5 ms·

> we do not learn these inferences "just by observing the world" We most certainly can, just not as well or as strongly as when we're able to influence the sys

> we do not learn these inferences "just by observing the world" We most certainly can, just not as well or as strongly as when we're able to influence the system under observation. You're speaking way too strongly and simplifying a complex mechanism down past anyone's expertise.

dumbfoundded 6y ago
An interesting related idea is inverse reinforcement learning. We watch how other people interact with a system to estimate a reward function and then we later test it out ourselves. Either directly with an environment or inside our own mental model of the environment. Simply "observing the world" can give us data about how to learn in an environment we've never interacted with before; only by watching others interact with it.
- still_grokking 6y ago
  That's true. But the key seems to remain the "interaction with the world".
  - dumbfoundded 6y ago
    Maybe it's semantics but interacting with a simulation of the world is actually more important. In a pure sense, this doesn't require any actual real world interaction. This concept is usually referred to offline and off-policy reinforcement learning. If you're saying that interaction in any sense is important, I'd very much agree that unsupervised learning and supervised learning aren't equipped to handle reinforcement learning problems. Correct framing of a problem is necessary to achieve a desired property like causality.
galaxyLogic 6y ago
Right. "World" includes us. When we perform actions we observe ourselves doing them and then we observe the consequences. That is the only way we can learn anything, by observing. And what else is there to observe than the "world"? We can observe our own thinking process but that is part of the world too. I would say. It is definitely not "out of this world" :-)
- deleted 6y ago
  [deleted]
- visarga 6y ago
  yes, we learn causal reasoning by observing consequences, we can't learn by simply watching without acting
shkkmo 6y ago
> We most certainly can That is a very, very strong statement that requires some proof to go with such a strong statement of certainty. While we can learn that the ball's change of movement just fine without swinging the bat, we can only do that because we are generalizing from a large body of knowledge that we devoloped by experimenting on the world using our body. I am not aware of a single piece of evidence that an agent can use purely observational learning to ever aquire the causal knowledge of the real world to sufficient level to make those sorts of inferences with any sort of reasonable accuracy.
- musingsole 6y ago
  I'd link studies about children aping things they have only seen through a window, but I suspect you'll quickly argue out of the bounds of those studies and so I won't.
deleted 6y ago
[deleted]
fractionalhare 6y ago
I don't think they're speaking too strongly. I think a lot of the time when we correctly infer causality without empirically interacting with the system, it's because we have built up significant categorical experience about more atomic systems we were able to interact with. In my view, a lot of things that are noninteractively inferred are compositions of more fundamental things that required empirical experience. When you've had the causality of gravity thoroughly beaten into you at a young age, a lot of other things seem intuitive that would otherwise completely fall outside a framework for being unempirically learned. Do you have a specific counterexample of causality you can infer without interaction or empirical experience of something related? Caveats: I'm not a neurologist or psychologist, so this is mostly philosophical speculation on my part.
- musingsole 6y ago
  Empirical evidence is a special case observation. If you observe the whole universe in its entirety, you could separate moments that effectively followed whatever conditions you might set in a lab. You can't act on the system, but you can forever tune your models to match the infinite observations you could record. At that point the separation between observation derived knowledge versus experiential knowledge is meaningless (it's just hard to imagine a universal model manifesting without having used experiental knowledge along the way). Whether you swing the bat or just watch it hit the ball into the sky, you have the prerequisites needed to reason about the interaction. A more entertaining question is how a system comes to believe causality (i.e. comes to believe that things can and must have causes)
  - still_grokking 6y ago
    > (it's just hard to imagine a universal model manifesting without having used experiental knowledge along the way) That's actually the point!
  - jozvolskyef 6y ago
    I agree. To say we're able to influence the system is to assume free will, which I believe is an undecidable problem. Ultimately this is just a matter of semantics, which makes this thread rather pointless.
jcims 6y ago
I think we have a hard time disconnecting our personal experience with the observations we make. When we look at a photograph of a person riding a bicycle down a path, even if we've never ridden a bicycle we've likely been outdoors, stood on a path, felt wind when we moved, etc. We may not be able to accurately simulate the experience in our mind but we can get close. On the other hand, the starting point for an ML system interpreting that same image is essentially a stream of scalar values that tend to demonstrate multiple layers of periodicity (3-4 byte intervals for RGBA and then another layer per line of rasterized image data and yet another per frame if it's a video). Here's a quick experiment. Let the video linked below play for a five count (sound is essential but a bit intense so maybe moderate the volume first) so you have some confidence it's not just me playing a rude trick, then close your eyes for a five count. There's going to be a major change in the sound when you get near 'five', now try to imagine how the scene changed before opening your eyes again: https://youtu.be/qnL40CbuodU?t=25 https://youtu.be/qnL40CbuodU?t=25 I think without any reference from an embodied perspective, we're asking ML systems to understand the sounds (which are also streams of scalar values that demonstrate periodicity) the same way we interpret the representation visually. (Also if you enjoyed the example above check out these two channels, some of them are mindblowing) https://www.youtube.com/user/jerobeamfenderson1 https://www.youtube.com/user/jerobeamfenderson1 https://www.youtube.com/c/ChrisAllenMusic https://www.youtube.com/c/ChrisAllenMusic And a fun video explaining it all - https://www.youtube.com/watch?v=4gibcRfp4zA https://www.youtube.com/watch?v=4gibcRfp4zA
Jeff_Brown 6y ago
After interacting with the world, we can learn from passive observation. No human has ever learned anything about causation without first spending a while experimenting in the world.

dumbfoundded 6y ago

An interesting related idea is inverse reinforcement learning. We watch how other people interact with a system to estimate a reward function and then we later test it out ourselves. Either directly with an environment or inside our own mental model of the environment. Simply "observing the world" can give us data about how to learn in an environment we've never interacted with before; only by watching others interact with it.

still_grokking 6y ago
That's true. But the key seems to remain the "interaction with the world".
- dumbfoundded 6y ago
  Maybe it's semantics but interacting with a simulation of the world is actually more important. In a pure sense, this doesn't require any actual real world interaction. This concept is usually referred to offline and off-policy reinforcement learning. If you're saying that interaction in any sense is important, I'd very much agree that unsupervised learning and supervised learning aren't equipped to handle reinforcement learning problems. Correct framing of a problem is necessary to achieve a desired property like causality.

galaxyLogic 6y ago

Right. "World" includes us. When we perform actions we observe ourselves doing them and then we observe the consequences. That is the only way we can learn anything, by observing. And what else is there to observe than the "world"? We can observe our own thinking process but that is part of the world too. I would say. It is definitely not "out of this world" :-)

deleted 6y ago
[deleted]
visarga 6y ago
yes, we learn causal reasoning by observing consequences, we can't learn by simply watching without acting

shkkmo 6y ago

> We most certainly can That is a very, very strong statement that requires some proof to go with such a strong statement of certainty. While we can learn that the ball's change of movement just fine without swinging the bat, we can only do that because we are generalizing from a large body of knowledge that we devoloped by experimenting on the world using our body. I am not aware of a single piece of evidence that an agent can use purely observational learning to ever aquire the causal knowledge of the real world to sufficient level to make those sorts of inferences with any sort of reasonable accuracy.

musingsole 6y ago
I'd link studies about children aping things they have only seen through a window, but I suspect you'll quickly argue out of the bounds of those studies and so I won't.

deleted 6y ago

[deleted]

fractionalhare 6y ago

I don't think they're speaking too strongly. I think a lot of the time when we correctly infer causality without empirically interacting with the system, it's because we have built up significant categorical experience about more atomic systems we were able to interact with. In my view, a lot of things that are noninteractively inferred are compositions of more fundamental things that required empirical experience. When you've had the causality of gravity thoroughly beaten into you at a young age, a lot of other things seem intuitive that would otherwise completely fall outside a framework for being unempirically learned. Do you have a specific counterexample of causality you can infer without interaction or empirical experience of something related? Caveats: I'm not a neurologist or psychologist, so this is mostly philosophical speculation on my part.

musingsole 6y ago
Empirical evidence is a special case observation. If you observe the whole universe in its entirety, you could separate moments that effectively followed whatever conditions you might set in a lab. You can't act on the system, but you can forever tune your models to match the infinite observations you could record. At that point the separation between observation derived knowledge versus experiential knowledge is meaningless (it's just hard to imagine a universal model manifesting without having used experiental knowledge along the way). Whether you swing the bat or just watch it hit the ball into the sky, you have the prerequisites needed to reason about the interaction. A more entertaining question is how a system comes to believe causality (i.e. comes to believe that things can and must have causes)
- still_grokking 6y ago
  > (it's just hard to imagine a universal model manifesting without having used experiental knowledge along the way) That's actually the point!
- jozvolskyef 6y ago
  I agree. To say we're able to influence the system is to assume free will, which I believe is an undecidable problem. Ultimately this is just a matter of semantics, which makes this thread rather pointless.

jcims 6y ago

I think we have a hard time disconnecting our personal experience with the observations we make. When we look at a photograph of a person riding a bicycle down a path, even if we've never ridden a bicycle we've likely been outdoors, stood on a path, felt wind when we moved, etc. We may not be able to accurately simulate the experience in our mind but we can get close. On the other hand, the starting point for an ML system interpreting that same image is essentially a stream of scalar values that tend to demonstrate multiple layers of periodicity (3-4 byte intervals for RGBA and then another layer per line of rasterized image data and yet another per frame if it's a video). Here's a quick experiment. Let the video linked below play for a five count (sound is essential but a bit intense so maybe moderate the volume first) so you have some confidence it's not just me playing a rude trick, then close your eyes for a five count. There's going to be a major change in the sound when you get near 'five', now try to imagine how the scene changed before opening your eyes again: https://youtu.be/qnL40CbuodU?t=25 https://youtu.be/qnL40CbuodU?t=25 I think without any reference from an embodied perspective, we're asking ML systems to understand the sounds (which are also streams of scalar values that demonstrate periodicity) the same way we interpret the representation visually. (Also if you enjoyed the example above check out these two channels, some of them are mindblowing) https://www.youtube.com/user/jerobeamfenderson1 https://www.youtube.com/user/jerobeamfenderson1 https://www.youtube.com/c/ChrisAllenMusic https://www.youtube.com/c/ChrisAllenMusic And a fun video explaining it all - https://www.youtube.com/watch?v=4gibcRfp4zA https://www.youtube.com/watch?v=4gibcRfp4zA

Jeff_Brown 6y ago

After interacting with the world, we can learn from passive observation. No human has ever learned anything about causation without first spending a while experimenting in the world.