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AI 2027

(ai-2027.com)
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visarga ◴[] No.43583532[source]
The story is entertaining, but it has a big fallacy - progress is not a function of compute or model size alone. This kind of mistake is almost magical thinking. What matters most is the training set.

During the GPT-3 era there was plenty of organic text to scale into, and compute seemed to be the bottleneck. But we quickly exhausted it, and now we try other ideas - synthetic reasoning chains, or just plain synthetic text for example. But you can't do that fully in silico.

What is necessary in order to create new and valuable text is exploration and validation. LLMs can ideate very well, so we are covered on that side. But we can only automate validation in math and code, but not in other fields.

Real world validation thus becomes the bottleneck for progress. The world is jealously guarding its secrets and we need to spend exponentially more effort to pry them away, because the low hanging fruit has been picked long ago.

If I am right, it has implications on the speed of progress. Exponential friction of validation is opposing exponential scaling of compute. The story also says an AI could be created in secret, which is against the validation principle - we validate faster together, nobody can secretly outvalidate humanity. It's like blockchain, we depend on everyone else.

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1. the8472 ◴[] No.43586239[source]
Many tasks are amenable to simulation training and synthetic data. Math proofs, virtual game environments, programming.

And we haven't run out of all data. High-quality text data may be exhausted, but we have many many life-years worth of video. Being able to predict visual imagery means building a physical world model. Combine this passive observation with active experimentation in simulated and real environments and you get millions of hours of navigating and steering a causal world. Deepmind has been hooking up their models to real robots to let them actively explore and generate interesting training data for a long time. There's more to DL than LLMs.

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2. visarga ◴[] No.43592981[source]
This is true, a lot of progress can still happen based on simulation and synthetic data. But I am considering the long term game. In the long term we can't substitute simulation to reality. We can't even predict if a 3-body system will eventually eject an object, or if a piece of code will halt for all possible inputs. Physical systems implementing Turing machines are undecidable. Even fluid flows. The core problem is that recursive processes create an knowledge gap, and we can't cross that gap unless we walk the full recursion, there is no way to predict the outcome from outside. The real world is such an undecidable recursive process. AI can still make progress, but not at exponentially speed decoupled from the real world and not in isolation.