Why not string theory? Because enough is enough

This sounds like the scientific equivalent of "overfitting" in machine learning. The point of a theory is not to predict the training set well, but to generalize well to new cases. This is what string theory fails at.

I don't think so.

I wish the Frame Problem[1] got talked about a little more, because it's one of the fundamental challenges for intelligent beings/systems. I think we'd see fewer grandiose claims about AI if people spent a little more time pondering it. But I'm getting ahead of myself.

You can think of the Frame Problem as the problem babies have when they first enter the world. You've got eyes that can look anywhere, ears that are hearing sounds at every frequency, every touch receptor in your body is feeling something... There is data coming in from every pore and it's all noise.

Where to start? You can randomly twitch a muscle, but there are a lot of muscles. The likelihood that anything coherent will happen is very small. You could just pick sensors at random and try to correlate them with each other, and that lets you pick up some regularity, which would be useful for perception, except that regularity is still totally valueless. You can figure out how to see lines moving across a visual field, but there's no way to know if they are good or bad. Or what you might want to do with them. There is a sense in which it doesn't matter how much computing machinery you have, it's impossible to learn to perceive the world without outside help.

That's the frame problem.

Now, if you are a human, you get bootstrapped into the world through social interaction. You have very dumb insect-like circuits in your nervous system that make black dots with white on either side (i.e. another human eye) look very enticing to you. Before you've had any indication about whether these other eyes are a good thing or a bad thing, your muscles will twitch and you will (clumsily) orient all of your sensors (which are just screeching noise at you) towards whatever the other eyeballs around you are orienting towards. There are a handful of buttons on your body that create pleasure (warmth on your skin) and pain (pressure on your skin). That combined with some places to look gets you started. A human being builds from there, but we continue to get a ton of help throughout our whole life.

Machine learning is the same way. You can train a network to pick a dog out of a bunch of pictures of cats, but you have to give it know the difference between dogs and cats first, so you can feed the network a training set that it can learn from. If you just fed the network the pictures, without categorizing them, all it would see would be noise.

Back to physics... You can feed as much data as you want to a network, "deep" or not, and it will learn nothing. You need to be able to coherently structure it first. In chess that's easy: wins and losses. In physics, not so much. What's a win in physics? Explaining a situation that a bunch of other theories can't explain. So you'd have to structure all of the existing theories in physics in some machine-readable way. And then you'd have to somehow generate the space of all possible theories and all possible experiments....

And that still only gets you basically to where the baby was when they only had some pain and pleasure receptors. I.e. you are still totally screwed by the frame problem. Because you have no idea where to start looking through all of those possible experiments and all of those possible theories.

[1] https://en.wikipedia.org/wiki/Frame_problem

The revision in the face of observations relies on them being new observations. You can make an almost infinite number of models that fit existing knowledge; the way you figure out which one to go with is by having the model make testable (falsifiable) predictions, and then test those predictions. The string theory revisions, however, seem to fall out of conflicts between the theory and existing observations (like, say, that the dimensionality of our physical space is 3, or that the cosmological constant is non-positive).

For example, general relativity could make Newtonian mechanics fall out as a low-mass, low-velocity special case, and solved the already-known theoretical problem of the constant speed of light, but there a lot of conceivable models that could solve that. To be accepted, its predictions needed to be tested (starting with gravitational lensing in the 1919, and going through higher-precision tests later).

This is related to the problem of overfitting in machine learning. You can get a system to be very very familiar with your training data, so it can predict the things it has already seen. However, until you have validated it on data it wasn't trained on, you don't know if the model reflects any of the underlying properties of the system it's observing.

> Machine learning can observe and learn patterns that are more complex than humans can grasp.

That is a common misconception. ML cannot do anything beyond our modeling ability because it is designed with it. Deep learning is simply a method to approximate a function with a nonlinear formula. Something that cannot be easily approximated this way may require too much memory and power to be practical.

It is fundamentally similar to how JPEG is not a good fit for storing text: glyphs are hard to approximate with Fourier transforms.

The edge that ML has against humans is not in the learning part, it is in the machine part. Human memory is volatile, while we have grown exceedingly good at making machines retain memory.

That is not a valid argument. You need to provide a reason why the patterns that machine learning grasps are all graspable by humans, or that humans grasp something that machine learning never will. Multilayer neural networks can capture very interesting (from a human perspective) patterns and concepts, but also many others that seem garbage to us (perhaps because we dont grasp their significance).