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Karl Friston: a neuroscientist who might hold the key to true AI

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Re: Karl Friston: a neuroscientist who might hold the key to true AI

#51

Post by atschantz »

paraschopra wrote: post #47

For anyone who is interested in a tutorial and actual implementation of active inference (an idea based on Free Energy Principle), here's one in Python https://kaiu.me/2017/07/11/introducing-the-deep-active-infer... I have been trying to understand FEP, and so far my understanding is that essentially the agent tries to learn the generative model that most closely explains observations and then tries to act in a way t…

The idea is that you learn a model by calculating the derivative of free energy with respect to your model parameters.

Re: Karl Friston: a neuroscientist who might hold the key to true AI

#52

Post by eli_gottlieb »

atschantz wrote: post #43
sydd wrote: post #19

Earlier quoted context omitted.

Thanks, it was a great read! From what I get this whole thing is more like an abstract ruleset describing how decision making in the brain works, rather than a brain model. Or am I wrong, is there anyone who built a network model based on this theory?

In terms of the free energy 'principle', it makes no predictions about how free energy minimized. But there have been multiple process theories suggested, most notably predictive coding (which is a dominant paradigm in neuroscience) [1] and variational message passing [2]. [1] https://en.wikipedia.org/wiki/Predictive_coding [2] http://www.jmlr.org/papers/volume6/winn05a/winn05a.pdf

Isn't variational message-passing the algorithmic-level theory about where predictive coding comes from?

Re: Karl Friston: a neuroscientist who might hold the key to true AI

#53

Post by paraschopra »

eli_gottlieb wrote: post #48
paraschopra wrote: post #47

For anyone who is interested in a tutorial and actual implementation of active inference (an idea based on Free Energy Principle), here's one in Python https://kaiu.me/2017/07/11/introducing-the-deep-active-infer... I have been trying to understand FEP, and so far my understanding is that essentially the agent tries to learn the generative model that most closely explains observations and then tries to act in a way t…

>True AI will build model from the scratch, and not just learn model complexity. There's no such thing as truly learning "from scratch" -- the No Free Lunch Theorem holds no matter what. What you can do is find a sufficiently large (ex: Turing-complete) hypothesis class, and make simplifying assumptions to allow it to be feasibly learnable (such as regularization or priors).

I'm not sure I follow you.

I didn't mean intelligence in any abstract problem-space. I meant intelligence in the world and type of problems we humans deal with (in fact, I'm unsure what process should we call intelligence in non-human context).

In the context I'm talking about, we at least have one algorithm that has build models from the scratch: evolution by natural selection.

Re: Karl Friston: a neuroscientist who might hold the key to true AI

#55

Post by paraschopra »

atschantz wrote: post #51
paraschopra wrote: post #47

For anyone who is interested in a tutorial and actual implementation of active inference (an idea based on Free Energy Principle), here's one in Python https://kaiu.me/2017/07/11/introducing-the-deep-active-infer... I have been trying to understand FEP, and so far my understanding is that essentially the agent tries to learn the generative model that most closely explains observations and then tries to act in a way t…

The idea is that you learn a model by calculating the derivative of free energy with respect to your model parameters.

Yes, but you have to specify a generative model (or at least put boundaries to it). Then you learn params of that model.

I was talking about learning the model structure also.

Re: Karl Friston: a neuroscientist who might hold the key to true AI

#56

Post by eli_gottlieb »

atschantz wrote: post #40
currymj wrote: post #30

As far as I can tell the “free energy principle” is just asserting that the brain is approximately Bayesian and is doing some kind of variational inference, right? I’m not sure how revolutionary that is. (I’m predisposed not to like Friston because his work in fMRI plays fast and loose with the idea of “causality”.)

The 'revolutionary' aspect is the suggestion that a single celled organism is also doing variational inference. Or, more accurately, can be described as such.

The trouble is hitting the right "happy medium" between (variational) inference as an explanation of the sensory and motor cortices, and variational inference as a universal theory of everything.

Re: Karl Friston: a neuroscientist who might hold the key to true AI

#57

Post by atschantz »

paraschopra wrote: post #55
atschantz wrote: post #51

Earlier quoted context omitted.

The idea is that you learn a model by calculating the derivative of free energy with respect to your model parameters.

Yes, but you have to specify a generative model (or at least put boundaries to it). Then you learn params of that model. I was talking about learning the model structure also.

Some attempts have been made in the form of Bayesian model reduction [1].

The idea is to 'carve' out the structure of your model using free energy minimization.

[1] https://arxiv.org/abs/1805.07092

Re: Karl Friston: a neuroscientist who might hold the key to true AI

#58

Post by antidesitter »

eli_gottlieb wrote: post #48
paraschopra wrote: post #47

For anyone who is interested in a tutorial and actual implementation of active inference (an idea based on Free Energy Principle), here's one in Python https://kaiu.me/2017/07/11/introducing-the-deep-active-infer... I have been trying to understand FEP, and so far my understanding is that essentially the agent tries to learn the generative model that most closely explains observations and then tries to act in a way t…

>True AI will build model from the scratch, and not just learn model complexity. There's no such thing as truly learning "from scratch" -- the No Free Lunch Theorem holds no matter what. What you can do is find a sufficiently large (ex: Turing-complete) hypothesis class, and make simplifying assumptions to allow it to be feasibly learnable (such as regularization or priors).

The No Free Lunch Theorem is irrelevant to the real world [0][1]. It assumes all functions, even those with infinite algorithmic complexity, are equally likely.

You should look into algorithmic probability for a better foundation.

[0] http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.540....

[1] https://arxiv.org/abs/1111.3846.pdf

Re: Karl Friston: a neuroscientist who might hold the key to true AI

#59

Post by [deleted] »

paraschopra wrote: post #53
eli_gottlieb wrote: post #48

Earlier quoted context omitted.

>True AI will build model from the scratch, and not just learn model complexity. There's no such thing as truly learning "from scratch" -- the No Free Lunch Theorem holds no matter what. What you can do is find a sufficiently large (ex: Turing-complete) hypothesis class, and make simplifying assumptions to allow it to be feasibly learnable (such as regularization or priors).

I'm not sure I follow you. I didn't mean intelligence in any abstract problem-space. I meant intelligence in the world and type of problems we humans deal with (in fact, I'm unsure what process should we call intelligence in non-human context). In the context I'm talking about, we at least have one algorithm that has build models from the scratch: evolution by natural selection.

[deleted]

Re: Karl Friston: a neuroscientist who might hold the key to true AI

#60

Post by atschantz »

eli_gottlieb wrote: post #52
atschantz wrote: post #43

Earlier quoted context omitted.

In terms of the free energy 'principle', it makes no predictions about how free energy minimized. But there have been multiple process theories suggested, most notably predictive coding (which is a dominant paradigm in neuroscience) [1] and variational message passing [2]. [1] https://en.wikipedia.org/wiki/Predictive_coding [2] http://www.jmlr.org/papers/volume6/winn05a/winn05a.pdf

Isn't variational message-passing the algorithmic-level theory about where predictive coding comes from?

I think you might be right, a quote from Friston on the relationship (in reference to belief propagation):

"We turn to the equivalent message passing for continuous variables, which transpires to be predictive coding [...]"

It could be that belief propagation is in the context of discrete variables, whereas predictive coding is in the context of continuous, both of which are a form of (variational) message passing.

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