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Why not string theory? Because enough is enough

backreaction.blogspot.com

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Re: Why not string theory? Because enough is enough

#61

Post by yoha »

throwaway43 wrote: post #18
colordrops wrote: post #16

The author says that he "became more convinced [string theorists] are merely building a mathematical toy universe." The explanation for his conclusion was that they kept revising string theory in order to fit observations. I know little of string theory, so I am probably misreading his meaning, but isn't that how all science works? Create a model, then revise it when you get more data.

It's called adding epicycles : http://rationalwiki.org/wiki/Adding_epicycles

I mostly agree, but I'd argue that epicycles actually made useful and testable predictions, although we found a simpler description later on. The article underlines a lack of refutable theories. Thus, I think [1] is a better analogy.

[1] http://rationalwiki.org/wiki/The_Dragon_in_My_Garage

Re: Why not string theory? Because enough is enough

#62

Post by tim333 »

Steuard wrote: post #11
tim333 wrote: post #3

I've got a theory that string theory is more of a sociological phenomena than real science. It started as a genuine attempt to model how the nucleus was held together and then continued because it's a good area to do maths and write papers rather than because it models reality. One thing I don't get, which may be down to my own stupidity - take maybe the simplest interaction in physics - you have two electrons in spa…

I've got a theory that string theory is more of a sociological phenomena than real science. I've got to say, that comes across as awfully dismissive toward a whole lot of very thoughtful people. I'm not entirely sure what you mean by it. Certainly all of the string theorists I've known (it's my profession) have talked as if they believed their work was involved in a "genuine attempt" to model reality. Maybe a physici…

>most of the questions you've asked are really quantum field theory questions: the string theory connection is almost entirely unimportant in that regime

My issue with the two electrons repelling scenario is that in normal quantum field theory it's modeled, I think, as the exchange of virtual photons and given that we don't really understand what a photon is and that they have odd spread out kind of properties that seem to allow that sort of interaction between distant things I can accept that that happens even if we don't understand how it fully. When you get to "emit and absorb virtual photons by pinching off into new tiny strings" and similar ideas though I just can't see it. My hunch is electrons and photons are in their base nature more abstractly mathematical and attempts to model them as bits of stuff will fail.

Re: Why not string theory? Because enough is enough

#63

Post by espadrine »

j-pb wrote: post #59
espadrine wrote: post #53

Earlier quoted context omitted.

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…

If you allow impractical ML systems, you might as well pick a dice. Sure, the answer is inaccurate, but there's a non-zero probability that it is correct!

But, realistically, the ML system you devise cannot learn about features that require knowledge outside of the observable universe.

Re: Why not string theory? Because enough is enough

#64

Post by jules »

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…

What would be the advantage of that over doing zillions of particle interactions in an actual particle accelerator?

If history is any guide then theories of physics are extremely simple. All the way from classical mechanics to special relativity, general relativity, quantum mechanics, quantum field theory, the theories are simple. In fact in some sense our theories have been getting simpler.

Take for instance the step from classical mechanics to special relativity. In classical mechanics time and space are not on equal footing. Time is the independent variable, and the space coordinates of particles are functions of time. In special relativity these are put on equal footing, and this simplifies the physics. It's not easier, but it's simpler in the sense that there are fewer parts. You used to have energy, and x-momentum, y-momentum, z-momentum for the 3 spatial directions. It turned out that energy is t-momentum in the time direction. This simplifies the equations because instead of having one equation for energy and another for x,y,z-momentum, you have one equation for x,y,z,t-momentum. Symmetries between electric and magnetic fields in Maxwell's equations were discovered that unified them into one object, just like with energy and momentum. A similar story exists for quantum mechanics.

What you suggest about modeling physics as a black box has been suggested before. After the first particle accelerators were built people were finding lots and lots of new particles, and there wasn't any good theory for them. Some people thought that we had to give up on a theory, and just model the real world as a black box of X particles in -> Y particles out, and make a big database of such interactions. However, then the standard model came along and all those particles turned out to consist of a much smaller number of quarks that interact in a small number of definite ways.

With string theory however we are in precisely the opposite situation. We don't have a big amount of experimental data that we need to find a theory for. We have a large number of theories and no experimental data to distinguish them.

Re: Why not string theory? Because enough is enough

#65

Post by espadrine »

ewjordan wrote: post #60
espadrine wrote: post #53

Earlier quoted context omitted.

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.

They're definitely not immune to No Free Lunch, but they are able to predict Perlin noise.

(Sure, you could try to analyse humans as if they were spherical objects floating in void, but in practice humans have computers.)

Let me give you an example. The 2011 Nobel prize in chemistry was dedicated to the discovery and analysis of quasicrystals. Those also cannot be modeled by deep learning, as its building blocks, linear separators, cannot finitely appreciate infinitely generated structures (unless it essentially encodes a completely different ML system within its neural network). Yet humans can model them.

I could go on all day about this, as there is an infinity of problems where deep learning is inadequate: proving the three-color theorem, routing, computing multiplications, …

Don't get me wrong: deep learning is outstanding for a set of menial tasks that I love to see being handed off to machines. But it is not the be-all, end-all that is sometimes claimed.

Re: Why not string theory? Because enough is enough

#66

Post by tim333 »

XorNot wrote: post #14
tim333 wrote: post #3

I've got a theory that string theory is more of a sociological phenomena than real science. It started as a genuine attempt to model how the nucleus was held together and then continued because it's a good area to do maths and write papers rather than because it models reality. One thing I don't get, which may be down to my own stupidity - take maybe the simplest interaction in physics - you have two electrons in spa…

It's a fundamental mistake to assume that these are "real" strings being talked about. String theory is about the idea that reality is best modeled as, notionally, strings - i.e. the mathematics looks a bit like them if you sort of squint and look at it really hard. But there's a really good reason strings are a compelling here: because near as we can tell, vibrational frequency is really important to particle physic…

>really good reason strings are a compelling here: because near as we can tell, vibrational frequency is really important to particle physics

I can give a sort of counter argument. Fair enough the relationship between frequency and energy and also wavelength and momentum are fundamental to physics. But consider maybe the classic experiment where you demonstrate that, where you have a barrier with two slits and fire particles at it and get an interference pattern on a screen on the other side. You can calculate how close the interference stripes will be using λ = h/p and this works not only for point particles like electrons but more complicated ones like helium atoms or C60 molecules. How do you explain that with strings? There's definitely something going on like the amplitude state of the universe changes according to the difference in momentum for different particle paths but I see no way you can explain it using strings.

Re: Why not string theory? Because enough is enough

#67

Post by sampo »

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 wonder, if given a big set of time1, time2 pairs (with relatively short dt) of the positions of the planets in our solar system, as seen in the night sky on earth, a machine learning system would be able to come up with good way to predict where the planets are going, given their positions.

Surely a machine learning system would need to be able to solve this 18th century physics problem, before we throw modern physics problems at it.

Re: Why not string theory? Because enough is enough

#68

Post by return0 »

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…

In principle its not impossible.

Re: Why not string theory? Because enough is enough

#69

Post by tim333 »

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…

Maybe advanced AI of that nature could be the way forward in physics. I was quite good at physics at school etc. but have quite a job mentally even figuring how a gyroscope works and my brain totally fuzzes over at attempts to combine quantum field theory with general relativity. It may be that the theory of everything is there but just a bit hard for our ape brains to process. It would probably require better AI than we have at the moment though.

A little closer to what you asked about, Demis Hassabis of Deep Mind has kind of suggested:

>I was giving a talk at CERN a few months ago; obviously they create more data than pretty much anyone on the planet, and for all we know there could be new particles sitting on their massive hard drives somewhere and no-one’s got around to analyzing that because there’s just so much data. So I think it’d be cool if one day an AI was involved in finding a new particle.

Re: Why not string theory? Because enough is enough

#70

Post by Gravityloss »

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…

It actually kind of has been done, can't find a link now...
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