We put human neurons into a computer! AI will surely follow
Today’s exciting new frontier in AI is biocomputing! A computer that has actual biological neurons in it. Human neurons. They stress the human bit a whole lot. The main company doing this is Cortical Labs, founded in Australia in 2019. They got a small amount of venture capital funding. Their big…
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Today’s exciting new frontier in AI is biocomputing! A computer that has actual biological neurons in it. Human neurons. They stress the human bit a whole lot. The main company doing this is Cortical Labs, founded in Australia in 2019. They got a small amount of venture capital funding. Their big splash was in 2022, when they made “Dishbrain”, a collection of 800,000 human neurons, and they got it to play the video game Pong. [Neuron] Cortical Labs makes a point of calling their blob of neurons “intelligent” and “sentient.” The headline on the paper talks about this network achieving sentience — an ambitious claim. The neurons were grown afresh from stem cells. They didn’t find them in a jar on a shelf. So that’s nice. Brett Kagan from Cortical Labs explains their approach: [ConnectSci] You know how there’s things that biology, whether it’s a dog or a bee or a human, can do better than your silicon computer? Well, we want to get those capabilities but in a controllable and reproducible way So they’re not doing this by understanding how animals do those things and forming a theory. They’re just throwing neurons at the problem and hoping for magic. Cortical kept at work. In June last year, Cortical released a biocomputer you can buy! The CL-1, which costs $35,000. Can you guess the use case for the CL-1 biocomputer? Three guesses to come up with the worst possible answers: [IEEE Spectrum] AI acceleration, and Bitcoin mining. Several prospects from the music and entertainment industries have also reached out, Cortical has sold a small number of CL-1 boxes, basically so academic researchers can try to come up with a use for it. The neuron blob game of Pong showed Cortical that video games get press. So earlier this year, they got their neural blob to play Doom running on a CL-1! Doom is a great test case when you want to magic up something that looks like a result. You can get performance from pressing buttons at random that looks good at a glance if you stick together short clips. [YouTube] You can describe what the blob is doing in all sorts of future-looking ways: [New Scientist] The neuronal computer chip, which used about a quarter as many neurons as the Pong demonstration, played Doom better than a randomly firing player, but far below the performance of the best human players. However, it learnt much faster than traditional, silicon-based machine learning systems and should be able to improve its performance with newer learning algorithms, says Kagan. It sucks, but it’ll totally do amazing stuff in the future! The Guardian, which these days never sees an AI hype story it doesn’t like, put up the most Guardian headline: “A petri dish of human brain cells is currently playing Doom. Should we be worried?” [Guardian] Cortical Labs is branching out. The National University of Singapore just set up a rack of 20 CL-1s, with 16 million human neurons. Sounds impressive. They don’t have applications, you understand — it’s human neurons, okay? [press release] To be clear, it’s impressive that Cortical Labs even did all of this. I’m a big fan of doing ridiculous and elaborate things with computers for the sake of it. That’s fun! Putting human neurons on a chip is frankly bizarre. And somewhat disturbing. So of course you’d run Doom on it. But back in the practical world, why do you want a computer with a blob of neurons? What are you actually getting out of this that you can’t do without it? I mean an actual defined and specified thing, not just completely unspecified future maybes. I think you’re not supposed to ask that sort of question — just marvel at the potential. To do … something! This is the magical approach to AI, where you come up with a machine that sounds full of potential — a neural net, or a generative model, or actual human brain cells — and you just make it bigger and bigger, and you wait for the magic to happen! Instead of forming a theory. This approach has not generated a lot of magic so far. Labs doing biocomputing get venture funding from time to time. Maybe when the venture guys are absolutely out of ideas, biocomputing will have its day in the sun! Also, Cortical Labs has yet to show the CL-1 running “Bad Apple.” Video — Podcast