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If you look at what was invented by Bitcoin originally, it was this concept of digital trust and the ability for computers to be able to reach consensus on something.
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And in the case of Bitcoin, it was about money.
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It was consensus about balances, about payments, about transactions.
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And that allows us to build incentive mechanisms for markets that could not really exist before.
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A decentralized means by which we can incentivize participation and maintenance of the network.
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The Bitcoin network is by far the largest aggregation of computing power in the world.
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and is itself a marketplace for that computing power.
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So with Bitcoin, you have proof of work.
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And proof of work, in a nutshell, involves effectively guessing a random number that fits a particular pattern, right?
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It s a lottery as to who gets to fill the transactions in the next block on the network.
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It s what s called mining.
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So all of the miners around the world, they go about solving this puzzle.
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When they solve it, the rewards go to the miner that solved that, that mined the block.
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And the rest of all of that effort is basically discarded.
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It s wasted.
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So with proof of work, the effort that is gone into everything that s done there is a means to an end, but in and of itself has no value, right?
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SPEAKER_00
This hashing mechanism that all of these computers around the world filling data centers, burning through vast amounts of energy are doing does not have any value in and of itself.
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It s a means to an end.
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But what if there was a way that we could take that mechanism, that incentive, and build a blockchain marketplace where actually all of that work did have value, rather than wasting it, it was actually going towards something useful.
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What if we could apply that same concept of decentralized, incentivized, permissioned network to machine learning?
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Well, that s what BitTensor is.
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BitTensor leverages the same innovation that brought us consensus on things like Bitcoin, but brings it to what we re deeming the most important thing in this fourth industrial age that we re going through, which is machine intelligence, machine knowledge.
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The hash function in Bitcoin, as I said, is a very concrete function.
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It simply finds a pattern to find bytes that match a particular pattern.
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The consensus mechanism of BitTensor has to deal with a far more fuzzy set of outcomes.
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It has to deal with, you know, actually evaluating knowledge.
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So if I ask everybody here in this room to take, say, a paragraph or a page of Shakespeare, for example, and summarize it down to a few sentences.
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Everybody here would come back with probably something slightly different based upon their understanding.
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Now the question being, how do we evaluate which is the best summarization of that data?
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How do we work out which one of those participants should be rewarded and how much they should be rewarded for their efforts?
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And that s what the consensus mechanism in BitTensor does.
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It looks at the decentralized network with each participant s contributing, effectively, intelligence or knowledge to the network.
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And they re rewarded in accordance to how well they are providing that information.
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So, in the same way, if I was to reward people here for how well they could summarize Shakespeare, then hopefully that would incentivize people to maybe go off and learn more about Shakespeare to get a better summary and get a better reward.
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So we re building a massive decentralized neural network that s aimed at better understanding the information in the world around us.
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At the moment, the current state of the industry is that it is dominated by a number of large players.
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Players like Meta, aka Facebook, our hosts here this evening, Microsoft, Google, Uber.
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They re all companies that have dedicated AI teams that are working on these big algorithms.
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But these kind of institutions are doing it in a very siloed way.
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They re seeking the computational power of, say, Bitcoin, but they re doing it in the wrong substrate.
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They re building huge, great big centralized data centers to process this and to run their models on.
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What if instead of solving these machine learning problems with big centralized supercomputers, we leveraged every computer available on the internet?
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That s what Bitcoin showed us, right?
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You know, the computing power of the Bitcoin network is greater than the top 500 supercomputers combined.
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The scale that we could possibly reach for that sort of thing would mean we d have a lot more availability to train much larger data sets.