I'm not sure why you don't think it's Physics. It's about formulating laws that describe the behaviour of physical systems - that's the essence of what Physics is. I have a PhD in high energy theory and this really seems like Physics to me.
As-is, it's pseudoscience. What happens when you do a factor analysis on images? You get some measure of the axes of geometrical variance across those images.
Are those axes "related" to any physical variables, sure -- but almost never directly. To suppose the system itself had these properties is to suppose, for example, constellations actually exist and cause your personality traits.
Everything we want to know is what phyical properties of the system give rise to the observed consistent correlations in geometrical properties. *THAT* is physics.
Showing these geometrical properties exist and are consistent is just what we're trying to explain.
You cannot go from images to the domain of physics -- there are an infinite number of theories consistent with these images domains. And this is pseudoscience.
There's no such thing as "physical properties of the system" other than measurable quantities that can be used to make predictions, which is what this does. There's no reason to be sure that temperature, for example, is a "real" physical property of a system rather than just one of many variables that would help us model it and understand it.
Do you think it's pseudoscientific because there's no theory-ladenness in the predictions?
From the article, it doesn't work. They found on known physics it gives 4.7 dimensions, of which only two are explicable -- 4 is correct; the others have no known physical interpretation. No surprises: those two are just the geometric properties of the system (angles) which are actually properties of the image. The others are pure bullshit.
Since, of course, the real physical parameters of the system we take to have generated those images are not present in them. The images are distal effects of these things
Only in cases where the geometric properties of the target system are causally relevant to its actual causal properties will this work -- ie., only when "angles matter"
Thinking you can infer laws of nature from images is pseudoscience, and these guys need to think more carefully about why we experiment in the first place
eg., Consider that if mass is a relevant causal property, there'd be no way of inferring it from images: two objects can be visually identical whilst having radically different masses... making images *OBVIOUSLY* not a measure of mass...
this project almost defines the modern kind of schizophrenic pseudoscience born of this wave of AI
So for example, take an extreme case, suppose that somebody says he wants to eliminate the physics department and do it the right way. The “right” way is to take endless numbers of videotapes of what’s happening outside the video, and feed them into the biggest and fastest computer, gigabytes of data, and do complex statistical analysis — you know, Bayesian this and that [Editor’s note: A modern approach to analysis of data which makes heavy use of probability theory.] — and you’ll get some kind of prediction about what’s gonna happen outside the window next. In fact, you get a much better prediction than the physics department will ever give. Well, if success is defined as getting a fair approximation to a mass of chaotic unanalyzed data, then it’s way better to do it this way than to do it the way the physicists do, you know, no thought experiments about frictionless planes and so on and so forth. But you won’t get the kind of understanding that the sciences have always been aimed at — what you’ll get at is an approximation to what’s happening.
Unless you have videos of experiments designed to observe measurement devices we have created, on systems we have designed, it's all useless.
The only useful thing in figuring out how nature works is creating truely novel experimental circumstances and measuring them with novel devices created for that purpose.
You cannot do science as a statitics of images; that's pseudoscience. And chomsky is here only half-right; it's actually much worse than he's sayign.
And maybe the 4.7 is actually more correct? The 4-parameter model is an approximation that neglects friction and air resistance. Moreover the double pendulum is a chaotic system and chaotic systems sometimes have dynamics described by laws with non-integer exponents such as Lyapunov dimension. I'm just spitballing, but the point is that it's not a priori ridiculous.
It's definitely possible to estimate mass from images. How do you think we know the masses of asteroids and planets? No-one put them on a scale, we just record their motion and work out which value fits best.
A concept that says gravity is the result of bending spacetime, with the speed of light being constant. It's not just a model, it's saying the universe is 4D spacetime, which explains why GR is so predictive.
I think a counter argument would be that if there is SOME signal in the photos AND there's enough training data that does have the correct ground truth signal that the scientists are matching up then you can have SOME level of accuracy. If the training set can reasonably cover the space of possibility that we're interested in then we can get reasonable interpretations.
However in this case the insane number of physical phenomena will always be larger than any training set so this approach should NEVER generalize there will always be way too much noise which is what the scientists have figured out here. So I agree with you that it's extremely limited but I don't think I'd call it pseudoscience there might be very limited domains where for example the only data we have available are images and so such a tool may be appropriate.
I definitely share your frustration though since any half way decent scientist should have just done a thought experiment instead and figured that this wouldn't work well. This smells like BS academic marketing where they always inflate their own impact and significance.
Perhaps they could leverage their lifelong training set which correlates scenes that look like they have bowling balls with scenarios that have a high mass movable sphere.
Perhaps we could have a good laugh together by painting a bowling ball to look like styrofoam and painting styrofoam to look like a bowling ball- then we could watch the silly ai/human apply an incorrect mental model and fail to grasp the causal reality! Ohohoho
Granted, the reason why people did Astronomy was because they believed in Astrology, but it's no longer been the case since a while.
I didn't see any attempt to formulate laws. The researchers trained a neural net model to predict the next event in a sequence. That is not a natural law, it's a maximum probability estimator.
To clarify, a natural law would be a formula with variables that one can plug in numbers to, in order to predict the behaviour of a system. For example, Newton's law of gravitation is a natural law, Kepler's laws of planetary motion are natural laws, the laws of thermondynamics are natural laws. But a neural net model trained to predict the next frame in a video? How is that a "law"?
As a for instance, if I train a neural net to predict the motions of the planets, the trained model is a law of planetary motion, like Kepler's laws of planetary motion? Is that correct?
Then the first half of the network (before the low-dimensional layer) will learn how to "encode" the state of the system in the video in as few variables as possible, such as the orientations and angular momenta of the double pendulum. This is equivalent to what humans do when we look at a messy physical system like the Solar System and model it with a few quantitative parameters.
The bottleneck layer will represent the handful of state variables, and then finally the other half of the network will learn the mathematical function that predicts the system's evolution. This is equivalent to what humans do when we work out physical laws and equations of motion.
I can agree that a neural net can learn a model that can predict the behaviour of a system, to some extent, within some margin of error.
That's not enough for me to see neural net models as (scientific) "laws". For the sake of having a common definition of what a scientific law is, I'm going with what wikipedia describes as a scientific law: a statement that describes or predicts some set of natural phenomena, according to some observations (paraphrasing from: https://en.wikipedia.org/wiki/Scientific_law). Sorry for not introducing this definition earlier on. If you disagree with it, then that's my bad for not estabilishing common terminology beforhand.
In that sense, neural net models are not scientific laws because, while they can predict (but not describe) they are not "statements". Rather they are systems. They have behaviour and their behaviour may match that of some target system, like the weather say. But like a simulation of the economy, or an armillary sphere are not, themselves "laws", even though they are possibly based on "laws", a neural net's model can't be said to be a "law", even if it's based on observations and even if it has an internal structure that makes its behaviour consistent with some (known or unknown) law.
There is also the matter of usability: neural net models are, as we know, "black boxes" that can't be inspected or queried, except by asking them to analyse some data. While useful, that's not a "law", because it does not help us understand the systems they model. If this sounds like a semantic quibble, it isn't. To me anyway it doesn't make sense to base scientific knowledge on a bunch of inscrutable black boxes. Scientific laws and scientific theories are not black boxes.
As an aside, neural nets fall short of what Donald Michie (father of AI in the UK) called "ultra-strong machine learning" [1]. That's the property fo a machine learning system that improves not only its own performance, but that of its user, also. Current techniques aren't even close to that.
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[1] Machine Learning: the next five years, Donald Michie, 1988
But I would argue that this parsimony is illusory. There's a lot of implicit knowledge needed for the interpretation of physical laws. The laws are written using specialized mathematical notation such as special functions, partial differential equations, in a certain conceptual framework such as Lagrangian mechanics. You need to understand the concept of abstracting and quantifying a dynamic system (most people wouldn't imagine you can do this) and then you have to learn all the tips and tricks of how to reformulate and solve systems.
For example, I could write a mathematical representation of quantum electrodynamics (the theory of how electrons and photons interact) on a single index card. However, I would need to dig into my two shelves of QFT textbooks to actually make any quantitative experimental predictions, on top of my degree, PhD and post doc experience, which I need to even be able to read the textbooks (and I would still mess up the minus signs).
I think it's important to remember that these neural networks are doing all of that - not just finding the physics, but also all the abstraction, calculation and interpretation that is usually taken for granted but actually very non-trivial.
The tools of physics have a lot of implicit assumptions that guide the end result in ways that I would describe as parsimonious in terms of how much the output state space must be reduced. They are much more free, which is why they can be amazing for some very hard shit, but proving they're behaving exactly in "physical" way is very hard.
"Time is defined so that motion looks simple" is my favourite quote from MTQ for this reason. It's intuitive and yet also very physically "rigorous" in a way that people don't necessarily realize is a thing in physics beyond just using mathematics.
Maybe we can just train the AI to do the maths for us, dunno, but I think currently this tabula Rasa approach will inform the physics-y-ness. I still call it physics personally, but I don't really think it's interesting from a purely physical perspective.
There have been some works deriving conservation's laws and so on from empirical motion, which I think is very impressive at scale, but I don't know what that does for physics as opposed to the applications of said physics.