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All right.
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Hello, everybody.
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Welcome to AI Sessions with BitTensor.
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My goodness, it s been a while since I ve hosted.
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I feel like it s rare that I even speak in the spaces these days.
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Thanks to BitTensor, I have actually completely been offline anywhere other than the BitTensor Discord for a while.
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Hi, Alla.
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Thank you so much for joining us, for giving us your time.
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How are you?
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Of course, of course.
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Thank you for having me.
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I m doing well.
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Glad to hear that we kind of took you away from everything else.
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Oh, yeah.
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Oh, yeah.
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And not surprisingly, gosh, you know, just reading about BitTensor for the first time ever.
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Actually, very initially, I think I ve told this story a few times.
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I was wondering, is this like a made up story, like in the way of a scam?
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Because it just seems too good that such a thing exists.
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But of course, it exists and has been operating for a year.
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Ala, you know for our audience I just should say that Ala Shaban is one of the two original founders of Bitensor which is a decentralized internet scale neural net that is incentivized via the blockchain.
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So last time, we kind of started by talking about, or me asking you and Jacob, the other co-founder of BitTensor, what a neural net is.
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I would love to get into some AI general topics, but I think that just what BitTensor is kind of deserves
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a bit of attention.
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But before that, I should say that this will be a regular bi-weekly space.
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Actually, as we might touch on later on, everything about BitTensor is entirely transparent.
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Of course, the blockchain is transparent, but also the code is open source.
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And beyond that, the team holds on their Discord a bi-weekly question and answer session where I suppose actually anybody from it could join the Discord and
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attend that and ask any questions.
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And it s, you know, now that I m a few weeks into, you know, participating as a miner of BitTensor, I m really impressed by how open the team is and how transparent it is in every way that you can imagine.
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So, Ala, to start, you know, neural nets have been around since 2012.
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For those of the audience who may not know what a neural net is, I would
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refer you maybe to the prior session we had with Bitensor, and that s in my timeline.
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That being said, neural nets have been around since 2012.
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And really, I should say they are the reason behind the intelligence of any AI that we see out there that even something as simple as facial recognition on your phone to unlock your phone all the way up to more complex things on all of this is really possible because of neural nets.
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So it is the
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I suppose, the standard architecture from which we derive the intelligence that we see from AI as of late, past 2012.
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So, Ala, what is the big deal about BitTensor as a neural net?
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Yeah, absolutely.
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So there s actually a couple of, I just want to make a correction really quick.
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Neural nets, I think a better way to say it, they were popularized back in 2011, 2012.
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The first artificial neural network was actually invented in 1958 by, I think it was a psychologist named Frank Rosenblatt.
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I think it was Frank Rosenblatt, and he called it a perceptron.
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Effectively, back then, they didn t have the compute that we did.
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They didn t have, they had
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what was the equivalent of an abacus today.
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So it was mostly a mathematical model.
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That s all it was.
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And then in 2011, 2012, basically, some really smart folks out of the University of Toronto, and eventually they kind of joined Google as well, and basically the AlexNet, which was a neural network,
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that was properly implemented on a computer and was actually able to kind of do all of the cool things that, you know, it kind of started the revolution of us doing all the cool things that we could do today.
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So at the time it was really good at telling different images apart.
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It was called the AlexNet.
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Now, ever since those days, ever since 2012 and onwards, there s been an explosion in research, right?
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There s been an explosion in funding.
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There s been an explosion in people learning how to create neural networks.
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There s entire Coursera specialization courses on how to build neural networks.
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And it s gotten to the point now today
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Back in 2012, if you wanted to build a neural network with code, it took a lot of code.
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It took a lot of work because there s no implementations.
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Nowadays, you can actually deploy a small one with a few lines of code and you re done because everything s abstracted away and it s very easy.
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Now, one of the things that happened, though, is that obviously they ve been commercialized, obviously they ve been used in R&D in all these massive companies.
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These neural nets ended up becoming massive, right, because they re becoming more intelligent and more interesting, and they re doing much more sophisticated tasks to accomplish
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to accomplish very specific goals for very specific companies.
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So, for example, Google or Facebook or, sorry, I guess Meta or OpenAI or any of these massive companies nowadays, they have such complicated tasks they need to do that they have to use these massive neural networks.
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But what this kind of did is, in addition to innovation, it also starved innovation from the little guy, right?
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So, for example, if I m a, at least in my days when I was a PhD student,
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I could not compete with anything that Google has deployed.
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There s no way, right?
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I don t have the resources.
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I don t have the funding.
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Even as a professor, I won t have the funding unless I m collaborating with them in some way.
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So it s kind of created this gap, right?
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The other pit that s kind of somewhat of an annoyance, well, not an annoyance actually, more of a real big problem in AI, and it s that
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knowledge does not compound, right?
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So all this research being done, and this applies everywhere, but it s especially a little bit embarrassing in AI, because we actually should be better than that.
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It s that
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we re building on top of previous work.
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So if I published a paper today and it was some really interesting neural network, Jacob, for example, my co-founder, could publish something even more interesting next year and he s built on the work that I ve done.
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So it s compounded the knowledge that I ve created and created something interesting.
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However, the network that he built to beat mine, let s say he s utilizing the work that I did, he has to still train from scratch even though my network has already learned a lot of the knowledge that his network needed to know.
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there s no sharing of knowledge, there s no way to build on top of what s already existing in terms of computational knowledge itself.
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So one of the cool things about BitTensor is, and that s kind of where it comes in, is it tries to kind of resolve these problems.
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It actually creates a compounding of knowledge within the network, so you no longer really have to train everything from scratch, the knowledge is already there.
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So what happens is when you deploy any neural network that you have on the BitTensor network,
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it will learn from the other networks who s most useful and who isn t and start communicating with them and kind of take advantage of what they ve already learned to make itself smarter.
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Does that sort of answer your question?
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Yes, absolutely.
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Absolutely.
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Yes.
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You know, so I m sure that, you know, it sounded like the way that you met Jacob was that you somehow met each other and realized that you have been thinking about the same problem for a while.
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And then, of course, you know, since whenever that happened,
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There has been quite a bit of work.
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The reason I m getting into this is because I was about to say that the tensor began operating from ground zero about just one week short of a year ago.
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But of course, quite a bit of work happened before that could happen.
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I m wondering, Aloy, if you can give us a sense of the progress that you have seen in one year of this network being alive.
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Yes.
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And basically, if you try to give us an understanding of how it compares to the rate of development or development of intelligence in, you know, already established paradigms that you touched on, like, you know, academia or industrial development by tech giants.
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Right, right, absolutely.
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So the one caveat I want to put down before I say anything else about the network and its progress is that this is still fairly new.
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So I m not going to say anything like, you know, we ve beaten the development of current existing neural networks because we re not really
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quite there yet.
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But what is interesting about the BitTensor network since it was deployed is what I want to, I think probably our machine learning team might hang me for this one, but what is basically called the synergy between the models, right?
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The way that they interact with each other and the way that they learn from each other has actually evolved quite a bit
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compared to November 3rd last year when we first launched.
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This is of course partially to all the code updates we ve made, partially due to all the hyperparameters changes that we ve made and everything like that, but the interesting thing about it is that they ve gotten much smarter at being able to tell and distinguish who s useful to them and who isn t.
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So one of our resident senior engineers, Joey, he s actually been working, well, he s done this a little while ago now, but it s an interesting result that we found and it s very, very promising.
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He deployed a model that speaks only German.
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It does not speak English, and it only learns in German, and it only understands German.
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What that model did is it actually was able to discern from all the 4,096 different models on the network, was able to find two or three that were actually able to speak and understand German with it, and it was actually able to speak with them and understand from them.
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And what that indicates is that these models, they don t know what s out there, but they re able to discern on the BitTensor network since a year, basically, for the past year, we ve actually been working towards this goal of being able to discern who is useful and who isn t.
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And that s actually one of the main hypotheses of BitTensor, is allowing models to speak for themselves.
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Don t let people speak for the models themselves, because that creates a lot of bias in the way that we pick the models, and it creates a lot of problems with the way that we develop them eventually, right?
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Right, right.
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You know, one classic example that I like to use, and that s because I m coming from academia, is peer reviews, right?
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The amount of times that peer reviews have been marked as fraudulent, or they ve been marked as problematic, or the results were funny.
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One of the most famous problems in AI as well is reproducibility.
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I cannot reproduce a paper that I see.
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And that s why websites like Papers with Code have popped up that will really rate papers that actually deploy their code with the paper as well, so you can actually see the results.
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Now, the other problem with this is that if I went to publish something, let s say I publish some interesting model and I want to publish it in one of the highest, most peer-reviewed conferences in the world, that d be great.
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And then if someone else who s significantly more famous than I am, let s say he s a really interesting, a really powerful AI scientist and is very well known, he publishes exactly the same thing I do.
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More likely than not, even though the reviews are like double-blind, his paper will be picked over mine because he s much more influential, he s much more well-known, and that s kind of a given, and that s just how humans work.
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An interesting thing about BitTensor is that who I am is no longer relevant, right?
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That s kind of part of the beauty of Web3 as well.
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It only matters how your model performs.
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Is your model doing well?
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You ll be rewarded.
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Is your model doing badly?
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You re not going to be rewarded.
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It s that simple.
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Yeah, BitTensor is very impartial.
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I ve thought about this quite a bit, about how BitTensor is made basically to accelerate development of this intelligence.
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very transparent and very impartial and unbiased.
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To make a correction in my question, I think it would be very, very unrealistic to expect BitTensor at this moment to be the world s most intelligent neural net or anything like that.
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But it seems to me that the rate of development of intelligence in BitTensor far outpaces
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you know, what has been developed out there in the way of neural nets.
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Am I correct in that understanding?
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Yeah, absolutely.
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You can actually, I think even a better way to phrase it would be the size, the collective size of the network has exploded compared to what s been out there, right?
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So as a really famous example is GPT-3, right, which is the kind of the state-of-the-art.
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Well, it was state-of-the-art.
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I think GPT-4 is coming up very soon.
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Right.
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$12 million to train GPT-3, and it didn t even train on the entire data set that they gave it.
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It trained for, I think, three quarters of an epoch or something like that.
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And it was an insane amount.
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It was so insane that the company that created it, which was OpenAI, had actually gone from a non-profit to a for-profit to sell a portion of itself to fund its training.
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It s just crazy.
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On BitTensor, if we take the collective size of the entire network, we ve actually surpassed GPT-2, which is their previous iteration, and we re actually close to surpassing GPT-3 already in size.
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And you can actually take advantage of all these models without having to pay millions and millions and millions of dollars.
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Now, again, of course, the caveat goes into being that this is an early project, there s a lot of work to be done in terms of the quality that you re going to get out of the project itself, but that s only a matter of time more than anything else.
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Sure, that s amazing.
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I think the last time we talked, which was maybe about a month ago, we were talking about this and you were saying how it took BitTensor about six months to get to GPT-2 level, which is something that took a lot longer than that.
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And also, just for a bit of an understanding of how big this is,
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When they made GPT-3, it had to be trained again on everything that actually on this, if I m not mistaken, on the same data set that GPT-2 was trained on.
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And what Allah means by retaining that training, that s a huge problem that BitTensor solves is that the network retains any prior learning, whereas currently there s lots of resources being spent on basically
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retraining the next iteration of a model.
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Is that right?
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Yeah, that s exactly right.
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So, futuristically speaking, if, let s say, BitTensor had been deployed and running, say, five years ago, and then OpenAI had trained their GPT model on BitTensor, their next training, A, won t cost as much, and B, it would just be deploying on top of what their previous training has done.
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it would not be starting over, which they re doing for GPT-4, right?
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They ve kind of started all over again and they re making a much larger model and et cetera, et cetera, to kind of get the model weights that they really want out of the system.
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Right, right.
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Ala, Jacob mentioned a while ago about, you know, there are people that like incentivized development of intelligence and people who are not agreeable with it.
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I m wondering, you know, to me, it was an obvious, tremendous advantage, because when I look at, you know, the size of Bitcoin as a computing network, the only reason that it has gotten bigger than, say, any one entity in the world could afford to own, let s say, or to have that large of a compute network is because of
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a popular incentive, basically, you know, it s because of people across the earth having financial motive to join and to contribute to it.
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So I m wondering, you know, is there, to me, it seems like a no-brainer that an incentivized system is what we would need to kind of significantly surpass in time the current capabilities in terms of neural nets that we have.
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have out there that that have been developed typically by by entities by large entities or larger or not so large.
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So what are you.
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Is there a disadvantage with incentivizing intelligence.
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That s a very good question.
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So incentivizing intelligence itself is not, obviously, it s not a silver bullet.
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There s going to be some problems you might run into eventually down the line with what you re doing, right?
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So one of the issues is that because it s open source, you know, and open everything, you might have folks trying to take advantage of the system to build something that s nefarious, right?
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And that could be anything from, you know, an evil model to do evil things or to a model that tries to take advantage of the system and how it runs.
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And that kind of opens it up as a bit of an attack vector.
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Now, obviously, this is a much more difficult problem to actually solve than it is to actually say.
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So building an evil problem to take advantage of the network is very, very difficult to do.
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That s something that we re kind of looking into slowly, but we re not even near that.
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But if somebody wants to deploy something evil on the network itself, and wants to do something bad with it, with that trained network, you can t really do anything about it.
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Because that is a bit of a, it kind of comes with the territory of being in Web3.
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However, having said that, that problem still exists today.
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I can still create an evil model.
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anyways, right?
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And I don t train on the TensorFlow, I could train on somewhere else, it s going to take me longer to do, but it would still exist out there and it would still be an evil network that I ve created.
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So it s kind of a give and go kind of thing.
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The other kind of potential disadvantage with decentralized AI is the fact that, and it s actually something that we had a brief chat with some very, very smart folks about recently, it s that
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Because the models are black boxes, you don t have insight into the architecture that they have in the back, right?
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So for example, let s say, Dr. M, my model is speaking to yours, and you re speaking to mine.
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We don t have insight into what it looks like on the inside, right?
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It s more dependent on your altruistically motivated goals to actually publish what your architecture is.
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So what they do nowadays is you publish a model on Arxiv or on whatever conference that you have, and then you would basically publish architecture for that.
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But what the companies normally do is after they publish this, they would not publish the weights.
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So the weights of the model itself, which is basically, you can think of it as the tuning within the model.
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That is always proprietary.
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That is not for something that somebody releases.
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So the folks who made GPT-3, they didn t publish their weights of how they tuned their GPT-3.
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That they leave up to the rest of the world to kind of deal with.
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Their excuse, I believe, was that it was too powerful to give to humans, and they were afraid of yada yada, which is a great goal, but we ve got to take it with a grain of salt, right?
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Because it s impossible for commercial purposes.
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And so the same thing with BitTensor applies in the sense that because I don t have insight into what your marketable model architecture looks like, I m a little bit stifled scientifically because I can t see what it is.
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However, this doesn t stop you from actually publishing this architecture if you wanted to to the rest of the world.
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Because the architecture itself, it s great and everything, but it s not really as important as the weights of your model.
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So in a sense, it s kind of a give and take problem.
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It s not a silver bullet solution in that sense, if that kind of answers your question.
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Yes, yes, yes.
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No, absolutely.
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Yeah.
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Is this related to Prometheus, or is that completely unrelated?
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No, it s completely unrelated.
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Yeah, Prometheus is actually, it s more of a way to give visibility to the rest of the network, but we still don t know what your model is, right?
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So I ll be able to see, for example, what you re up with looking like, I ll be able to see all kinds of information like, you know, what the model parameter size is, if you pulled it from Hugging Face and so on, but I don t, for example, if I went and created a custom model myself, I didn t pull anything from Hugging Face, I started from scratch, I was really sophisticated,
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I can maybe hide what that model looks like.
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That s not to say that it s anything nefarious.
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The entire network still has access to your outputs.
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We still know what your model will output back to us, because that s part of how the tensor works.
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You have to give us back an output.
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But we just might not necessarily have access to see what the inside of the model looks like.
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Now, Prometheus itself is actually almost like a
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You can think of it in a way as a God s eye kind of tool.
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It s a way for us, us being the users of BitTensor, to see what BitTensor is doing, what the network looks like.
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How many hyperparameters are we sitting on right now?
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We don t really know until we use Prometheus.
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What is the quality of our logits?
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What is the quality of our embeddings coming in and out of the network?
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We still don t have that information.
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So all of that is kind of
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put it into a collective little dashboard for the entire network to play with and look at.
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And it also gives you local information about your own model and your own miner.
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So you can also deploy Prometheus the same way, but you can also just look at your own local statistics that might not be visible to the rest of the network.
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Because, for example, you want to keep it private, or you want to see how your own performance is doing.
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Because at the end of the day, every user is being competitive.
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Right, right.
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Ala, something in my mind is pulling me away, and I just need to state it.
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So, first of all, everybody here and whoever is listening to this, full disclosure, I began mining Bitensor the moment that I really realized what it is, because it seemed very foolish not to, and in fact have kind of
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you know, rearrange my life to to be involved in BitTensor.
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So, you know, actually, I just want to say that if you re in this space and you re hearing about BitTensor, it is kind of in terms of a financial incentive to me at least.
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And, you know, this is not by any means an official BitTensor space.
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So I m just expressing my opinion.
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It is, you know, sort of equivalent to having heard of Bitcoin in about 2012.
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So, you know, I began mining Bitensor because I ve always been a nerd and actually the idea of learning to do something that I didn t know how to do before and that it was a technical thing, this was very appealing to me as just, you know, by personality.
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Um, but, um, uh, you know, I would say that even the opportunity, because I know that, you know, you got, you guys are not really like, you don t have a marketing department.
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You haven t been like focusing on like, um, creating hype by any means.
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In fact, in fact, if anything, it s been now very low key.
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So I just.
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want to say to our audience that if you re hearing about BitTensor, just the opportunity to just invest, even if it s a tiny bit, will likely grow very, very, very far, and that this project is very early and
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SPEAKER_00
And really, when I say you guys don t do any marketing in my mind, I just feel like BigTensor won t need it because I feel like for anyone who remotely has any understanding of just even conceptually of what AI is and how AI works, when they read about BigTensor, it should be very obvious that it should be very mind-blowing that it even exists.
2
SPEAKER_00
I noticed that a lot of the models that I personally have been using to train on BitTensor have been GPT-3 based, so actually one of my questions was going to be is the network at around the stage where at GPT-3 capability linguistically
2
SPEAKER_00
or not, but I think you kind of touched on it, if there s anything further.
2
SPEAKER_01
Yeah, no, I think we re fairly close.
2
SPEAKER_01
It s safe to say that we are getting very close to GPT-3 s linguistic ability, just of the sheer number of networks that exist out there, right?
2
SPEAKER_01
However, to beat it or to achieve state-of-the-art, that s a different story.
2
SPEAKER_01
We re not, I don t think we re quite there yet.
2
SPEAKER_01
This is something that we re, this is our Cortex teams, Cortex teams,
2
SPEAKER_01
uh kind of it s their territory they re the ones who are kind of working on the validation and benchmarking and kind of reaching state-of-the-art and once we do i promise you we ll be telling everybody about it but um at the time being for now uh yes we re entirely gpt3 based and so um collectively in size we ve definitively beaten gpt3 size i think they re at 175 billion um i think
2
SPEAKER_01
For us, conservatively, we re at 200 billion probably, but size does not always equal power, right?
2
SPEAKER_01
In our case, because we re so decentralized, it s a whole other problem to test and to validate this network working as anticipated for us.
2
SPEAKER_01
And that s kind of partially also why we ve capped the amount of miners on the network to be at 4,096, just so we can kind of have some semblance of control and kind of have more of an environment that we can test things on.
2
SPEAKER_00
Right, right, right.
2
SPEAKER_00
For our relatively general audience here, those numbers are referring to the number of parameters.
2
SPEAKER_00
And, you know, the models are sized that way too, so how many parameters they have.
2
SPEAKER_00
And generally speaking, the more parameters, the more intelligent a network would be, or the more intelligent a particular AI model would be.
2
SPEAKER_00
Alla, what do you see stands in the way of BitTensor?
2
SPEAKER_00
Because to me, it s by all indications, all that BitTensor needs is just time and the continued hard work of however many you are there in Canada developing it.
2
SPEAKER_00
I m wondering what stands in its way, what are the major roadblocks?
2
SPEAKER_01
We re actually scattered all over the world.
2
SPEAKER_01
Canada is the base camp, but we have an office in Austin, we have folks, our head of AI is out in South Africa, our infrastructure engineer is out in Spain, so we re all over.
2
SPEAKER_01
Actually, it s a very good question about what stands in the way of the tensor expanding all the way out.
2
SPEAKER_01
To be honest with you, I think the one thing that does is
2
SPEAKER_01
powerful people taking advantage of the network.
2
SPEAKER_01
That s probably the best way to place it.
3
SPEAKER_00
Myself and Collins in the foundation, all of our goals are long-term.
3
SPEAKER_00
We re all after the ultimate goal of decentralizing AI properly, creating this network that is beautifully crafted, that works as we expect and as we want it to, and that is creating this amazing AI.
3
SPEAKER_00
But, for example, if you have some massive entity that comes in, tries to kind of take advantage of the network to kind of
3
SPEAKER_00
Speculate more on the Tau and kind of earn more and yada, yada, yada.
3
SPEAKER_00
It s a very difficult thing to do nowadays because there s so many people on the network now that we re kind of past that point.
3
SPEAKER_00
But somebody acting non-altruistically to the point where they harm the network, that s one possible problem that could stand in the way.
3
SPEAKER_00
And the only people who might actually end up doing that are the people who are actually, you know, they have a lot at stake.
3
SPEAKER_00
So they re people who kind of want to take advantage of the whole thing.
3
SPEAKER_00
Thankfully, we re kind of decentralized past that point for now, so we should be okay, but we still have safeguards in for this.
3
SPEAKER_00
The other bit that could stand in the way is, to be completely frank with you, is us not being able to produce the results that we are trying to produce.
3
SPEAKER_00
Us not being able to show that decentralized AI works the way that we expect it to, or that us not being able to achieve state-of-the-art because of some intrinsic problem that is within the research.
3
SPEAKER_00
However, this is, again, another thing that is, thankfully, as far as we re aware, as of this podcast, is minimized because we ve been validating our research, we ve been validating our work.
3
SPEAKER_00
It s actually a little premature to say, but we ve actually gotten accepted to Neural IPS, one of the bigger conferences in AI.
3
SPEAKER_00
So it s more validation for the work that we re doing.
3
SPEAKER_00
And we re getting a lot of really smart people evaluating our system and looking into it and seeing how we re doing and looking at it with a critical eye.
3
SPEAKER_00
And always, the output is, this makes sense.
3
SPEAKER_00
you know, however, fix A, B, C, which is a given when you have anybody evaluating your work.
3
SPEAKER_00
So thankfully, you know, knock on wood, we re kind of doing all right on that side as well.
3
SPEAKER_00
So really, the problems are more engineering more than anything.
3
SPEAKER_01
Okay, right, right.
3
SPEAKER_01
I just remember having read something about
3
SPEAKER_01
the UID expansion and for the audience, the UIDs, Ala mentioned the 4096 number, that s the number of basically available mining spots and mining in BitTensor is serving the network with an AI model and currently it is in natural language models or text generation models, transformers, that sort of thing.
3
SPEAKER_01
The question I was going to ask is,
3
SPEAKER_01
Wow, actually, that question just escaped me, but that s okay.
3
SPEAKER_01
Let s talk about multimodal models.
3
SPEAKER_01
Will that ever be a possibility on BitTensor?
3
SPEAKER_00
Oh, of course, 100%.
3
SPEAKER_00
That s how you end up with interesting stuff like stable diffusion, right?
3
SPEAKER_00
But we re kind of building the building blocks towards multimodal models at this point.
3
SPEAKER_00
So for the general audience, we re only working on text-based models for now.
3
SPEAKER_00
So any data that is text-based, whether it s in any language, that s kind of what we re working with.
3
SPEAKER_00
We re not doing anything like images, videos, audio, and so on, just because we want to make sure that we can solve the problem correctly and concisely for text.
3
SPEAKER_00
Because that s kind of the easiest medium to start with.
3
SPEAKER_00
Once we ve done that, then it becomes just really a matter of expanding and optimizing what we do towards other modalities like images and audio.
3
SPEAKER_00
Once we ve done, for example, let s say we ve moved into the next bit that we want to move into would be images.
3
SPEAKER_00
So if we solve the problem for images as well, and we get the images working as we expect, and the network that runs the images is running as we expect it to, and it s producing the right output, then it s time to create multimodal models, right?
3
SPEAKER_00
Because what you would end up with before we introduce multimodal models is kind of subnetworks, right?
3
SPEAKER_00
You have a subnetwork or like a branch of the tree that goes
3
SPEAKER_00
towards text, and a branch of the tree that goes towards image.
3
SPEAKER_00
And what we wanted with multimodal models, because they work with two modalities or more, so you can have a model that works with text or with image, or a model that combines the two, you want to have those in the middle.
3
SPEAKER_00
So they ll kind of start bridging those networks together, because they work with both text models and with image models.
3
SPEAKER_00
So they kind of act kind of as intermediaries in the middle, and that s how they get deployed eventually.
3
SPEAKER_00
So it is, to kind of get back to the question, it is definitely on the horizon.
3
SPEAKER_00
This is something that we re working with.
3
SPEAKER_00
But we want to make sure that we solve the problem appropriately for text and for the next modality.
3
SPEAKER_00
And then we ll deploy the multimodal model accordingly to the text and the next modality as well.
3
SPEAKER_01
I hope the audience here is aware of how honest your earlier answer also was.
3
SPEAKER_01
I love when I ask what stands in the way of vTensor.
3
SPEAKER_01
You talked about the possibility that it may not work in the way that it is intended to, or this paradigm may not work, and I really appreciate that because I find that
3
SPEAKER_01
Typically, generally, just across the world, people tend to only tell of the bright side.
3
SPEAKER_01
But you guys don t do this, and I noticed this also in the question and answer sessions.
3
SPEAKER_01
internal to the Bitcoin, I m not Bitcoin, I m sorry, to the BitTensor community on Discord, on those sessions where you guys just say it like it is without any hesitation.
3
SPEAKER_01
And I really appreciate that, honestly.
3
SPEAKER_01
Yeah, go ahead.
3
SPEAKER_00
We re happy to be honest, just to kind of take you up a little bit, you know, we re scientists, we re not salesmen, so we give you the most scientific answer that we can.
3
SPEAKER_01
You know, there has been, do you guys have a marketing department, Ella?
3
SPEAKER_00
We have one person.
3
SPEAKER_00
That s Todd Quillen on the Discord.
3
SPEAKER_00
She s our entire marketing department.
3
SPEAKER_00
I think she s doing a wonderful job.
3
SPEAKER_00
That s amazing.
3
SPEAKER_00
Shout out to you.
3
SPEAKER_00
Well done.
3
SPEAKER_01
Amazing, amazing, very cool.
3
SPEAKER_01
Yeah, you know, it just bewilders me that a few intelligent people wherever across the world have, you know, developed something that can potentially outpace, you know, these billion, multi-billion dollar companies with thousands of resources, you know, thousands of employees, thousands of developers.
3
SPEAKER_01
But it is taking advantage of decentralization and the power of the people and
3
SPEAKER_01
Um, gosh, that is, that is so powerful.
3
SPEAKER_01
You know, the first thing I thought when I, um, uh, when I read about what Bitensor is, uh, was that, uh, you know, this thought came to me.
3
SPEAKER_01
I thought, oh my God, it s like Bitcoin, except all of that compute that is just arbitrarily calculating in order to, uh, produce really a symbol, um, as, as good as that symbol is to people and as, um, uh, profound as that idea of decentralization that it brought is.
3
SPEAKER_01
But it s kind of being wasted, and I thought bTensor is like exactly the same thing, except not only is the compute not being wasted, but it s actually going towards one of the most useful things for humanity.
3
SPEAKER_01
I think people at large are a little bit maybe missing awareness on
3
SPEAKER_01
how much resources are huge companies like Google, OpenAI, Tesla, how much resources these guys are spending to train AI because that is what s going to get them to resolve those really difficult problems to get to the next level in everything it is that they do.
3
SPEAKER_01
And so the implications of there being such a neural net potentially in the future should everything go according to plan, that would be the world s most intelligent, but that it would be
3
SPEAKER_01
contribute.
3
SPEAKER_01
You know it would be it would have been developed from contributions of massive number of people.
3
SPEAKER_01
That is that s just incredible.
3
SPEAKER_01
On the mining side, did the miners compete with each other?
3
SPEAKER_01
And I noticed internally to Btensor s community, there is sometimes a bit of dismay when some people are kind of disappointed that maybe whatever model they were using before is not competitive anymore.
3
SPEAKER_01
And I feel like they kind of missed the point that that s the whole point, is this is the way that you get Btensor to become
3
SPEAKER_01
more intelligent faster is to continuously be trained with better and better models or further and further fine-tuned models.
3
SPEAKER_01
And so like on every side, so even as a minor, it will force me to get better at what I do or I ll be phased out.
3
SPEAKER_01
And of course, there is incentive to take on that learning.
3
SPEAKER_01
So I kind of appreciate that, you know, B-Tensor itself is all about intelligence.
3
SPEAKER_01
But then on other aspects of it, like on serving the models, it s also
3
SPEAKER_01
forcing me to become better at it and ultimately, really, I m sure down the line will produce probably
4
SPEAKER_00
some of the most expert ML engineers in the world, because at some point, I imagine this will get so competitive that you really have to know what you re doing to even be mining it at all.
4
SPEAKER_01
That s a great point.
4
SPEAKER_01
Yeah, that s a great point.
4
SPEAKER_01
I think that it s one of those things that
4
SPEAKER_01
You know, it has all the potential in the world in the future, right?
4
SPEAKER_01
Everything from producing amazing ML engineers that may not be classically trained.
4
SPEAKER_01
So, you know, one of the big revolutions that we saw with YouTube and with, you know, selling the internet is the emergence of these kids that are just amazing programmers, right?
4
SPEAKER_01
The classic Silicon Valley story is that you re a 33-year-old programmer, but your boss is a 17-year-old out of high school, right?
4
SPEAKER_01
Because that s genuinely the thing that happens, because there s so many talented people that don t necessarily need... They re not really, for example, good at school or good at whatever, but they re really good at doing one specific thing.
4
SPEAKER_01
For example, it could be coding.
4
SPEAKER_01
So kind of the same thing applies in BitTensor, right?
4
SPEAKER_01
It might, as you said, it might actually incentivize, it will actually incentivize a lot of people to be competitive, and some of those people might actually achieve peaks that some of us might not really be able to achieve with our tiny brains compared to them.
4
SPEAKER_01
So it s going to be really interesting.
4
SPEAKER_01
The other tidbit that s actually, that I wanted to also highlight
4
SPEAKER_01
about BitTensor, that s kind of one of the bigger differences with Bitcoin, besides producing, you know, besides producing intelligence, is that we have, BitTensor really is a living, breathing network, right?
4
SPEAKER_01
It s not just a monotonous mine at this much hash rate, this is what you ll get out, and then if you keep mining at this hash rate, this is what you re going to get.
4
SPEAKER_01
You know, even it might change over time.
4
SPEAKER_01
It ll change much slower because of the, you know, the halving events and the amount of people that join.
4
SPEAKER_01
BitTensor, because there are so many knobs to it, it s almost like flying a plane.
4
SPEAKER_01
I m not sure if you ve had the experience flying in those little Cessnas, but there s a lot of gears and knobs you have to always have your eye on to keep the plane going at the right rate that you want it to, at the right altitude, at the right pitch, and so on.
4
SPEAKER_01
So it s the same thing with BitTensor.
4
SPEAKER_01
There s a lot of hyperparameter updates.
4
SPEAKER_01
These are all machine learning
4
SPEAKER_01
parameters that we have to tune all the time.
4
SPEAKER_01
Everything from the size of the data set that the miners are pulling in, all the way out to the length of the sentences that they re learning on, all the way out to even just how the incentive mechanism rewards the top miners and the lowest miners and so on.
4
SPEAKER_01
And that s kind of what makes it kind of a more of a living, breathing network, because there s so many things in it that go into engineering the network on the outside as well as the inside.
4
SPEAKER_01
So you re not just competitive with your peers, but you were also rewarded fairly.
4
SPEAKER_01
And that s kind of the harder part that we re kind of working on right now.
4
SPEAKER_00
Right, right.
4
SPEAKER_00
What are upcoming milestones?
4
SPEAKER_00
I mean, I understand that you guys, as the people working on BitTensor, are extremely busy on a daily basis, and there s constant problem solving, I m sure.
4
SPEAKER_00
But I m just wondering, from just a general public perspective, what
4
SPEAKER_00
What are the milestones that are upcoming or that you re working on hitting within whatever time frame?
4
SPEAKER_01
Yeah, there s quite a few.
4
SPEAKER_01
So the publication that we re working on is one of the big milestones that we had for ourselves for this year.
4
SPEAKER_01
Another one is listing on
4
SPEAKER_01
Sorry, joining the Polkadot network really is what we re working on as well.
4
SPEAKER_01
So that means, you know, plugging our blockchain into the Polkadot s blockchains.
4
SPEAKER_01
You know, for those of you who are unaware, so Polkadot is really a massive collective.
4
SPEAKER_01
It s almost a blockchain of blockchains, and it s its own ecosystem.
4
SPEAKER_01
And with that ecosystem, if you become part of it, you get a lot of security because it becomes much harder to attack you.
4
SPEAKER_01
You get a lot of transaction validation as well because you re
4
SPEAKER_01
depending on their validators and not yours.
4
SPEAKER_01
So everything becomes a lot more cohesive.
4
SPEAKER_01
And so that s one of the big things that we re looking into is to join the Polkadot network.
4
SPEAKER_01
And that s one of the big engineering challenges we ve been tackling.
4
SPEAKER_01
This is kind of for this year.
4
SPEAKER_01
It s up to the end of this year is what s remaining for us to achieve.
4
SPEAKER_01
For next year, one of the big ones is potentially hitting and
4
SPEAKER_01
image modality, kind of starting to work on that.
4
SPEAKER_01
Working on the subnetworks, which is the one thing we ve been talking about for a long time, which will enable us to hit the image modality as well as the text modality at the same time, and eventually would enable multimodals.
4
SPEAKER_01
Heck, even multimodal models are also on the milestones for next year if we can achieve it in time.
4
SPEAKER_01
And then finally it s the, you know, we want to hit a point where we want to fail the Howey decentralization test.
4
SPEAKER_01
For those of you unaware, the Howey decentralization test, for example, Bitcoin, fails that test and that s why it s not a security.
4
SPEAKER_01
Same thing for us.
4
SPEAKER_01
We re classified as security today.
4
SPEAKER_01
We want to remain doing this properly and as legal as we can, so we want to fail that test.
4
SPEAKER_01
So right now we actually are not decentralized enough, so we re actually past the Howey test, which is not a good thing.
4
SPEAKER_01
Which is interesting to say, because sometimes passing is what you want, but in this case we re going backwards, so we want to fail that test.
4
SPEAKER_01
And that s kind of the last one we want to hit next year.
4
SPEAKER_00
Right, right.
4
SPEAKER_00
You know, Alaa, I see questions all the time on Discord of people just asking, you know, where can I buy it, or what exchange do I go to to buy it?
4
SPEAKER_00
And I totally understand why they want to buy it, I ve been telling.
4
SPEAKER_00
You know, Alaa, I ve been describing Bitensor to my friends and family personally as the world s very best thing, and really, I ve never been so interested in really wanting someone, like my sister-in-law, who has no technology background, to understand the implications of
4
SPEAKER_00
something like BitTensor succeeding to do what it aims to do in almost a strange way.
4
SPEAKER_00
I m just wondering if you can just touch on the reason why
4
SPEAKER_00
that exchange or like what s why is it that it s not so easy to buy Tau and by that I just mean you cannot go to Coinbase and buy Tau.
4
SPEAKER_01
Yep, absolutely.
4
SPEAKER_01
So actually just to your earlier point, the amount of times I ve shilled BitTensor to my dad is ridiculous.
4
SPEAKER_01
He s gotten to the point where he s like, I don t care anymore.
4
SPEAKER_01
Just start mining for me.
4
SPEAKER_01
I don t even know how to do this.
4
SPEAKER_01
But yeah, so to be more specific about the listing, and I think a lot of people have been asking us about this for a long time, as you know, for folks who are unaware and folks who already know.
4
SPEAKER_01
BitTensor started with a fair launch, right?
4
SPEAKER_01
So basically it was myself, Konst, the foundation, and kind of the collective of the early folks who found BitTensor through GitHub or through whatever means they were looking for.
4
SPEAKER_01
They were the first miners on the network, right?
4
SPEAKER_01
And then the interesting part is that because we did it this way, this actually put us very high up on the tree of potential projects to pursue for entities like the CFTC or the SEC.
4
SPEAKER_01
So the good thing about this is that we have been doing things the right way.
4
SPEAKER_01
We haven t gotten greedy.
4
SPEAKER_01
We haven t done anything silly that has gotten us in legal jeopardy.
4
SPEAKER_01
And we want to keep it that way.
4
SPEAKER_01
So legally speaking, Tau is a security.
4
SPEAKER_01
There s no way around this right now.
4
SPEAKER_01
We re not decentralized enough.
4
SPEAKER_01
And if we go about listing it ourselves, that ll put the entire project into legal limbo.
4
SPEAKER_01
And we don t want to do that because we want to focus on the AI, frankly.
4
SPEAKER_01
We want to focus on building cool stuff.
4
SPEAKER_01
We want to focus on building this network out and making it as powerful as it can be and writing cool scientific papers and publishing them.
4
SPEAKER_01
We don t want to focus on lawsuits and dealing with the SEC and CFTC, which, you know, they will eventually review us for these things anyways.
4
SPEAKER_01
We want to do things the right way.
4
SPEAKER_01
And especially for our users as well, we don t want our coin to be stuck in limbo like a lot of other coins has.
4
SPEAKER_01
So what we re trying to do is we re trying to
4
SPEAKER_01
go through the Polkadot network first, which is a decentralized exchange.
4
SPEAKER_01
We ve been advised that this is actually a more
4
SPEAKER_01
What s the word I m looking for?
4
SPEAKER_01
A more safe route towards decentralization.
4
SPEAKER_01
Because ultimately, it s kind of a tick-and-a-neck problem, right?
4
SPEAKER_01
You want to decentralize, but you cannot decentralize without speculating.
4
SPEAKER_01
Because as soon as we speculate on Tau, then we re in legal trouble.
4
SPEAKER_01
And we don t ever want to speculate on that.
4
SPEAKER_01
We, in fact, only talk about the AI itself.
4
SPEAKER_01
We don t even talk about the blockchain aspect because that is the most important part anyways.
4
SPEAKER_01
Yes.
4
SPEAKER_01
If we go directly to an exchange, we re speculating right off the bat and we re benefiting ourselves right away and we re just making a lot of problems for a lot of people that are just completely unnecessary.
4
SPEAKER_01
So that s why we re taking our time.
4
SPEAKER_01
We re making it very slow.
4
SPEAKER_01
It is rather difficult.
4
SPEAKER_01
I completely agree.
4
SPEAKER_01
And I feel everyone s pain to actually obtain Tau.
4
SPEAKER_01
I get it.
4
SPEAKER_01
But at the same time, this is for the common good, right?
5
SPEAKER_01
a marketing ploy.
5
SPEAKER_01
This is not something we re playing with or making this on purpose.
5
SPEAKER_01
It s just better.
5
SPEAKER_01
Once we go on to the Polkadot network, it should be fairly easy for you to kind of exchange dots for tau.
5
SPEAKER_01
It should be rather easy to do it that way because the dots are actually things that you can purchase very easily because they are actually decentralized and they re actually listed legally on a lot of exchanges.
5
SPEAKER_00
Awesome.
5
SPEAKER_00
Awesome.
5
SPEAKER_00
Actually, of course, you know, I m in complete agreement with the way that you were going about this.
5
SPEAKER_00
And actually, you know, the reason I asked that question is just for some of that to come out.
5
SPEAKER_00
And thank you for explaining this.
5
SPEAKER_01
Oh, absolutely.
5
SPEAKER_00
Thanks for asking.
5
SPEAKER_01
It s good to clarify.
5
SPEAKER_00
Of course, of course, you know, because, you know, the attitude that I see from you guys is quiet.
5
SPEAKER_00
In fact, you could say by the numbers, definitely the opposite of what you typically see in the world of anything having to do with crypto, which is that the founders want to get listed and this and that and, you know, as quickly as possible.
5
SPEAKER_00
Whereas your attitude, and I hear it in both the TGIFT, which is, thank God it s Friday, but on a Thursday, to the Discord community, the attitude I see from you guys is how careful you are around all of this, because I can feel that you re wanting BitTensor to be around for a very long time, and that you don t want something as silly as
5
SPEAKER_00
some mistake in the past become a cause of regulatory problems down the line.
5
SPEAKER_00
And I really actually appreciate that.
5
SPEAKER_00
At the same time, I do understand that that s because I am not an ML developer.
5
SPEAKER_00
I m not an ML expert by any means.
5
SPEAKER_00
But just being slightly aware of the AI space when I read what BitTensor is, it was profoundly moving.
5
SPEAKER_00
And so I totally understand why there are so many people wanting to buy this token.
5
SPEAKER_00
And I wanted the audience to kind of
5
SPEAKER_00
hear that care that you have about making sure that you don t fall into a pitfall that would be regrettable down the line.
5
SPEAKER_00
So amazing things have been done to get here, of course, by this team.
5
SPEAKER_00
Alaa, just tell us a little bit about the team.
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SPEAKER_00
It s a very small team, I have heard.
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I don t know, but I ve heard that you guys are very selective with who you bring in.
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Tell us a little bit about the team.
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Yeah, absolutely.
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We re a very tiny team, actually.
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For the longest time, it was just me and Cons, basically coding on Bitensor 24-7.
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That was a lot of fun for a while, because it s a little sad that he s not here, because he would have told you all kinds of stories about me tearing my hairs out and him just tearing out entire pieces of code and replacing them with something even more interesting.
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But it was really fun.
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And then, so the team itself is... Actually, we ve grown large enough to go into sub-teams as well, but we re about 18 people in total.
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We re fairly scattered around the world.
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Most of the concentration is in Toronto.
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We have folks out in Western Canada, in Vancouver.
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We have folks out in Austin.
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Shout out to Carol who s on the call.
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I think Quack is as well.
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We ve also got folks out in, you know, obviously Jacob is in South America, he s in Peru, and we ve also got, you know, folks out in Europe and ahead of AI, Taco is also in South Africa.
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So we re fairly scattered.
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Teams specifically is almost divided evenly into AI and blockchain teams.
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There s actually two AI teams, there s one blockchain team.
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The AI teams are, you know, we are an AI company first, so our main focus is the AI.
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The blockchain is actually an engineering thing that we re working with right now.
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So because the innovation comes from AI, all of our R&D and all our focus is on the AI part, whereas the blockchain is almost a software development team in itself.
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However, there is some R&D going on in blockchain as well.
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Yeah, we ve also got a very, very tiny marketing department, and that is Todd Cullen.
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He s doing a great job.
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And then there s myself and Const.
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And of course, we ve got our HR department, who s doing a wonderful job with selectivity of the people who bring on board.
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It s just because one of the main reasons is that we re not raising as much money as these massive AI companies.
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I don t want to list names, but I think there was one recently that just got $100 million investment.
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Which is, you know, in terms of AI, all that money goes towards training models, right?
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It goes towards, you know, building the state-of-the-art and stuff like that.
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And then eventually it ll go into either an exit or like selling the company or into going into the NASDAQ or something.
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But we raised very little on purpose, and that s because we are only working with accredited US investors.
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And as a result, we are very selective with the team that we have, because we actually don t want to kind of spend that absurd amounts of money on a giant team that s going to move to a crawl.
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Myself and Cost have both been parts of companies that expanded too fast, too quickly, and have, as a result, created some garbage code, because they hired anybody off the street, and they ended up being not very good developers as a result.
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And so that s why we re kind of very selective with who we bring on.
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Yes, and it s a beautiful thing.
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You know, what I perceive from around BitTensor and from the development team is just how much care goes into it in every aspect of it that I can think of.
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And also for the audience, just because I m sure many people who listen to this will be
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may not even know much about AI or even crypto.
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You touched on this, Alaa, but I really don t think of BitTensor at all as a crypto project or don t put it in the same ranks as what s out there in terms of the
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in terms of the crypto world, and that it is really using one of the supreme advantages that was brought by blockchain, which is just digital trust.
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So it s just using a blockchain to distribute the incentive to its miners and validators.
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And I mean, it is
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I mean, in my mind, at least, and I ve been exploring Web3 and AI space for a while now, it is the most appropriate use case for a blockchain because oftentimes you see many, many, many people out there just creating a blockchain that really may not need to be.
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Whereas in this case, the blockchain enables something to be transparent and to be trustless.
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And that it s just one of the things that has made BitTensor possible.
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Ala, anything else that you want to share about BitTensor or anything really that I haven t asked you?
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You kind of nailed it really on the head in a lot of ways.
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One of the things that I wanted to clarify a little bit about us being an AI company that basically is using a blockchain as a tool is that, and I think that s something that I d love to be able to, there s one sore point that I think in Web3 that I think I d love to have BitTensor hit and solve and kind of get Web3 moving out of the kind of the stint that it s in right now.
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It s that Web3 currently today, and I think that s maybe me going on a rant, so please feel free to interrupt me.
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No, please, please.
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It reminds me a lot of the app stores of iPhones and Androids back in 2006.
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There s not many crazy interesting applications yet.
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And I m not saying that BitTensor is the most interesting one by far.
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There s a lot of really cool projects.
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I m just saying that BitTensor is one of the most competitive ones to hit that source pod.
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There wasn t really a lot of really cool applications that brought app stores to the masses back in 2006.
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The coolest app that I remember back in 2006 that I had, which was like, I think I was still in high school,
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It was the lighter app, if you recall.
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It was like a Zippo, you click it and then you swipe up and it lights up a lighter on your phone.
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And that was it.
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Now you have the insanely cool apps on phones that you can have that really have brought everything to the mainstream.
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Now everything s an app, right?
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Even MVPs are done in apps, right?
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Most minimal viable products are done in apps.
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And I think that crypto right now is kind of, or Web3 actually more specifically, is in that state.
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There needs to be some applications that will bring the masses to Web3.
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Now, everyone in Web3 understands exactly how important Web3 is, and they understand exactly what the promise is, and they understand what the future is going to be in that system.
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But it s difficult to bring a layman into that, right?
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And I think there s a lot of really cool projects, and I really hope a lot of people see BitTensor in the same level as those other projects.
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that are doing the job of bringing BitTensor to the masses.
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Sorry, not BitTensor, bringing Web3 to the masses.
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And that is to really bring a use case that allows everyone in the world to be able to use Web3 in the sense that they re able all to kind of
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benefit from it.
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So one really cool project, as an example, is Helium.
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I love Helium.
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It s such a cool idea and it s such a really cool way to kind of bring Web3 into the masses while solving a really particularly difficult problem.
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And I really hope that one day BitTensor will achieve the same thing, getting to the point where people are able to use BitTensor as an example and say, okay, this project brought, helped bring Web3 to the masses in a lot of ways.
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Yeah, absolutely.
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Gosh,
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There is just really sheer excitement when I think about the future, should everything continue to progress as it has been in a year.
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We re coming up on the one hour and we ll wrap it in the next couple of minutes, but I just want to point out to people
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you know, just some of the things that have been mentioned here like the BitTensor network reaching near or potentially very soon, I m sure if not like by today it would be very soon, surpassing GPT-3 capability linguistically and how big of a deal that is because BitTensor has had about a week short of a year actually since operating and
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And I think I mean, I think it should be obvious to anyone remotely aware of this space, how accelerated the intelligence of this network is in terms of its development.
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And of course, you know, it s being enabled by contributions from a massive number of numbers of people.
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And that s, that s really a beautiful thing.
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Someone like myself,
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who is not one of the foremost, let s say, or is not at all an ML engineer or is not, I don t even have a computer science degree.
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The idea of someone like me contributing in any way to any such systems out there is ridiculous.
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I would need to, I basically would need to be someone who works for Google and specifically on the teams that develop these things.
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Whereas BitTensor is being contributed to by anyone who, I would say at this point, if someone who is technically proficient is able to mine BitTensor.
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And if you are one of those people, I would highly encourage you to begin learning.
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And there is definitely a learning curve there as a complete outsider to the realm of AI and how to train models and whatnot.
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A lot there is not a more worthwhile thing to me, at least personally, to be involved in.
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And as my audience can see here, I used to tweet many times a day.
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I used to host many spaces a day.
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And it seems like I have pretty much stopped doing everything else for BitTensor.
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And I m grateful for it, of course.
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In fact, one of the people in the audience here originally got Const to come up in another space and speak.
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And gosh, I m so grateful for her.
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because without her, shrimp, she s here in the audience.
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Without her, I wouldn t, that connection with cons wouldn t have happened.
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And you guys wouldn t, I wouldn t have interviewed you guys.
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And, and you know, a lot, this is the first time in my life that I am kind of going all in.
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I m like relative to myself in something, but that should down the line, God forbid for any reason should be tensor not work out.
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this is the first time that I would not regret having devoted to it as much as I have.
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I think that, at least to me, is a very powerful thing to say about anything.
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Any last words, Alain, before we close on this?
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I really appreciate you saying that.
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Frankly, the really interesting thing about BitTensor is because we re building on so much knowledge,
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In the off chance that this doesn t work out, we put together a lot of scientific research that s worthy of so much publication.
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And there s still going to be more and more building on the field of giants regardless.
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The one thing I wanted to add is if you are technically proficient, you only really need two steps to join BitTensor.
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One, run a miner.
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Two, go to Coursera and learn how to build neural networks.
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And then you re good to go.
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You can start building your own stuff.
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Yes, it s all open source.
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And gosh, yeah, there s lots of resources out there.
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Absolutely, guys.
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I really can t be more excited about anything.
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In fact, it s rare for me to even see an idea that I personally haven t thought of before or haven t thought of the possibility of it before.
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But BitTensor definitely was one of those, to the point that I really was so impressed that I couldn t believe that it s real.
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At first, it seemed like there is no way that there is such a thing already and that it s operating.
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And of course, it is
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And what a tremendous thing to have done.
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It really started with the two of you, Jacob and Alla.
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Alla, thank you so much for giving us your time.
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We ll be here again two Thursdays from now, same time.
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And if you re interested in learning more, probably, of course, there is the BitTensor.com website.
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It s a very clean website.
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There is a learn section up there on the top right, but that learn section will kind of get you the very basics of installing BitTensor and things like that if you re interested in mining or validating.
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But otherwise, maybe a good place to learn more would also be the Thank God It s Friday Thursday Q&A session that the team holds with their Discord community on the Discord itself.
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You can join it on bittensor.com.
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There is a Discord link right next to that learn button on the site.
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And with those for whom I care about, I suppose people in
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I have actually come to the point of feeling like it is an injustice on my part not to tell them about BitTensor, and I hope that my audience here hears how tremendous this is.
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And I hope to also at some point come back and tweet maybe more than not at all.
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SPEAKER_01
So thank you so much for joining us.
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SPEAKER_00
Of course.
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SPEAKER_00
Thank you.
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Thank you so much for having me.
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And for everyone, the next TGIFT, I think is going to be the next one.
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I think it s the next one.
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Celebration of our one year.
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So it s going to be a very, very special one.
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And that s going to be November 3rd.
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So yes, that is the next one.
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Celebration of our one year.
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We re going to be hosting, again, another AMA.
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It s going to be very special.
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So yeah, please, we d love to see you.
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SPEAKER_00
Thank you so much.
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Also, to join that, you can join that on the Discord, and the Discord is open for anyone to join.
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Thank you so much, everybody.
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We ll speak again.
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And, you know, this is the second session on BitTensor, and I felt like, you know, I feel like just describing BitTensor and what it is deserves more attention than just the one session, so we kind of devoted to that.
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Later on, we ll get into more intriguing things, maybe having to do with possibilities of AI down the line and that sort of thing with Ala and Jacob.
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Ala and Jacob, for the time being, at least this is the plan, they will alternate in this space on a bi-weekly basis.
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So next time we should have Ala, I mean Jacob, or on const one is the handle.
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And thank you so much for joining us.
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We ll be here next time.
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Have a good day, everybody.