AI on the blockchain (it actually might just make sense)



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AI on the blockchain (it actually might just make sense)

Ala Shaabana and Jacob Steeves on the benefits of decentralized AI

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Editor’s note: The TDS Podcast is hosted by Jeremie Harris, who is the co-founder of Mercurius, an AI safety startup. Every week, Jeremie chats with researchers and business leaders at the forefront of the field to unpack the most pressing questions around data science, machine learning, and AI.

Two ML researchers with world-class pedigrees who decided to build a company that puts AI on the blockchain. Now to most people — myself included — “AI on the blockchain” sounds like a winning entry in some kind of startup buzzword bingo. But what I discovered talking to Jacob and Ala was that they actually have good reasons to combine those two ingredients together.

At a high level, doing AI on a blockchain allows you to decentralize AI research and reward labs for building better models, and not for publishing papers in flashy journals with often biased reviewers.

And that’s not all — as we’ll see, Ala and Jacob are taking on some of the thorniest current problems in AI with their decentralized approach to machine learning. Everything from the problem of designing robust benchmarks to rewarding good AI research and even the centralization of power in the hands of a few large companies building powerful AI systems — these problems are all in their sights as they build out Bittensor, their AI-on-the-blockchain-startup.

Ala and Jacob joined me to talk about all those things and more on this episode of the TDS podcast. Here were my favourite take-homes from the episode.

  • Bitcoin is in some sense the largest supercomputer in the world. It consists of countless miners, each of which is feverishly generating random strings of characters in the hope that they’ll find just the right sequence to earn the right to add a block to the blockchain — and the financial reward that comes with it. When you zoom out, this all seems like a lot of wasted computational power. Random number generation isn’t exactly a valuable economic activity, and yet hundreds of thousands of GPUs are furiously at it right now, keeping the Bitcoin ecosystem going. Ala and Jacob began to wonder: could we design a new kind of blockchain where we direct all those computations towards something more constructive?
  • That’s where Bittensor comes in. Instead of guessing random strings of characters, miners on Bittensor solve machine learning problems. They compete to generate latent representations of images, sentences, or other useful inputs, and develop a consensus when they begin to converge on similar representations. Miners are then rewarded when their representations are similar to those of others.
  • Bittensor’s network has been expanding rapidly, fast approaching the size of Open AI’s GPT-3 model. And that’s a very conservative estimate, largely because Jacob and Ala can’t know the architectures of the models that Bittensor miners are using: literally all they can see are the models’ outputs, which makes their network intrinsically meritocratic.

You can Ala on Twitter here, or me here.

Chapters:

  • 0:00 Intro
  • 2:40 Ala and Jacob’s backgrounds
  • 4:00 The basics of AI on the blockchain
  • 11:30 Generating human value
  • 17:00 Who sees the benefit? 22:00 Use of GPUs
  • 28:00 Models learning from each other
  • 37:30 The size of the network
  • 45:30 The alignment of these systems
  • 51:00 Buying into a system
  • 54:00 Wrap-up

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