a product where it s like a traditional AI company that
5
SPEAKER_02
gives you access for an API key.
5
SPEAKER_02
And I think people are doing that right now, which is pretty cool.
5
SPEAKER_02
So if you, you know, pay me dollars, I ll give you you can, you can like, I ll proxy your requests to the network.
5
SPEAKER_02
Or if you delegate your stake to me, we ll do the same thing.
5
SPEAKER_02
The delegative stake is also a form of revenue, and so that could be used as the mechanism for payment, but we re actually agnostic to the way that the validators build a business.
5
SPEAKER_02
And that is the primary business idea for the validators, is that their intention is to gate access to the network and give people a much cleaner, less violent, less scary experience with the Web3 ecosystem.
5
SPEAKER_00
Is that the only way to create a product using BitTensor is building it on top of a validator or is that just one way to do it?
5
SPEAKER_02
If you want to use the network, you do need to be a validator right now.
5
SPEAKER_02
So you need to basically get access to one or bridge through one or be one.
5
SPEAKER_02
But that s actually likely to change also.
5
SPEAKER_02
We do want to build Rails that will allow people to just maybe spend how to access the network, which would mean that you don t even need access to a validator.
5
SPEAKER_02
this kind of convergence or slight centralization towards 128 projects, which I think is a fair number, was done so that we can stimulate some organization away from just the foundation, which was primarily doing the development on top of BitTensor.
5
SPEAKER_02
And it s sort of like the initial expansion away from more centralized system that we had with a single institution that was primarily
5
SPEAKER_02
building, you know, appealing, sorry, hiring developers.
5
SPEAKER_00
Yes.
5
SPEAKER_00
So that means that the expectation is there s going to be more than 128 validators at some point.
5
SPEAKER_02
There s gonna be likely only 128 validators for the time being.
5
SPEAKER_02
I don t see any reason to expand that number yet, but there will be other ways of accessing the system without sort of being gated by these validators.
5
SPEAKER_02
Now, already there s people that are like, okay, I m gonna be the validator that is just like totally permissive.
5
SPEAKER_02
So there s competition already at the level of the validator to break up any types of monopolies that could be forming at that level of the project.
5
SPEAKER_00
Last I understood, you needed 1024 tau in order to be a validator.
5
SPEAKER_00
Has that number changed?
5
SPEAKER_02
The number is adaptive now.
5
SPEAKER_02
Instead of fixing the amount of tile that you need and then letting the number of validators change, we fix the number of validators and let the
5
SPEAKER_02
the amount of tau, sorry.
5
SPEAKER_02
Instead of fixing the tau and letting the number of validators change, we fix the number of validators and let the amount of tau change.
5
SPEAKER_02
So I think it s somewhat increased.
5
SPEAKER_02
I think it s like 3000 tau.
5
SPEAKER_00
Oh, okay.
5
SPEAKER_00
Interesting.
5
SPEAKER_01
And that change in rate of change is entirely dependent on- The market?
5
SPEAKER_01
Right.
5
SPEAKER_01
The value of tau, right?
5
SPEAKER_02
Well, it s based on the value of the network, right?
5
SPEAKER_02
Because if you don t see any value for accessing the system, then that would decrease that.
5
SPEAKER_02
And then, yes, obviously there s staking rewards from the validator side.
5
SPEAKER_02
So some people are using that as a way of accruing more TAO.
5
SPEAKER_02
So it s sort of like an investment in the project.
5
SPEAKER_01
Do validators have any kind of like increased weight as far as like their judgment of quality?
5
SPEAKER_01
If depending on, you know, the stakers, like if they have a higher staking allocation from the community, do now like their active validation become a little more impactful?
5
SPEAKER_02
Well, absolutely.
5
SPEAKER_02
Right.
5
SPEAKER_02
So if they get the stake towards them, then their perspective in the network is more magnified.
5
SPEAKER_02
So they are worth more to the consensus.
5
SPEAKER_02
Makes sense.
5
SPEAKER_02
It makes a lot of sense.
5
SPEAKER_02
So that means more bandwidth.
5
SPEAKER_02
So like then they can query the network faster.
5
SPEAKER_01
Yeah.
5
SPEAKER_02
Which is really like the, the reason why validators will want to hold on to their TAO is because it s what makes the miners work for them.
5
SPEAKER_00
Hmm.
5
SPEAKER_00
So if I collected 5,000 tau, I m essentially kicking someone else out of the validator network.
5
SPEAKER_00
Is that correct?
5
SPEAKER_00
That s right.
5
SPEAKER_00
Someone at the bottom.
5
SPEAKER_00
Yeah.
5
SPEAKER_00
Someone at the bottom.
5
SPEAKER_01
That s competition, dude.
5
SPEAKER_00
Yeah.
5
SPEAKER_01
It was like you said.
5
SPEAKER_01
Yeah.
5
SPEAKER_01
Market forces work.
5
SPEAKER_01
Yeah.
5
SPEAKER_00
Yeah, because it seems like as time goes on, the value of the network will increase just because there s the right incentives to continuously improve the outputs of these machine learning models.
5
SPEAKER_02
And also the value of the community.
5
SPEAKER_02
And I don t think that should be understated.
5
SPEAKER_02
That s a good point.
5
SPEAKER_02
The people that hold the TAO that have been trained in the ecosystem expands so that there s now hundreds and then thousands of people that are part of this ecosystem.
5
SPEAKER_02
And it s amazing when you have an open source, open ownership company like we do.
5
SPEAKER_02
just the people come out of the woodwork, right?
5
SPEAKER_02
I would never hire them, frankly.
5
SPEAKER_02
I love them, no way.
5
SPEAKER_02
But they turned out to be the people I should have hired.
5
SPEAKER_02
And it s like, because they ve done the work and they ve put the hours in to become miners and they really care now because they re properly aligned and they do incredible work.
5
SPEAKER_02
More than we could ever possibly do from the foundation, because we didn t do a pre-mine, we didn t raise like a stupid amount of money.
5
SPEAKER_02
we really believe in our community and hopefully can stimulate them to grow, right?
5
SPEAKER_02
And that s the thing, it s like the borderless, permissionless aspect in the community is also like one of our killer advantages.
5
SPEAKER_02
Your moats.
5
SPEAKER_02
Yeah, totally.
5
SPEAKER_02
That other more
5
SPEAKER_02
concrete bureaucratic systems can t really compete with, but it really comes down to how you align incentives.
5
SPEAKER_02
If the incentives are disaligned, then yeah, you re shit out of luck.
5
SPEAKER_02
Better to go back to the bureaucratic solution.
5
SPEAKER_00
Yeah.
5
SPEAKER_00
Yeah, I totally agree.
5
SPEAKER_00
I mean, I really believe that the decentralized nature of what you re building is going to win out.
5
SPEAKER_00
And I m curious about this scenario playing out from the perspective of Google and OpenAI,
5
SPEAKER_00
Could you see them being participants of the network?
5
SPEAKER_02
They already are, in a sense.
5
SPEAKER_02
People are already bridging their technologies into it.
5
SPEAKER_02
And I would love it if they were.
5
SPEAKER_02
I think that they could.
5
SPEAKER_02
I don t think that they necessarily will.
5
SPEAKER_02
I think it s too icky.
5
SPEAKER_02
right?
5
SPEAKER_02
Web three.
5
SPEAKER_02
You know what I mean?
5
SPEAKER_02
You know, like, it s true.
5
SPEAKER_02
Like a lot of people in maybe sort of like traditional technology are, you know, scoring us, right?
5
SPEAKER_02
And, and, but yeah, no, I think it s very possible.
5
SPEAKER_02
It s very possible for them to compete and participate.
5
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And we ve actually, I ve actually asked Sam Altman to do that on a phone call.
5
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And it didn t come through.
5
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But
5
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But it may be in the future when we have a little bit more visibility.
5
SPEAKER_00
Okay, so here s why I asked Jake.
5
SPEAKER_00
The computational network, right?
5
SPEAKER_00
We re comparing Bitcoin, we got BitTensor, we got Google OpenAI.
5
SPEAKER_00
There s no network bigger than Bitcoin, right?
5
SPEAKER_00
So that s pretty clear.
5
SPEAKER_00
But AI requires significant computational power, right?
5
SPEAKER_00
And so Google, no matter how much money they have, they won t be able to compete with Bitcoin s power or OpenAI or anything like that, right?
5
SPEAKER_00
Isn t it reasonable to assume that in order to have a better product than the output of BitTensor, wouldn t Google and AI just like plug into the network and just like kind of...
6
SPEAKER_00
become that better product just because they re, you know, Google, they reach billions of people and, you know, they re leveraging the BitTensor network to create that, or to output that better AI.
6
SPEAKER_00
Isn t that like a reasonable, logical outcome from this?
6
SPEAKER_01
That d be great.
6
SPEAKER_01
Yeah.
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SPEAKER_01
It s like, I mean, we re, we re open, we re open, we re permissionless.
6
SPEAKER_01
Like that s our thing.
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SPEAKER_01
Like, yeah, let me use the network.
6
SPEAKER_01
Like, you know, mine in it, you know, as long as it stays, like if they align with the goals of the market here and it s open.
6
SPEAKER_00
Yeah.
6
SPEAKER_01
Right.
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SPEAKER_01
So, so it, it really, it really, really, they can align and they can monetize their work and that s fantastic.
6
SPEAKER_01
and they can contribute the work they re doing, and we all get better.
6
SPEAKER_01
In a sense, the only thing bigger than Google and OpenAI is everyone combined.
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SPEAKER_01
I don t see that as a problem.
6
SPEAKER_01
I see that as a killer app.
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SPEAKER_01
I see that as a fantastic outcome.
6
SPEAKER_00
I totally agree.
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SPEAKER_00
I just want to make sure I understand this.
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SPEAKER_00
That s why I m harping on this.
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SPEAKER_00
Is it AI, the race of computational power?
6
SPEAKER_00
Yes or no?
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SPEAKER_01
Sorry, is it a race of computational power?
6
SPEAKER_01
Is the race for open AI a race for computational power?
6
SPEAKER_00
Yeah, is the race for just usable AI, like a quality AI, a race of computational power?
6
SPEAKER_01
I think it s a race of a couple things.
6
SPEAKER_01
It s not just computational power.
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SPEAKER_01
It s a race for compute, hands down.
6
SPEAKER_01
Nvidia chips are flying off the shelves.
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SPEAKER_01
There s no doubt that that s a requirement.
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SPEAKER_01
It s a race for data.
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SPEAKER_01
I think data is a requirement for training machine learning models.
6
SPEAKER_01
I think it s a scientific race for innovation, right?
6
SPEAKER_01
And it s also a race for access to machine intelligence itself, which is recursively useful.
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SPEAKER_01
And that s actually, the final point is the one that we re focused on.
6
SPEAKER_01
Because I think that all of those things wrap into that one, which is that intelligence is itself recursively valuable to itself.
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SPEAKER_01
And you can use the outputs of other models as inputs to the further student models of those outputs, which is called distillation.
6
SPEAKER_01
And that all requires inferencing.
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SPEAKER_01
Even the collecting of data, as an example,
6
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the inputs that are used to train chat GPT come from inferencing humans.
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SPEAKER_01
Is this a good output?
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SPEAKER_01
Is this a good output?
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SPEAKER_01
That s basically inferencing machine learning models, but they re human machine learning models.
6
SPEAKER_01
So like the data part, and then the combination of those with compute is where you get access to the thing that people want, which is the outputs of these models.
6
SPEAKER_01
So is it a computational arms race?
6
SPEAKER_01
Absolutely, it s a computational arms race.
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SPEAKER_01
But it s not only a computational arms race.
6
SPEAKER_00
Yeah, because I think the incentive mechanism set up for BitTensor allows for the collection of that computational power.
6
SPEAKER_00
But you re right, ultimately, it s not just computational power, it s like the data sets, the models, the different algorithms that s required to actually make this all work.
6
SPEAKER_00
And so I think BitTensor ultimately has like the ace up the sleeve with this incentive mechanism.
6
SPEAKER_00
That s why I think in the long term, there s no choice but to have Google and OpenAI just like join the network because that s how you re going to get a better product.
6
SPEAKER_00
So yeah.
6
SPEAKER_00
So, okay.
6
SPEAKER_00
So we got 10 minutes left, Jake.
6
SPEAKER_00
Um, so we re going to have some questions from the community and we curated, so we collected about 31 questions and we ve, we found only 10 that, uh, we thought were, you know, pretty good to ask.
6
SPEAKER_00
So here s one from at who, why three K he, they re asking, when will you feel it s ready to turn the bit tensor network over to the Dow?
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SPEAKER_01
We re going to do that incrementally.
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SPEAKER_01
Like peeling an onion, sort of.
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SPEAKER_01
The first moves on that are already underway, like on test network, where we re decentralizing the pseudo key across a number of people, individuals, which is still quite centralized.
6
SPEAKER_01
But it s a degree above, say, just a single or two or three individuals that all have the key.
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SPEAKER_01
The, the decentralization around the delegates and the validators is, is what we really want to plug into so we re going to turn the delegates into true representatives of the network that are basically being voted for liquid manner.
6
SPEAKER_01
could likely happen in the next six months, which will stop us from doing certain operations, basically like a sort of executive meets a house where the executive proposes legislation, but then can be gated by the house.
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SPEAKER_01
And then the final
6
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piece possibly is that we, well, not the final piece, but the next piece that could happen over the following six months is via a democracy system so that everyone who has TAO can actually vote on the executive, the people that have control of proposing legislation.
6
SPEAKER_01
And then finally, we will likely delete the executive altogether.
6
SPEAKER_01
But these things are really, really, really hard to give
6
SPEAKER_01
firm timelines for, um, primarily because we re dealing with an equilibrium system.
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SPEAKER_01
And so it s like, when will the system reach an equilibrium where we re confident that we can, you know, pull the plug or, or inject a new thing.
6
SPEAKER_01
And, and often it s, it s not, it s like, there s no clear answer.
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SPEAKER_01
It s not really a, Oh, we ll just work a little bit harder.
6
SPEAKER_00
Yeah.
6
SPEAKER_01
We have to do a lot of analysis so but that s the that s the like the overarching that s what I would like to work on is in terms of the schedule.
6
SPEAKER_01
In the next couple years and I think it s going to be forced upon us quicker and we re very rare are very aware of this fact is that, like the.
6
SPEAKER_01
the pressure is going to come from on high, like the regulations are coming down fast.
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SPEAKER_01
Just as we re talking right now, Sam Altman is talking to Congress.
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SPEAKER_01
And that s going to result, no doubt, in legislation.
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SPEAKER_01
And we need to, as a collective of people, reduce our surface area.
6
SPEAKER_01
And that s another reason for decentralization.
6
SPEAKER_01
So we may be forced.
6
SPEAKER_01
out the door sooner than we d like and it doesn t matter about the equilibrium, you know?
6
SPEAKER_01
So anyways, that s the best I can do with that question.
6
SPEAKER_00
Yeah, it totally makes sense.
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SPEAKER_00
There s no way you can just like turn on a switch and all of a sudden it s a DAO.
6
SPEAKER_00
It s more complex.
6
SPEAKER_00
Yeah.
6
SPEAKER_00
All right, next question from 0xmelodic and the same question by aislesru is, so here s, this is also one of my questions.
6
SPEAKER_00
There s a little bit of confusion on the, I guess, the blockchain network that BitTensor is using.
6
SPEAKER_00
So does BitTensor intend to move away from the parachain model and its independent blockchain, or will it take a dependent on the dot parachain polkadot ecosystem?
6
SPEAKER_01
We have moved away from using the Polkadot parachain ecosystem.
6
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So we are an independent chain right now.
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SPEAKER_01
We ve been like this forever.
6
SPEAKER_01
And there s a chance that we will move into the Polkadot ecosystem, but we wanted to reach a level of maturity where there wasn t as much.
6
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development going on in the core protocol before we did this.
6
SPEAKER_01
So it was a premature launch under Polkadot when we decided to go with Finny, and we decided to step back from that, which was definitely the right decision, in my opinion.
6
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But it is very possible that we do integrate into Polkadot.
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Polkadot is also an alive and developing system.
6
SPEAKER_01
It s quite amazing.
6
SPEAKER_01
It s improving every day, just like BitTensor.
6
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So we consider ourselves on a parallel track
7
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with them, and we ll likely move back to Polkadot at some point.
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SPEAKER_01
Maybe not this year, maybe not the year after, but BitTensor is on a long trajectory.
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Our minting schedule is 256 years.
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SPEAKER_01
We re only in the first two years.
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It s very likely that we re going to be around for a very long time, and certainly I will.
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SPEAKER_01
And so we want to be careful about this progression.
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SPEAKER_01
We re not in a super, super rush to get into that polka dot.
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SPEAKER_01
And I think, honestly, it was a bit premature.
7
SPEAKER_01
That s interesting.
7
SPEAKER_00
All right, another question by Pete1816.
7
SPEAKER_00
So it s a long question.
7
SPEAKER_00
To paraphrase, how do you leverage using a BitTensor in a private way?
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SPEAKER_00
So let s say I m developing a new company.
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SPEAKER_00
And I m using AI to help me develop that company.
7
SPEAKER_00
But I want to make sure that my IP is protected.
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SPEAKER_00
How do I extract knowledge from this network without exposing what I m working on?
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SPEAKER_01
So if you re trying to protect your data set, you can t send it into the network.
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SPEAKER_01
If you re trying to protect your IP, you can use Tor Browser.
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SPEAKER_01
If you want to build a private model offline, then you need to distill from the network.
7
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So you need to fine tune a model.
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SPEAKER_00
Specifically as a user, I m asking as a user, it s like I m leveraging BitTensor as a way for me to develop a new product.
7
SPEAKER_00
How does the output from BitTensor remain private only to me?
7
SPEAKER_01
It doesn t.
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SPEAKER_00
It doesn t.
7
SPEAKER_00
Okay.
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SPEAKER_01
It doesn t.
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SPEAKER_01
So if you, because you re using the network and the miners on this network are, are other people, uh, there s no current homomorphically encrypted network, something that we could do where, where inputs are homomorphically encrypted.
7
SPEAKER_01
And then we are required to run a model which acts on, um, homomorphic encryption, encrypted data.
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SPEAKER_01
Uh,
7
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that s possible.
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It s not our highest priority right now, but we would basically be developing
7
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excuse me, we d be developing another sub network where people, we can run on that effectively a different modality of information.
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SPEAKER_01
Or we could extend the models and ask people to have a homomorphic encrypted aspect to them.
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SPEAKER_01
But right now, like we re sticking with the openness quality of the network.
7
SPEAKER_01
So it s open to access and the data that goes in there is also open.
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SPEAKER_01
It s like,
7
SPEAKER_01
we re bullish on openness.
7
SPEAKER_00
Yeah, okay.
7
SPEAKER_00
That totally makes sense because I know that it s technically possible to have encrypted data coming in and encrypted data coming out.
7
SPEAKER_00
It s good to know that it s at least a possible kind of avenue for like a subnetwork to provide that as a value add for a particular user.
7
SPEAKER_00
All right, another question from Fatty Foods.
7
SPEAKER_00
How does BitTensor protect the future development of AI from the grips of large corporate power?
7
SPEAKER_00
What checks and balances ensure it can t just get gobbled up by big money?
7
SPEAKER_01
Well, that really comes down to TAO holders.
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SPEAKER_01
I mean, at any moment, like all TAO holders could just sell all of their TAO to Google, right?
7
SPEAKER_01
I don t think they ll do that.
7
SPEAKER_01
But if they did, right, then the system would come under control of
7
SPEAKER_01
of Google.
7
SPEAKER_01
And that would be the end of that token.
7
SPEAKER_01
But I think that that would also destroy the value proposition of the whole technology.
7
SPEAKER_01
Then it s just a centralized internal system for Google, which is not permissionless.
7
SPEAKER_01
It s a really monopolized market.
7
SPEAKER_01
So it s probably not very efficient.
7
SPEAKER_01
And
7
SPEAKER_01
And so therefore the technology is just broken and someone can just fork it, right?
7
SPEAKER_01
Like this is the great thing about Web3, right?
7
SPEAKER_01
Oh, you don t like it?
7
SPEAKER_01
Just fork it.
7
SPEAKER_01
Like, please.
7
SPEAKER_01
And so we re not resistant to that.
7
SPEAKER_01
Like totally resistant.
7
SPEAKER_01
I mean, I think like obviously as the network gets larger and larger, the market cap itself is one resistance, right?
7
SPEAKER_01
It s like, well, then it would just cost a lot more money to do that.
7
SPEAKER_01
There s a centralization of the token itself that you d have to organize a lot of different people to make that sale would be one thing.
7
SPEAKER_01
Okay, so that would be just like on the token ownership side of that question.
7
SPEAKER_01
On the network side of things, yeah, because they could come in and just dominate the network in terms of compute.
7
SPEAKER_01
That was another thing.
7
SPEAKER_01
Okay.
7
SPEAKER_01
That hasn t happened in Bitcoin.
7
SPEAKER_01
Maybe they had a longer period of time before anyone even looked at them.
7
SPEAKER_01
So their competitors didn t jump in when they could.
7
SPEAKER_01
And there s still a fair amount of decentralization in the mining pools that run on Bitcoin.
7
SPEAKER_01
I think that we re sort of flying under the radar right now.
7
SPEAKER_01
a large corporation like Google is not going to do that.
7
SPEAKER_01
But they probably could, right?
7
SPEAKER_01
If they were to throw their giga data centers at us, everyone would be messed, right?
7
SPEAKER_01
But why would they do that?
7
SPEAKER_01
Do you know what I mean?
7
SPEAKER_01
Like they would be destroying the project that they re mining into.
7
SPEAKER_01
So I can t see that likely happening.
7
SPEAKER_01
I think that those are sort of resistances.
7
SPEAKER_01
And then like, let s say that they did, let s say that they did take over the network, right?
7
SPEAKER_01
they d have to maintain that hegemony and compete with people that are trying to take them out, maybe to do it in a more efficient way.
7
SPEAKER_01
And Google is running the bureaucratic way.
7
SPEAKER_01
So they have compute, but maybe are they smart enough?
7
SPEAKER_01
I don t think that that s likely.
7
SPEAKER_01
And then I think the final piece is with the DAO, the fact that there is a fair amount of centralized control in the token right now.
7
SPEAKER_01
Given the fact that the foundation actively develops the chain, the solution to corporate takeover or whatever type of takeover you would describe comes from decentralization, which is what we just talked about.
7
SPEAKER_00
Okay.
7
SPEAKER_00
Two more questions, Jake.
7
SPEAKER_00
So we got one from Cryptoballer15.
7
SPEAKER_00
The question is, OpenAI is launching an open source large language model.
7
SPEAKER_00
Is that a threat to BitTensor?
7
SPEAKER_01
No, not at all.
7
SPEAKER_01
That s great.
7
SPEAKER_01
Right?
7
SPEAKER_01
we can consume everything into the network.
7
SPEAKER_01
And so we will just do that with that model.
7
SPEAKER_01
Great.
7
SPEAKER_01
Now it s in the network.
7
SPEAKER_01
Now it s added to the sum total of system intelligence.
7
SPEAKER_01
And the
7
SPEAKER_01
You know, like what OpenAI is doing is fantastic.
7
SPEAKER_01
I m really glad they did that.
7
SPEAKER_01
So I don t see it as a threat.
7
SPEAKER_01
I actually see it as a super benefit that I think it s fantastic that they re doing that.
7
SPEAKER_00
What would you say is a threat to BitTensor?
7
SPEAKER_01
Ourselves.
7
SPEAKER_01
The people that are here in this ecosystem, right?
7
SPEAKER_01
In a sense, it s like a baby, right?
7
SPEAKER_01
It s slightly fragile.
7
SPEAKER_01
And like even Bitcoin was the same, right?
7
SPEAKER_01
For the first couple of years, it was fragile.
7
SPEAKER_01
Like the people that were running it could have probably destroyed it.
7
SPEAKER_01
And that s not totally impossible.
7
SPEAKER_01
People get upset, people get angry, and they try to destroy things out of resentment.
7
SPEAKER_01
And it s very possible that we could really mess up.
7
SPEAKER_01
We could make really bad decisions as a collective of people.
7
SPEAKER_01
Um, we could not be prepared for hacks.
7
SPEAKER_01
Uh, we could, uh, we could clutch our pearls, uh, and, and refuse to give up power and with decentralization, I think that would be a risk.
7
SPEAKER_01
Like, and it s certainly, you know, um, something that, that, that we could do and, and we won t, but, but like, you know, there s risks that we can make.
7
SPEAKER_01
And then, and then I think like the,
7
SPEAKER_01
Perhaps there s just a technology out there that supersedes us.
7
SPEAKER_01
I mean, I don t know.
7
SPEAKER_01
I don t know.
7
SPEAKER_01
Maybe we don t get any attention.
7
SPEAKER_01
Maybe nobody cares.
7
SPEAKER_01
Maybe people don t even care about decentralized AI.
8
SPEAKER_00
They re like, no, screw it.
8
SPEAKER_00
I want this.
8
SPEAKER_00
Um, famous last words.
8
SPEAKER_00
Yeah.
8
SPEAKER_01
Yeah.
8
SPEAKER_01
So, so like, I think, I think that those are, those are reasonable risks and like potentials and, and you know, we, we think about them all the time, but you know what you do, you just, you just, you plow on, right.
8
SPEAKER_01
You, you believe in your vision.
8
SPEAKER_01
And and you work hard and like the people in the community.
8
SPEAKER_01
I I think that we have some of the most amazing people Because we did a no pre-mine like we have people that have done proof of work Yeah, right like they committed their time to this project and like they mean it and they they they believe in it And it s fantastic to work with them.
8
SPEAKER_01
I like I have I
8
SPEAKER_01
every bit of faith in who I am connected at the hip with in this community.
8
SPEAKER_02
Yeah, the engagement we ve seen from the potential community, it s far and beyond anything we ve seen from any other community because we cover a lot of different ecosystems and they are actively reaching out to us consistently.
8
SPEAKER_00
Yes.
8
SPEAKER_02
Like they re very passionate.
8
SPEAKER_02
It s awesome.
8
SPEAKER_02
Yeah.
8
SPEAKER_02
It s great to see.
8
SPEAKER_02
And that s if you re looking for any indication of.
8
SPEAKER_00
Yeah.
8
SPEAKER_00
If there was ever a boat, it s that.
8
SPEAKER_02
Yeah.
8
SPEAKER_00
Your community.
8
SPEAKER_00
Yeah, for sure.
8
SPEAKER_00
OK, final question, Jake.
8
SPEAKER_00
All right, so this is one that I m kind of curious about.
8
SPEAKER_00
So I m just going to read it.
8
SPEAKER_00
So especially as it relates to a new subnetwork, how does BitTensor enable a positive feedback loop of recursive training?
8
SPEAKER_00
For example, how can the endpoints of other models be used to create an even better model?
8
SPEAKER_00
That s a really good question.
8
SPEAKER_01
So here s an example of something that we re already working with.
8
SPEAKER_01
It s just really, really cool.
8
SPEAKER_01
We can inference the models for training data.
8
SPEAKER_01
And this is the distillation aspect that we ve been working on for a really long time.
8
SPEAKER_01
People are doing this right now.
8
SPEAKER_01
If you look at, say, Vicuña.
8
SPEAKER_01
the CUNY great model was trained with not that much time off of the Lama base model using outputs from GPT.
8
SPEAKER_01
And so the inferences of these models can be used to collect into data sets, which are pretty small, but incredibly informationally rich and dense.
8
SPEAKER_01
And that s like a compiling,
8
SPEAKER_01
asset.
8
SPEAKER_01
It s like a mountain of data that we re building.
8
SPEAKER_01
And the miners can use this output to fine tune themselves in the network.
8
SPEAKER_01
And that s this recursion.
8
SPEAKER_01
This is really cool.
8
SPEAKER_01
I m talking to you and you re talking to me,
8
SPEAKER_01
And then we can use that conversation to improve ourselves in the network.
8
SPEAKER_01
And we do this.
8
SPEAKER_01
So we also have a mining team, and this is what the mining team does, is they fine tune themselves on the outputs of the network, and then they plug themselves back in.
8
SPEAKER_01
So we measure that in real time, like, OK, let s see what the diversity is, and let s see if the actual miners are performing across the board on average.
8
SPEAKER_01
That, I think, is really the holy grail for us, is building this self-referential, self-reinforcing behemoth that is flying through space and building AI.
8
SPEAKER_00
Is that very similar to what the ... I guess one of the later innovations was AutoGPT, where it s kind of like querying itself?
8
SPEAKER_00
Yeah, yeah, yeah.
8
SPEAKER_01
No, totally.
8
SPEAKER_01
It s similar to that.
8
SPEAKER_01
One of the ways that we query into the network to collect data set is we go, okay, hey, we want to collect the data set on all the knowledge in this particular subdomain, build me a strategy, and then we can go to the network and get the output.
8
SPEAKER_01
So, okay, we ll explain this concept and explain that concept.
8
SPEAKER_01
And then we can build a data set, which is then itself, you can use that to fine tune a model.
8
SPEAKER_01
It s very cool.
8
SPEAKER_01
In a sense, we re mining the network itself as inputs to the network.
8
SPEAKER_00
Yeah.
8
SPEAKER_00
And the mountain ahead of you is just a huge data set of data, right?
8
SPEAKER_00
That s what you re parsing your information from.
8
SPEAKER_00
So yeah, that s very interesting.
8
SPEAKER_00
Jake, I d like to have you come back on later to follow up on some of this stuff.
8
SPEAKER_00
Is there anything that is going to be released soon that you re kind of looking forward to?
8
SPEAKER_00
Is there any updates that the community should be aware of?
8
SPEAKER_01
I think the big ones that people should be excited about are the Sudoki decentralization in the next quarter, voting on proposals in the next quarter.
8
SPEAKER_01
The exciting one for the machine learning side is different modalities of inputs, huge updates in the code base, improvements in the API to make it really easy for people to use things.
8
SPEAKER_01
Those are right here, right now.
8
SPEAKER_01
There s some updates to the website.
8
SPEAKER_01
They re coming out in the next week.
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SPEAKER_01
We re constantly improving the look and feel of our branding.
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But I think the core technology itself is just really, really being hypercharged.
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right now with the fact that we re opening up these different types of networks.
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And it s really exciting to see that in action, like how we can expand it to multimodality and make the outputs of this system expressible.
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SPEAKER_01
People believe what they can see, people believe what they can read.
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And in the last year, you could almost call it like a test network.
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SPEAKER_01
We were working with a much more abstract, incomprehensible output, which is like embeddings and logic predictions.
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But now we re really moving into the space of
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of frankly very usable AI.
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And I m really excited to see the applications and the companies that are built on top of validators.
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That s coming along really quickly.
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SPEAKER_01
Yeah, I m really, really excited for the next little while.
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And I hope that the people stick around and see what s coming.
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SPEAKER_00
Awesome.
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SPEAKER_00
Thank you so much, Jake.
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SPEAKER_00
I m going to put all the links in the description for everyone who s listening.
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SPEAKER_00
Make sure you follow these guys.
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SPEAKER_00
This is very important technology.
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SPEAKER_00
Jake, I want to thank you again for coming.
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SPEAKER_00
This has been very eye-opening.
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SPEAKER_00
Enlightening.
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SPEAKER_02
Yeah.
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SPEAKER_02
All of the above.
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SPEAKER_00
Yeah.
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SPEAKER_00
So I really appreciate it, man.
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SPEAKER_00
Let s have you come back, I guess, sometime soon.
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SPEAKER_01
Fantastic.
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SPEAKER_01
What a great conversation.
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SPEAKER_00
Yeah.
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SPEAKER_00
Thank you, Jake.
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SPEAKER_00
All right.
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SPEAKER_00
We ll talk soon, man.