Why not string theory? Because enough is enough • Hacker News Board

Re: Why not string theory? Because enough is enough

#51

Post by sgt101 » Sun, Jun 12, 2016, 9:22 AM UTC

visarga wrote: post #39

sidek wrote: post #34

Earlier quoted context omitted.

So, how many bits are you giving the deep learning machine? If you give it enough bits, you aren't predicting anything -- you're just feeding it experimental data which it more or less spits back out. For an elegant solution, one would need to have deep learning that also optimises on a constrained set of bits. I feel like in this scenario, humans perform much better. The main strength of computer learning is being a…

Enough bits to make it efficient at predicting. When it predicts better than the current theory, it starts getting closer to having enough bits. That's the beauty of ML, you don't need to worry about these details if it gives good accuracy. My intuition was that there could be different ways to explain the laws of Physics that don't look like the current ones which evolved based on human intuition, math and language…

>That's the beauty of ML, you don't need to worry about these details if it gives good accuracy.

I get very alarmed by this. At work we have several examples of ML systems that have done good things for many years before suddenly and inexplicably blowing up and producing nonsense. Our folk explanation (as we have failed to produce anything resembling a proper one) is that the models that are captured in some cases appear to replicate reality but are deficient of some fundamental part of it which later comes into play destroying there predictive power. The domain theory changes in a sense, in another sense the driver was there all along but just hadn't featured in this part of the regime.

Re: Why not string theory? Because enough is enough

#52

Post by 4ad » Sun, Jun 12, 2016, 9:24 AM UTC

tamana wrote: post #21

Steuard wrote: post #8

Speaking as a professor specializing in string theory, I'd say, "More power to her." I have no idea whether string theory gets too much attention relative to its actual value in modeling the real world, but I think one essential part of finding the right amount of attention for any physical theory is for theorists to make their best judgement about what's worth their time to study. In fact, it's quite comforting to m…

So you feel strong theory connects to reality, and is falsifiable and avoids "piling on epicycles"? Will string theory be more parsimonious than the the more concrete model of physics it tries to model?

Of course string theory is falsifiable. String theory reduces to quantum field theory in some limit and to general relativity in some other limit. Unlike all the other previous theories, string theory is the theory that has resisted all efforts to falsify it yet.

It's a ridiculous misconception floating around that string theory can't make predictions, when in fact string theory is the only theory we have so far that predicts all known observed phenomena.

Re: Why not string theory? Because enough is enough

#53

Post by espadrine » Sun, Jun 12, 2016, 9:31 AM UTC

return0 wrote: post #50

espadrine wrote: post #48

Earlier quoted context omitted.

> 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 fundamental…

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).

Give me a ML system, and I can give you a problem it cannot solve. I am guaranteed success thanks to the No Free Lunch theorem: https://en.wikipedia.org/wiki/No_free_lunch_theorem.

In the case of deep learning, I can point to the task of determining values above 0.5 on an infinite Perlin-noise-derived 2D space fed by Mersenne Twister with seed 0, with an infinite number of octaves. Deep learning does not deal well with infinite spaces to begin with, and the pseudo-random generator cannot be easily encoded with common neural network nonlinear functions.

On the other hand, while we cannot compute an infinite number of octaves, and while places extremely far or extremely small details will run into IEEE754 limitations, we will get a good approximation by writing the program that computes the texture.

And that is just with only two numbers as input and one number as output.

Re: Why not string theory? Because enough is enough

#54

Post by ggggtez » Sun, Jun 12, 2016, 9:43 AM UTC

sgt101 wrote: post #51

visarga wrote: post #39

Earlier quoted context omitted.

Enough bits to make it efficient at predicting. When it predicts better than the current theory, it starts getting closer to having enough bits. That's the beauty of ML, you don't need to worry about these details if it gives good accuracy. My intuition was that there could be different ways to explain the laws of Physics that don't look like the current ones which evolved based on human intuition, math and language…

>That's the beauty of ML, you don't need to worry about these details if it gives good accuracy. I get very alarmed by this. At work we have several examples of ML systems that have done good things for many years before suddenly and inexplicably blowing up and producing nonsense. Our folk explanation (as we have failed to produce anything resembling a proper one) is that the models that are captured in some cases ap…

In ML, they might say you were overfitting. Predicting is all well and good, but predicting too well can indicate the machine hasn't really learned anything. It's just spitting back nearly identical information as the original.

It sounds like that is what this black box is supposed to do. All you need for a perfect black box is every possible data point...

Re: Why not string theory? Because enough is enough

#55

Post by cygx » Sun, Jun 12, 2016, 9:47 AM UTC

4ad wrote: post #52

tamana wrote: post #21

Earlier quoted context omitted.

So you feel strong theory connects to reality, and is falsifiable and avoids "piling on epicycles"? Will string theory be more parsimonious than the the more concrete model of physics it tries to model?

Of course string theory is falsifiable. String theory reduces to quantum field theory in some limit and to general relativity in some other limit. Unlike all the other previous theories, string theory is the theory that has resisted all efforts to falsify it yet. It's a ridiculous misconception floating around that string theory can't make predictions, when in fact string theory is the only theory we have so far that…

String theory reduces to quantum field theory in some limit and to general relativity in some other limit.

That's nice and clearly shows why string theory research is a worthwhile endeavor, but you only get a gold star once you make an experimentally verified new prediction. There is a reason why people were excited about the discovery of the Higgs boson.

Re: Why not string theory? Because enough is enough

#56

Post by ggggtez » Sun, Jun 12, 2016, 9:47 AM UTC

seanmcdirmid wrote: post #37

visarga wrote: post #30

This is probably a naive question: can we input a bunch of particle interactions into a deep learning system and train it to predict the probability of future interactions? It would be like a "black box version" of physics. If we can predict, then we can find a more elegant mathematical notation and a more intuitive physical interpretation. Machine learning can observe and learn patterns that are more complex than hu…

The idea that the universe runs on a simple program with simple rules hasn't failed us so far. All we have to do is figure out what that simple program is with our capability for abstract symbolic thinking and reasoning; machines are presently very bad at this while humans are less bad at it.

Look up automated proofs. This exists. It's largely useless, due to mathematical properties of logic. Every mathematical system has things which are true but can't be discovered from first principals.

Re: Why not string theory? Because enough is enough

#57

Post by hugh4 » Sun, Jun 12, 2016, 9:53 AM UTC

visarga wrote: post #30

This is probably a naive question: can we input a bunch of particle interactions into a deep learning system and train it to predict the probability of future interactions? It would be like a "black box version" of physics. If we can predict, then we can find a more elegant mathematical notation and a more intuitive physical interpretation. Machine learning can observe and learn patterns that are more complex than hu…

I was actually thinking about this earlier in the day. Could we derive the law of universal gravitation if all we had was a bunch of data about the positions of objects at times plus a bunch of machine learning algorithms, and were otherwise complete idiots?

But in this case, no, because there's nothing in the particle data that we have that can't already be explained by the standard model, plus any number of other possible models. If only we could find something in the data that we couldn't explain, we might be able to make some progress, but that data doesn't exist.

Re: Why not string theory? Because enough is enough

#58

Post by hugh4 » Sun, Jun 12, 2016, 10:00 AM UTC

oska wrote: post #25

spectrum1234 wrote: post #22

This ties in with my thinking that there is just too much funding to do research for the sake of research. Let the private sector work on moonshots if they want but more realistically no one should be researching super far out problems. Instead the agile "just in time" approach needs to be used in academia as well as private companies. A good analogy is no one was trying to build electric cars fifty years ago but now…

> A good analogy is no one was trying to build electric cars fifty years ago but now they are. People were trying to make electric cars 120 years ago and succeeding. [1] [1] https://en.wikipedia.org/wiki/Electric_car#History

True. On the other hand, nobody was trying to build them fifty years ago.

Re: Why not string theory? Because enough is enough

#59

Post by j-pb » Sun, Jun 12, 2016, 10:06 AM UTC

espadrine wrote: post #53

return0 wrote: post #50

Earlier quoted context omitted.

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).

Give me a ML system, and I can give you a problem it cannot solve. I am guaranteed success thanks to the No Free Lunch theorem: https://en.wikipedia.org/wiki/No_free_lunch_theorem . In the case of deep learning, I can point to the task of determining values above 0.5 on an infinite Perlin-noise-derived 2D space fed by Mersenne Twister with seed 0, with an infinite number of octaves. Deep learning does not deal well w…

Sure, I choose... "Exhaustive Search in the space of programs". (maybe with some genetic algorithm heuristics to shave of a couple billion years on each query)

It's a ML system that can solve any decidable and even some semi decidable problems. Which is (if the church turing thesis holds) everything that can be understood by humans or other.

You might not be able to wait around long enough to see it give you a result though, but hey at least _it_ got an answer.

Re: Why not string theory? Because enough is enough

#60

Post by ewjordan » Sun, Jun 12, 2016, 10:06 AM UTC

espadrine wrote: post #53

return0 wrote: post #50

Earlier quoted context omitted.

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).

Give me a ML system, and I can give you a problem it cannot solve. I am guaranteed success thanks to the No Free Lunch theorem: https://en.wikipedia.org/wiki/No_free_lunch_theorem . In the case of deep learning, I can point to the task of determining values above 0.5 on an infinite Perlin-noise-derived 2D space fed by Mersenne Twister with seed 0, with an infinite number of octaves. Deep learning does not deal well w…

Humans are neither immune to No Free Lunch, nor able to predict Perlin noise.