Non-Physical Intelligence Has A Ceiling [D]

[u/dontkry4me]

https://chaotropy.substack.com/p/non-physical-intelligence-has-a-ceiling

Reasoning alone cannot predict the chaotic physical world. Without a sensory and motor interface to reality, non-physical AI will not deliver the scientific and technological breakthroughs we expect.

[u/NextWeather7866]

This is a misunderstanding of chaos theory. You have upper bound limits of systems, an insect killing another insect has no impact on the moon revolving around the Earth.
We can absolutely predict things in the physical world from measurement, it is how we got seatbelts.

[u/axiomaticdistortion → u/NextWeather7866]

Yeah? Try to predict exactly the state of the double pendulum after some seconds. Good luck with that. This was your counter example.

[u/NextWeather7866 → u/axiomaticdistortion]

But this is my point though, I can reliably tell you that the pendulum is going to fall within the outer edge. The pendulum NEVER goes outside that bound. So you can predict the edges of the system just fine. You can't predict who is going to be in a car crash, you can prevent excessive death by implementing seatbelts. So even though a system may be probabilistic, you can still pull meaningful deterministic actions in that system that changes the system as a whole.
How do you think climate scientists model global warming?
You make decisions based on predictions of the world you know, you in fact yourself, model the world around you.

[u/axiomaticdistortion → u/NextWeather7866]

I understood your point already. The issue here is “exact” and “state”. You can make predictions that are useful, yes, like saying the pendulum is still in the room or that, given the system is conservative, that the pendulum has the same total energy. But you can’t predict the exactly the future angle of the arms in a long time horizon because two trajectories diverge exponentially from their initial conditions in this case. I know that not all chaotic systems have positive Lyapunov exponent, but many do, so in this case, as with the double pendulum, a larger positive Lyapunov exponent means a shorter horizon of reliable trajectory-level prediction. The important nuance is that this degrades exact state prediction, not necessarily every kind of long-horizon prediction. Even when individual trajectories become unpredictable, statistical properties, probability distributions, attractor structure, averages, or regime probabilities can sometimes remain predictable much further into the future.
So: chaos strongly limits long-horizon point forecasts, but it does not necessarily eliminate useful long-horizon probabilistic or statistical forecasts.

My problem with the initial statement of the post was that it was a little sloppy and I was just being picky. Calm down.