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The Current Crop of AI Startups is not Prepared for Big Worlds

khurramjaved.com · 757 words · saved by 1 readers

Several companies have bet that a big enough neural network, once trained on a sufficient amount of data, would be able to do everything we want our AI systems to do. This bet makes sense if the world we live in has a finite number of things that are useful to learn. It doesn't make sense if the world is filled with infinitely many things to learn, and achieving different goals requires learning different things. The view that the world is big and filled with infinitely many subtle things that can be learned for achieving goals is the big world hypothesis (Javed & Sutton, 2024). AI startups of today that are aiming to build generally useful AI systems are blind to the big world hypothesis. If the big world hypothesis is true, then these companies will fail unless they either build a strategy to deal with big worlds or decide to settle for commercializing AI systems in narrow domains. The humanoid robotics companies are especially guilty of pretending that the world is simple. These com

Several companies have bet that a big enough neural network, once trained on a sufficient amount of data, would be able to do everything we want our AI systems to do. This bet makes sense if the world we live in has a finite number of things that are useful to learn. It doesn't make sense if the world is filled with infinitely many things to learn, and achieving different goals requires learning different things. The view that the world is big and filled with infinitely many subtle things that can be learned for achieving goals is the big world hypothesis (Javed & Sutton, 2024). AI startups of

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