5 ms·

Time is only a symptom of what's missing: causation. ML operates with associative models of billions of parameters: trying to learn thermodynamics by parameter

Time is only a symptom of what's missing: causation. ML operates with associative models of billions of parameters: trying to learn thermodynamics by parameterizing for every molecule in a billion images of them. Animals operate with causal models of a very small number of parameters: these models richly describe how an intervention on one variable causes another to change. These models cannot be inferred from association (hence the last 500 years of science). They require direct causal intervention in the environment to see how it changes (ie., real learning). And a rich background of historical learning to interpret new observation. You need to have lived a human life to guess what a pedestrian is going to do. If you overcome the relevant computational infinities to learn "strategy" you will still only do so in the narrow horizon of a highly regulated game where causation has been eliminated by construction (ie., the space of all possible moves over the total horizon of the game can be known in an instant). The state of all possible (past, current, future) configurations of a physical system cannot be computed -- it's an infinity computational statistics will never bridge. The solution to self-driving cars will be to try and gamify the roads: robotize people so that machines can understand them. This is already happening on the internet: our behaviour made more machine-like so it can be predicted. I'm sceptical real-world behaviour can be so-constrained.

nextos 7y ago
Exactly, that's why I think we need to put logic and probability theory back into cutting edge ML. [1,2] are only early approaches that show potential directions to achieve this. Deep learning is very useful, but only one piece of the whole AGI puzzle. Furthermore, many AI problems will benefit the generality of being formulated as a probabilistic program synthesis problem [3]. In this framework, lots of program semantics (~formal methods) concepts like abstract interpretation [4,5] might become very useful. They allow to explore huge program spaces very quickly. Lastly, Pearl's do calculus [6] is a good starting point closely related to [1]. [1] http://probmods.org/ http://probmods.org/ [2] http://pyro.ai/ http://pyro.ai/ [3] https://web.mit.edu/cocosci/Papers/Science-2015-Lake-1332-8.pdf https://web.mit.edu/cocosci/Papers/Science-2015-Lake-1332-8.... [4] http://www.concrete-semantics.org/concrete-semantics.pdf http://www.concrete-semantics.org/concrete-semantics.pdf [5] http://adam.chlipala.net/frap/frap_book.pdf http://adam.chlipala.net/frap/frap_book.pdf [6] http://bayes.cs.ucla.edu/BOOK-2K/causality2-epilogue.pdf http://bayes.cs.ucla.edu/BOOK-2K/causality2-epilogue.pdf
- sjg007 7y ago
  Well you need a model of the world you can simulate. That would be where you start. We could probably program a world simulation to help the robot learn in the environment. There would be some uncertainty though between the simulation and real world as well as uncertainty in the simulated vs learned object concepts. Interesting questions is if deep learning could learn the concept of gravity?
deleted 7y ago
[deleted]
naasking 7y ago
> Animals operate with causal models of a very small number of parameters: these models richly describe how an intervention on one variable causes another to change. These models cannot be inferred from association (hence the last 500 years of science). * Tegmark and Wu's AI Physicist: https://news.ycombinator.com/item?id=18381827 https://news.ycombinator.com/item?id=18381827 * AIXI: https://en.wikipedia.org/wiki/AIXI https://en.wikipedia.org/wiki/AIXI
iandanforth 7y ago
The causal argument suffers from a problem of nomenclature. On one side we have the colloquial understanding of cause and effect where a cause is a true impetus of effect. On the other side we have "causal" learning in biology where you're not actually learning causes, just strong correlations. We can learn just about any temporal association even if there is no direct cause-effect relationship. Random reward structures are a way to illustrate this: present a reinforcing stimulus to an animal at random times and a random subset of behavior will increase in frequency. The animal develops a false "causal" belief that a series of its actions is influencing the presentation of a reward. That's why I like focusing on "sequence prediction", even colloquially we know predictions can be wrong. Those predictions can be influenced by low-d world models, but you don't accidentally elevate that model to claim a pure/symbolic/accurate model as can happen with incautious use of the words like "causal."
- perl4ever 7y ago
  You sound like an expert, but I have a really strong negative reaction to saying the issue is just nomenclature. People give lip service to "correlation is not causation" by say, well, we're not going to call our correlations causation, we're just going to use them like that. There, are your delicate sensibilities satisfied? No! Because if you have the wrong causal relationships whatever you call them used to predict stuff, the predictions will be wrong! Totally, not 1% off.
- mjburgess 7y ago
  The key element here is intervention. Animals learn by changing their environment. Superstition in pigeons arises because they believe their actions cause the reward, it isnt "mere sequence". Any distribution over two variables observed overtime, for all time can change unpredictably given an environmental change. Animals have rich models of objects and their behaviour over time, these models aren't "sequential", and they are brought to bare on deciding whether mere sequences should be regarded causally.
  - iandanforth 7y ago
    'Animals have rich models of objects and their behaviour over time, these models aren't "sequential"' This strikes me as patently false, which means I'm probably not understanding what you mean. What does forward simulation mean if it isn't sequential?
scottlegrand2 7y ago
Or you could engineer the roads and the cars to provide telemetry on where they are in high resolution in real time. This would reduce a nearly impossible job in machine intelligence to a really difficult simulation problem. But I don't see that happening anytime soon.
- blackeneth 7y ago
  We build cell towers everywhere. It’s not a stretch that we could build telemetry towers everywhere. The main problem is an economic chicken-and-egg problem: need to invest in towers before cars will be built. The one advantage is that one can be assured of success in developing a self-driving road system. Now if we could reuse cell tower signals, the telemetry system is already in place. Cell phone operators keep the location of their cell towers secret, but with some persuasion ($$$) would reveal their coordinates.
  - fbender 7y ago
    I see a huge risk for manipulation and spoofing. You‘ll have to establish some kind of trust if you want to rely on beacon data (from whatever source) for navigation and safety. Just imagine that someone spoofs a signal that triggers autonomous cars to emergency break – depending on road conditions and/or if there are non-autonomous cars as well, an attacker can create some serious damage, injury, and even death. Given the existence of spoofed base stations (stingrays) among other reasons, a PKI-based solution may not be sufficiently safe. So you‘d have to overlay beacon data with sensors, at which point it’s questionable if there‘s a significant added benefit.

nextos 7y ago

Exactly, that's why I think we need to put logic and probability theory back into cutting edge ML. [1,2] are only early approaches that show potential directions to achieve this. Deep learning is very useful, but only one piece of the whole AGI puzzle. Furthermore, many AI problems will benefit the generality of being formulated as a probabilistic program synthesis problem [3]. In this framework, lots of program semantics (~formal methods) concepts like abstract interpretation [4,5] might become very useful. They allow to explore huge program spaces very quickly. Lastly, Pearl's do calculus [6] is a good starting point closely related to [1]. [1] http://probmods.org/ http://probmods.org/ [2] http://pyro.ai/ http://pyro.ai/ [3] https://web.mit.edu/cocosci/Papers/Science-2015-Lake-1332-8.pdf https://web.mit.edu/cocosci/Papers/Science-2015-Lake-1332-8.... [4] http://www.concrete-semantics.org/concrete-semantics.pdf http://www.concrete-semantics.org/concrete-semantics.pdf [5] http://adam.chlipala.net/frap/frap_book.pdf http://adam.chlipala.net/frap/frap_book.pdf [6] http://bayes.cs.ucla.edu/BOOK-2K/causality2-epilogue.pdf http://bayes.cs.ucla.edu/BOOK-2K/causality2-epilogue.pdf

sjg007 7y ago
Well you need a model of the world you can simulate. That would be where you start. We could probably program a world simulation to help the robot learn in the environment. There would be some uncertainty though between the simulation and real world as well as uncertainty in the simulated vs learned object concepts. Interesting questions is if deep learning could learn the concept of gravity?

deleted 7y ago

[deleted]

naasking 7y ago

> Animals operate with causal models of a very small number of parameters: these models richly describe how an intervention on one variable causes another to change. These models cannot be inferred from association (hence the last 500 years of science). * Tegmark and Wu's AI Physicist: https://news.ycombinator.com/item?id=18381827 https://news.ycombinator.com/item?id=18381827 * AIXI: https://en.wikipedia.org/wiki/AIXI https://en.wikipedia.org/wiki/AIXI

iandanforth 7y ago

The causal argument suffers from a problem of nomenclature. On one side we have the colloquial understanding of cause and effect where a cause is a true impetus of effect. On the other side we have "causal" learning in biology where you're not actually learning causes, just strong correlations. We can learn just about any temporal association even if there is no direct cause-effect relationship. Random reward structures are a way to illustrate this: present a reinforcing stimulus to an animal at random times and a random subset of behavior will increase in frequency. The animal develops a false "causal" belief that a series of its actions is influencing the presentation of a reward. That's why I like focusing on "sequence prediction", even colloquially we know predictions can be wrong. Those predictions can be influenced by low-d world models, but you don't accidentally elevate that model to claim a pure/symbolic/accurate model as can happen with incautious use of the words like "causal."

perl4ever 7y ago
You sound like an expert, but I have a really strong negative reaction to saying the issue is just nomenclature. People give lip service to "correlation is not causation" by say, well, we're not going to call our correlations causation, we're just going to use them like that. There, are your delicate sensibilities satisfied? No! Because if you have the wrong causal relationships whatever you call them used to predict stuff, the predictions will be wrong! Totally, not 1% off.
mjburgess 7y ago
The key element here is intervention. Animals learn by changing their environment. Superstition in pigeons arises because they believe their actions cause the reward, it isnt "mere sequence". Any distribution over two variables observed overtime, for all time can change unpredictably given an environmental change. Animals have rich models of objects and their behaviour over time, these models aren't "sequential", and they are brought to bare on deciding whether mere sequences should be regarded causally.
- iandanforth 7y ago
  'Animals have rich models of objects and their behaviour over time, these models aren't "sequential"' This strikes me as patently false, which means I'm probably not understanding what you mean. What does forward simulation mean if it isn't sequential?

scottlegrand2 7y ago

Or you could engineer the roads and the cars to provide telemetry on where they are in high resolution in real time. This would reduce a nearly impossible job in machine intelligence to a really difficult simulation problem. But I don't see that happening anytime soon.

blackeneth 7y ago
We build cell towers everywhere. It’s not a stretch that we could build telemetry towers everywhere. The main problem is an economic chicken-and-egg problem: need to invest in towers before cars will be built. The one advantage is that one can be assured of success in developing a self-driving road system. Now if we could reuse cell tower signals, the telemetry system is already in place. Cell phone operators keep the location of their cell towers secret, but with some persuasion ($$$) would reveal their coordinates.
- fbender 7y ago
  I see a huge risk for manipulation and spoofing. You‘ll have to establish some kind of trust if you want to rely on beacon data (from whatever source) for navigation and safety. Just imagine that someone spoofs a signal that triggers autonomous cars to emergency break – depending on road conditions and/or if there are non-autonomous cars as well, an attacker can create some serious damage, injury, and even death. Given the existence of spoofed base stations (stingrays) among other reasons, a PKI-based solution may not be sufficiently safe. So you‘d have to overlay beacon data with sensors, at which point it’s questionable if there‘s a significant added benefit.