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Which is true. And here you are close to why systems with massive discrete logic (like the bits and operands of digital computers) always have some trouble in messy reality. What holds for that scaling symbolic logic doesn't get you there probably is true to for discrete logic in general. (Just riding a hobby horse 😀).
Which is true. And here you are close to why systems with massive discrete logic (like the bits and operands of digital computers) always have some trouble in messy reality. What holds for that scaling symbolic logic doesn't get you there probably is true to for discrete logic in general. (Just riding a hobby horse 😀).
As I see it, anything a human can model, a machine can model.
If a machine needs higher precision in calculations, that is easy to add. Same if it needs more memory. However, machines beat us in both of these by a very large margin already.
The issue AI is failing so far is the world representations we hold in our heads are outrageously immense and at multiple levels.
We can effortlessly imagine the whole universe, then zoom to an individual galaxy, a star, a planet, an atom, a quark.
We can switch in no time to talking about a science fiction book, historical trends, humor, and how any of these relate to anything in cosmology.
We never lose track of our train of thought as we do any of this. That suggests very clever representations.
Robots can build silicon chips at the level of 3 nanometers. I worked in that industry. The precision of the computer logic for physics work is not the problem.
The problem of AI is being able to operate at multiple levels of abstraction in a massively complex world. An unrelated issue.