5 ms·

At some point we always need some sort of model of the world, you don’t want to call it physics?, that’s fine, but you are still modeling the world Interesting

by nico 2y ago

At some point we always need some sort of model of the world, you don’t want to call it physics?, that’s fine, but you are still modeling the world Interestingly though, physical models are usually expressed as mathematical equations. Which is an arbitrary way of modeling A Neural Network could technically “discover” different models, just through optimizing predictions for whatever we want the model to do We might not be able to distill the NN into nice compact equations, but they might still form a pretty good model of whatever phenomenon is fed to it through observational data Note that just by picking the input data and the output expectations, we are already defining a model

universa1 2y ago
in some sense yes, and if you are only interested in good predictions this might work out well. What, maybe due to my limited understanding, is, that this is not theory driven and therefore does not really provide understanding of the underlying process.
- bongodongobob 2y ago
  I think we understand the processes just fine. The issue is that we can't get a "closed form" solution for 10^100 interactions per planck time.
  - gowld 2y ago
    That's like saying chemistry has nothing to understand because we know Schrodinger's wave equation. Or that we understand biology and psychology just for for the same reason.
    - bongodongobob 2y ago
      Yeah, I think that makes sense. Those systems are similar in that they are made of zillions of tiny parts and there's no way to pull a one equation to rule them all out of it. We could, given infinite compute, but it's just not feasible.
- Mathnerd314 2y ago
  I think at some point it is worth admitting that there are variables you can't account for. Like the precise geography - the models model an area 1 mile square as a single vector, maybe even more coarse. They don't model every tree, rock, and bush. In a neural net you can just have "weight goop" which accounts for the net effect of these unmodeled features, but in a traditional model adding "fudge factors" and extrapolating back from the model to points of interest is tricky.
- nico 2y ago
  > and therefore does not really provide understanding of the underlying process What is “the underlying process”? For example, Newton was able to model gravity quite successfully without ever being able to “understand the underlying process”. In fact, physics today still doesn’t have a good grasp on what gravity is. Yet we use the models and equations all the time In a way, physics is also a collection of black boxes, perhaps just seemingly more elegant boxes
  - lainga 2y ago
    > What is “the underlying process”? IMO - The simple ones: advection, latent heat release/absorption from water changing phases, and the Coriolis force. If you need an AI for this, please take a course on differential equations. The hard ones: droplet/ice crystal formation, cloud feedback on radiative transfer, evaporation at air-sea boundaries. If you can train a model for these processes, please, please tell someone.
    - danielmarkbruce 2y ago
      "what is the underlying process" is another way of saying, all we have is models. We don't really understand anything. Even gravity. Yet, we can model gravity extremely well for practical purposes.
      - nico 2y ago
        Exactly. Taking it further, I don’t understand how my hands work. Yet here I am typing away, without even having a good model for it, except just the language I’m using to describe what I’m doing
  - SaberTail 2y ago
    Gravity can be modeled as "things on the earth's surface accelerate downward at about 10 m/s², regardless of their mass". This works very well. Gravity can be modeled as "planets orbit the sun according to Kepler's laws". This also works very well. Newton realized that these phenomena could be explained as arising from the same underlying process of an inverse square law. This is a much more useful model, and allows predictions that allow us to do things like space flight, even if it is not complete.
- MyFirstSass 2y ago
  Wouldn't you be able to somehow couple it with another model that takes the NN data and somehow untangles it's convoluted logic into an isomorphic human readable equation, ie. a model that has one task and that is translating NN logic into human equations. The training data could be real physics in a simulator held up against evolutionary driven AI logic that competes against it with various goals that are then evaluated and if given a high score then marked as isomorphic and given enough runs you'd get a dataset.
  - nico 2y ago
    Very cool idea! I wonder how simple could the NNs be to model some basic physical process?
akira2501 2y ago
My dog knows nothing about physics. He's an expert at catching frisbee's though. The problem is he chases anything that looks remotely like a frisbee.
- nico 2y ago
  My comment was in the context of the article, which states that AI doesn’t need physics Having models doesn’t imply what you probably mean by “knowing about physics”, it just means having a representation of something that is not that something (like a map of the world is a map, not the world) So, depending on how the dog’s understanding of the world works, it maybe doesn’t need any models, I can’t really know But I do know, that if I want to describe anything, using any symbols whatsoever, then I’m implicitly creating a model of what I’m trying to describe with the symbols So, if I’m trying to communicate and understand physical phenomena using AI, then I’m implicitly creating a physics model, whether I want to call it that or not
PaulDavisThe1st 2y ago
> but you are still modeling the world No. That's precisely the issue here. NN's do not "model the world" in anything remotely like the traditional meaning of that term. Modelling the world historically means identifying objects/phenomena and proposed causal relationships between them. Without the relationships - e.g. if we add heat to the system, it will move more - there's no model. You may still be able to get predictions, and they may still be useful, but you are not defining a model.
- JulianChastain 2y ago
  A charitable interpretation of nico is that he was saying a well-trained NN is itself a model of the world. If it can tell you what a system will do given some inputs, then it functions as a model. While internally it isn't creating a model that we could understand, it does "model the world" in the sense that we can treat it as a model
  - nico 2y ago
    That’s an excellent interpretation. Thank you
  - PaulDavisThe1st 2y ago
    Consider for a moment replacing the NN with another person, who forms a model of the world that is very useful for prediction. Now our lead experimenter asks this person "what will happen if the global average temperature increases by N degreesC?" and they get an answer. Can we way that the lead experimenter has built a model? They have not, certainly not in the sense that they have any access to it. The person who replaced the NN may have (and indeed, probably has) built some sort of model, but that's a very different claim. Explainability in NN/ML systems is a hot topic, and many people (not all!) would say that if the NN/ML system cannot explain why adjusting parameter X will cause changes in parameters A, M and T, then you have no access to anything that merits being called a model. A consequence of this is that if the person who replaced the NN can explain themselves (e.g. answer the X -> A,M,T coupling), then even the experimenter can probably be said to "have a model". But if all that can be said is "I don't know and/or I can't explain, you just need to trust me that this coupling is real", then the claim that a model has been built is on unstable ground.
Xcelerate 2y ago
> Interestingly though, physical models are usually expressed as mathematical equations. Which is an arbitrary way of modeling Not exactly. The particular symbols used in mathematics may be arbitrary but the mathematical structures and relationships are not. There’s some interesting research on how the limits of predictability may come down to what is mathematically provable in a formal system. That may sound kind of weird, but consider that any computable model can be modeled as a Turing machine, which is essentially a process that manipulates symbols according to a set of rules—not too different than mathematics itself. The difference is that based on certain assumptions of internal consistency (that cannot be proven if the system is in fact consistent), the mathematical manipulation of symbols can be used to make predictions about the behavior of Turing machines. There’s a very deep connection between the two and neural networks are still just another form of this, perhaps not as human-interpretable however.

by nico 2y ago

universa1 2y ago

in some sense yes, and if you are only interested in good predictions this might work out well. What, maybe due to my limited understanding, is, that this is not theory driven and therefore does not really provide understanding of the underlying process.

bongodongobob 2y ago
I think we understand the processes just fine. The issue is that we can't get a "closed form" solution for 10^100 interactions per planck time.
- gowld 2y ago
  That's like saying chemistry has nothing to understand because we know Schrodinger's wave equation. Or that we understand biology and psychology just for for the same reason.
  - bongodongobob 2y ago
    Yeah, I think that makes sense. Those systems are similar in that they are made of zillions of tiny parts and there's no way to pull a one equation to rule them all out of it. We could, given infinite compute, but it's just not feasible.
Mathnerd314 2y ago
I think at some point it is worth admitting that there are variables you can't account for. Like the precise geography - the models model an area 1 mile square as a single vector, maybe even more coarse. They don't model every tree, rock, and bush. In a neural net you can just have "weight goop" which accounts for the net effect of these unmodeled features, but in a traditional model adding "fudge factors" and extrapolating back from the model to points of interest is tricky.
nico 2y ago
> and therefore does not really provide understanding of the underlying process What is “the underlying process”? For example, Newton was able to model gravity quite successfully without ever being able to “understand the underlying process”. In fact, physics today still doesn’t have a good grasp on what gravity is. Yet we use the models and equations all the time In a way, physics is also a collection of black boxes, perhaps just seemingly more elegant boxes
- lainga 2y ago
  > What is “the underlying process”? IMO - The simple ones: advection, latent heat release/absorption from water changing phases, and the Coriolis force. If you need an AI for this, please take a course on differential equations. The hard ones: droplet/ice crystal formation, cloud feedback on radiative transfer, evaporation at air-sea boundaries. If you can train a model for these processes, please, please tell someone.
  - danielmarkbruce 2y ago
    "what is the underlying process" is another way of saying, all we have is models. We don't really understand anything. Even gravity. Yet, we can model gravity extremely well for practical purposes.
    - nico 2y ago
      Exactly. Taking it further, I don’t understand how my hands work. Yet here I am typing away, without even having a good model for it, except just the language I’m using to describe what I’m doing
- SaberTail 2y ago
  Gravity can be modeled as "things on the earth's surface accelerate downward at about 10 m/s², regardless of their mass". This works very well. Gravity can be modeled as "planets orbit the sun according to Kepler's laws". This also works very well. Newton realized that these phenomena could be explained as arising from the same underlying process of an inverse square law. This is a much more useful model, and allows predictions that allow us to do things like space flight, even if it is not complete.
MyFirstSass 2y ago
Wouldn't you be able to somehow couple it with another model that takes the NN data and somehow untangles it's convoluted logic into an isomorphic human readable equation, ie. a model that has one task and that is translating NN logic into human equations. The training data could be real physics in a simulator held up against evolutionary driven AI logic that competes against it with various goals that are then evaluated and if given a high score then marked as isomorphic and given enough runs you'd get a dataset.
- nico 2y ago
  Very cool idea! I wonder how simple could the NNs be to model some basic physical process?

akira2501 2y ago

My dog knows nothing about physics. He's an expert at catching frisbee's though. The problem is he chases anything that looks remotely like a frisbee.

nico 2y ago
My comment was in the context of the article, which states that AI doesn’t need physics Having models doesn’t imply what you probably mean by “knowing about physics”, it just means having a representation of something that is not that something (like a map of the world is a map, not the world) So, depending on how the dog’s understanding of the world works, it maybe doesn’t need any models, I can’t really know But I do know, that if I want to describe anything, using any symbols whatsoever, then I’m implicitly creating a model of what I’m trying to describe with the symbols So, if I’m trying to communicate and understand physical phenomena using AI, then I’m implicitly creating a physics model, whether I want to call it that or not

PaulDavisThe1st 2y ago

> but you are still modeling the world No. That's precisely the issue here. NN's do not "model the world" in anything remotely like the traditional meaning of that term. Modelling the world historically means identifying objects/phenomena and proposed causal relationships between them. Without the relationships - e.g. if we add heat to the system, it will move more - there's no model. You may still be able to get predictions, and they may still be useful, but you are not defining a model.

JulianChastain 2y ago
A charitable interpretation of nico is that he was saying a well-trained NN is itself a model of the world. If it can tell you what a system will do given some inputs, then it functions as a model. While internally it isn't creating a model that we could understand, it does "model the world" in the sense that we can treat it as a model
- nico 2y ago
  That’s an excellent interpretation. Thank you
- PaulDavisThe1st 2y ago
  Consider for a moment replacing the NN with another person, who forms a model of the world that is very useful for prediction. Now our lead experimenter asks this person "what will happen if the global average temperature increases by N degreesC?" and they get an answer. Can we way that the lead experimenter has built a model? They have not, certainly not in the sense that they have any access to it. The person who replaced the NN may have (and indeed, probably has) built some sort of model, but that's a very different claim. Explainability in NN/ML systems is a hot topic, and many people (not all!) would say that if the NN/ML system cannot explain why adjusting parameter X will cause changes in parameters A, M and T, then you have no access to anything that merits being called a model. A consequence of this is that if the person who replaced the NN can explain themselves (e.g. answer the X -> A,M,T coupling), then even the experimenter can probably be said to "have a model". But if all that can be said is "I don't know and/or I can't explain, you just need to trust me that this coupling is real", then the claim that a model has been built is on unstable ground.

Xcelerate 2y ago

> Interestingly though, physical models are usually expressed as mathematical equations. Which is an arbitrary way of modeling Not exactly. The particular symbols used in mathematics may be arbitrary but the mathematical structures and relationships are not. There’s some interesting research on how the limits of predictability may come down to what is mathematically provable in a formal system. That may sound kind of weird, but consider that any computable model can be modeled as a Turing machine, which is essentially a process that manipulates symbols according to a set of rules—not too different than mathematics itself. The difference is that based on certain assumptions of internal consistency (that cannot be proven if the system is in fact consistent), the mathematical manipulation of symbols can be used to make predictions about the behavior of Turing machines. There’s a very deep connection between the two and neural networks are still just another form of this, perhaps not as human-interpretable however.