Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

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

wired.com

Post reply on HN
Print view View original

91–100 of 111 posts

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

#91

Post by ss2003 »

nuguy wrote: post #22

This article is a complete waste. The title implies that it’s about ai but it turns out to be a portrait of a mans life — a pr piece. Not only that but free energy minimization has nothing to do with intelligence other than vaguely describing one of its most obvious and superficial characteristics. —- Ai is the most important issue in the world. True general ai is an existential threat to human kind. The economics of…

I agree with most of what you state, but the prohibition of AI is impossible. How could you stop nations from researching it secretly? How could you stop the Amazons and Baidus?

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

#92

Post by josepi_ »

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.

Man, I can barely understand any of this. Is there a more ELI5 type explanation anywhere?

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

#93

Post by dr_dshiv »

poslathian wrote: post #87
calebm wrote: post #69

This almost sounds like a special case of Jeremy England's dissipation-driven adaptation theory. Does anyone know the overlap/differences between these theories (other than specificity)?

Ctrl-F England and here you are. I’ve been searching for someone more informed than me that has compared and contrasted the two but haven’t found anything.

Second. Check out David Bohm's idea of wholeness and harmony, in "on creativity." Not what you are looking for, but another puzzle piece with the same scent

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

#96

Post by sixdimensional »

ArtWomb wrote: post #78

Most grad-level Deep Learning classes have a week or so devoted to "Approximate Bayes" methods. And it's conceivable future updates to all popular probabilistic programming languages will include "programmable" rather than "fixed-function" inference methods. "Inference Metaprogramming" paper https://people.csail.mit.edu/rinard/paper/pldi18.pdf Latest state-of-the-art research will be presented at upcoming NeuroIPS co…

Since you mention Bayesian methods, I thought I may randomly ask you - have you come across any good work about applications of subjective Bayesian statistics in AI?

I was particularly interested in subjective Bayes theory due to the way it seems to interleave human input with mathematical theory.

I first learned about it from a non-fiction book in which these techniques were used by scientists in the US to locate Russian ICBMs that were test-fired during the Cold War and landed in the ocean. The wisdom of experts was quantified and fed into a simple Bayesian subjective probability calculation which lead to prioritization of target areas to investigate and the US located on either the first or second try - I can't recall. I've seen a few other interesting applications of this as well.

I'm not an expert in this area, but you sound like you might be - so I thought I'd take the change to ask :)

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

#97

Post by lurquer »

klodolph wrote: post #77
lurquer wrote: post #75

Earlier quoted context omitted.

It may be a fallacy to assume a neuron is less complex than a brain. Depends on how one measures complexity and at what scale... but living systems -- unlike no living systems -- strangely get more complex the closer in one goes. That is, it's fairly trivial to simulate an earthworm... it's trickier to simulate the components of the earthworm.

This is a completely minor point here... but "fallacy" means that there's something wrong with the argument, if you have a disagreement about facts or assumptions then the word "fallacy" doesn't really apply (you can just say "wrong" instead).

It suffers from the fallacy of petitio principii, in that it assumes arguendo that consciousness is comprised of neurons (and, as mentioned above, that a neuron is less complex than consciousness.) But it's not stated as an 'argument' in any case so perhaps the term fallacy was out-of-place.

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

#98

Post by paraschopra »

snrji wrote: post #85
paraschopra wrote: post #50

Earlier quoted context omitted.

There are similarities. The difference in two approaches are: - FEP is Bayesian in nature, while there's usually no notion of uncertainties in curiosity driven RL - In FEP, there's no explicit weighting of explore/exploit tradeoff. It automatically emerges from equations - FEP, since it's Bayesian, allows for more complex reasoning (like counterfactuals) - Curiosity driven RL is scalable while FEP is not feasible for…

Excuse me, another followup question (can't edit on mobile): can you ELI5 how do exploitation and exploration "emerge" naturally instead of the tradeoff being explicitly coded as in RL?

In forumations of FEP, there are two terms: cost and ambiguity. Minimisation of this combined term happens in a Bayesian optimal way. So you don't have to explicitly code weights for exploration and exploitation.

Although what you do have to code is prior preferences, and since it is a distribution, you implicitly code the range of those preferences. But once you do that the FEP, algorithm figures out when to collect more data to build a better model and when to use the existing model to get near the prior preferences.

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

#99

Post by conjectures »

atschantz wrote: post #44
conjectures wrote: post #29

Anyone have a reference relating the free energy minimisation principle / active inference to reinforcement learning type environments?

The particular study cited in the article is [1], however for a more general review of the links to reinforcement learning [2]. [1] https://www.biologicalpsychiatrycnni.org/article/S2451-9022(... [2] https://journals.plos.org/plosone/article?id=10.1371/journal...

Cheers.

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

#100

Post by atschantz »

snrji wrote: post #85
paraschopra wrote: post #50

Earlier quoted context omitted.

There are similarities. The difference in two approaches are: - FEP is Bayesian in nature, while there's usually no notion of uncertainties in curiosity driven RL - In FEP, there's no explicit weighting of explore/exploit tradeoff. It automatically emerges from equations - FEP, since it's Bayesian, allows for more complex reasoning (like counterfactuals) - Curiosity driven RL is scalable while FEP is not feasible for…

Excuse me, another followup question (can't edit on mobile): can you ELI5 how do exploitation and exploration "emerge" naturally instead of the tradeoff being explicitly coded as in RL?

As a general answer, the theory suggests that organisms maximize a quantity known as model evidence, which is just a way of saying 'how much evidence does some data provide for my model of the world?'

There are two complementary ways to maximize this - change your model or change your world.

If we now grant that actions also maximize model evidence, then actions can either be conducted to sample data that make the model a better fit of the data (exploration), or they can be conducted to sample observations that are consistent with the current model (exploitation).

Post reply on HN