Why not string theory? Because enough is enough

Will string theory be more parsimonious than the the more concrete model of physics it tries to model?

"I am far from "silent" on what should be done. I have said here and many times before that what needs to be done is to take measures against social and cognitive biases. If people still work in masses on string theory after that, so fine."

    (Perhaps a bit self-serving for me).

At the level of quantum theory, you don't have charges repelling. You have charged particles, like electrons, exchanging other particles--photons in the case of the electromagnetic force. So really it's all particles (or all quantum fields, if you look at it another way). And you can make any particle (or quantum field) out of strings, so string theory is just the next level down, so to speak, where all of the particles--the ones that exchange other particles, and the ones that get exchanged--are just different vibration states of strings.

Anyway, particles repel because that is the nature of the fields they exist in. Electrons don't have to do anything 'to' each other to repel, they just have to interact with the electromagnetic field. The string model simply employs the word 'string' to describe some components of the field.

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 physicist can make a living by just "doing math and writing papers" without any expectation of making meaningful progress toward understanding the world, but the people who really drive the field and get noticed are almost invariably folks who dream of changing the world, of being the next Hawking or Witten or even the next Einstein. If someone like that doesn't have the a pretty solid hope that what they're working on is likely to be truly relevant to reality, they work on something else.

I think that others have already more or less answered your electron question. I might answer it this way: one of the great triumphs of 20th century physics was the realization that the details of how physics works on very small scales (or equivalently, very high energies) get very predictably "blurred out" when phenomena are measured at longer scales (or lower energies). The formal process involved is called "renormalization", and the punchline here is that essentially any ultimate theory of nature (whether that's string theory or something else) will reduce to an ordinary quantum field theory (with "renormalizable" fields) once you get a few orders of magnitude below its intrinsic scale.

That means in particular that any "stringy" physics (or any "loopy" physics, or even any simpler "grand unification" physics) will inescapably be completely washed out and undetectable when electrons interact from a few cm apart. All you need to understand is "simple" quantum electrodynamics, in which the two electrons exert a repulsive force on each other by way of their interactions with the surrounding electromagnetic field: interactions that can be described as being mediated by exchange of virtual photons as force-carrying particles.

For that calculation, it's not at all important to know that (e.g.) each electron is an extremely tiny string in a specific vibration mode, nor that they (more or less) emit and absorb virtual photons by pinching off into new tiny strings or in a different specific vibration mode and then joining back up with them in a uniquely determined way. (This would be a good place for me to note that nobody knows exactly which scenario in string theory would correspond to our world, so nobody actually knows exactly what vibration mode corresponds to an electron. But we know a lot of ways in which things like that might work.) My point here is just that most of the questions you've asked are really quantum field theory questions: the string theory connection is almost entirely unimportant in that regime.

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 physics - the energy of a photon is quantized as E=nhf , (Energy = integer * planck's constant * frequency). And this seems to scale all the way down - it works for photons, it works for electron orbitals, and it comes into play dealing with the virtual particles which are exchanged in force interactions.

So however you describe reality, whatever you do, your abstraction pretty assuredly needs to somehow recover the vibrational importance, which implies certain kinds of "geometry". You get "strings" because strings are one of the most fundamental structures which can hold a vibrational mode, and importantly, they kind of neatly explain the quantization as standing waves - closed strings can only setup a standing wave at certain frequencies depending on their length, same with open strings (the wave needs to be zero at the ends of the "string" or it'll lose energy).

Of course this is all my very ad hoc handwavey explanation of it, but the point is, there's a lot of very basic physics which works quite well in the model. It did not, and does not, just come from nowhere even at this very high level.

Yes. I have a friend with a PhD in physics, and he says that a generation of string theory people basically just shouted everyone else down in a rather obnoxious way. Other critics (e.g. Lee Smolin) have said similar things, albeit slightly more politely.

At some point it stopped being science and became more about careers, reputations, funding, and paper mills. Which, given the way that academia works at the moment, became self-sustaining.

Some interesting math has fallen out of string theory, but it should never have been allowed to hold back other approaches to quantum gravity to the extent that it has.

Considering the amount of time and paper involved, there's a huge wall of maybe-perhaps-if but very little solid physics to show for the effort.

Incidentally, that blog is a real find - some of the clearest explanations of hard concepts I've seen anywhere.

There are still some formulations that haven't been ruled out by observations such as LHC data, but not many, and they're not necessarily the ones that were anyone's darlings.

Let's say I propose that no existing solar panel designs respond to artificial light. Easily falsifiable. Say you perform the experiment and find that panels from First Solar do respond to artificial light. I refine my hypothesis to "no existing solar panel designs respond to artificial light except for First Solar". You find that SunEdison panels do, by testing at your lab in SF. I refine my hypothesis to "no existing solar panel designs respond to artificial light when tested outside San Francisco except for First Solar". You repeat the experiment in San Jose, and I refine my hypothesis again.

We're revising the model, keeping it falsifiable, and keeping it consistent with existing evidence. It would not be without merit to claim that this is something of a toy hypothesis, however, and that our time would be better served otherwise.

Given how real people and organizations work, psychology, resource limitations, incentives, etc., this is VERY idealistic, but still, imagine that you had found your data in a different order, so you ended up with the same data you have now, but had a different subset previously. With two different subsets in the past, your best past models might have been different from each other, but your best choice now should be the same now that both paths have converged on the same data. So why should your choice in the past have a "vote" in your choice now?

So (again, ideally), don't "revise" your old model; take the data you have now, old and new, pretend you are starting from scratch, and choose the best model. If a theory such as String Theory keeps failing to account for new data and is repeatedly modified to keep it from being disqualified, a reasonable question would be whether, if we started from scratch knowing what we know now, we would come up with this repeatedly patched String Theory version as our first choice.

(And, yes, I know that from a Bayesian perspective you can't literally start "from scratch", but that doesn't mean you have to use your most recent model as your prior).

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.

The author and I got the impression that string "theory" (let's call it "string hypothesis" instead?) has so many degrees of freedom that it can predict basically anything you throw at it, even things we now know aren't physical, so its predictive value is zero.

After it was proposed as a self-consistent theory of quantum gravity it was realized it wasn't self-consisten because it had ghosts, which was then fixed by adding 22 dimensions. But this is a problem because that's not how many dimensions the world has so they solved it by compactification that folds all the extra dimensions tiny so they don't bother us. But then it turns out that there is no particular reason to fold just so so that we get the world we live in. And now they are trying to find a way to make it so its natural that the folding happens just so that we get the world we live in.

Secondly, and most importantly, while it is true that some models get revised whenever there is more data, those are poor models. Good models agree with all the data and don't need modification. (The best models agree with most of the data, but sometimes don't so that the scientist gets a mystery to solve!) Realistically this stage takes a while to happen, and proponents of string theory argue that they right now have a models that is going to be great one day, but right now needs a bit of tinkering. What critics say is that string theory has been in the "tinkering" stage for around 50 years now and maybe we should try other ideas a bit more.

Finally, I don't think Sabine likes being refereed to as a "he" and rather have that pronoun used to describe the father of her children rather than herself.

On the other end of the predictive spectrum there's Einstein. Somebody fiddles with the equations and says, "if this is right then there could be black holes!" And then... they find black holes. It seems that every few years there's some confirmation of an odd corner case that shows the predictive power of Einstein's theories.

That string theory can calculate the known might still be useful, if it's a more convenient way to get the results than other theories. Plain old Newtonian physics is still hella useful today, for instance, even for space missions. AFAIK, string theory is not more convenient.

Take this with a grain of salt, because I haven't kept up on what string theory has been doing lately. As a matter of personal triage I stopped following it until such time as I heard that someone predicted something interesting with it, and it was confirmed in observation or experiment. If that happened then I missed it, and would love to hear about it.

"I have solved all of math, physics, and everything else ! Look ! My theory is simply the assembly programming language. Any problem can be expressed in it and anything that we can predict we can predict using some assembly language".

You wouldn't consider this to actually predict anything, right ? Despite the statement being perfectly true. Change any prediction into the assembly program printing it out. Done/done. So the critique of string theory is that it is such a form of assembly language. It is a general principle, that can express nearly any algorithm.

So generally one considers that any theory, for it to be a theory, has to predict things unrelated to why it was originally designed, with no changes (or at the very least, very minimal changes). Quantum theory, for instance, was designed to solve a particular problem relating to electrons, but turned out to solve the ultraviolet catastrophe (and dozens of other problems).

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 able to harness a massive dataset and really good storage capabilities.

Also, you would need to impose a "consistency" constraint on the computer which would be hard to do. Like a computer might say if (mass > 5) do this; else do that. And that is valid computationally. We do that in our split between gr/qm. But in some sense we feel this is wrong physically. The universe shouldn't run on arbitrary if statements.

So I think the answer is just no: the computer algorithms we have today can't handle the problem's constraints.

Long answer, the input you are thinking of (particle interactions) is already what partly defines the theory you are trying to uncover. So you have a serious chicken-egg situation.

You could think about inputting the data that comes out of particle detectors to look for patterns etc. and that sort of thing is being done already. However there is a difference between finding existing patterns and then figuring out the underlying theory which is the mathematical construct that lets you predict things.

Finally, even if you could have a "black box" predict stuff (correctly), the black box-ness is a serious problem for scientists from a meta-science or philosophy of science point of view, and people would be highly unsatisfied until they actually understood what was going on.

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

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.

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.

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.

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.

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.

People were trying to make electric cars 120 years ago and succeeding. [1]

[1] https://en.wikipedia.org/wiki/Electric_car#History

Flamebait: Should we stop funding schools because most kids are bad at reading ?

> But they only are because progress has been made indirectly in other fields to make it worth it now

Yeah, like the fundamental 'moonshot' research done in the 1700s, 1800s and early 1900s, which now serves as foundation for all electronics.