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

Yann LeCun's comment on AlphaGo and true AI

facebook.com

Post reply on HN
Print view View original

101–110 of 216 posts

Re: Yann LeCun's comment on AlphaGo and true AI

#101

Post by hughperkins »

sakopov wrote: post #11
kailuowang wrote: post #9

Can someone elaborate the difference between reinforcement learning and unsupervised learning? It seems that I mistakenly think that human learns through reinforcement learning, that we learn by the feedback from the outside world. I mean without feedback from aldult can a baby even learn how to walk?

Exactly what i was going to ask! Is there such a thing as unsupervised learning? A human mind is constantly fed a stream of learning data. And while some things may be predispositioned by our DNA, I think for the most part we learn directly from the rules and guidelines dictated by our parents first and our society later.

So, its like, when I learned salsa dancing, i couldnt hear the 'beat' of the music, and was always out of time. So what I did was just listen to salsa music whilst cycling and walking for a couple of weeks. Now i could hear the 'beat', and lots more structure in the music to boot.

Re: Yann LeCun's comment on AlphaGo and true AI

#102

Post by dilemma »

chrisfosterelli wrote: post #12
kailuowang wrote: post #9

Can someone elaborate the difference between reinforcement learning and unsupervised learning? It seems that I mistakenly think that human learns through reinforcement learning, that we learn by the feedback from the outside world. I mean without feedback from aldult can a baby even learn how to walk?

Not an expert, but my understanding is that humans can learn many things through reinforcement learning, but most of our intelligent decisions are a result of unsupervised learning. For instance, if the stove element was red hot and you touched it, you'd receive a feeling of pain. Reinforcement learning would suggest that you shouldn't do this again, and you might learn to not touch stove elements when they are red h…

Computers follow instructions written for them. Humans write their own instructions (and can even disregard instructions).

Re: Yann LeCun's comment on AlphaGo and true AI

#103

Post by gracenotes »

yoodenvranx wrote: post #100
gracenotes wrote: post #93

Earlier quoted context omitted.

Interesting. I have heard the opposite of this. Supervised learning may be how it looks from the outside, but consider that out of the >6,570,0000 waking seconds of a child's life up to age 5, there maybe only a few dozen instances of supervised adult instruction per day. Besides those, what do neurons do the remaining 99.99% of the time? Part of the problem might be that comparing supervised and unsupervised learnin…

> there maybe only a few dozen instances of supervised adult instruction per day There might be only a few dozen instances but I think each instance has a lasting effect which makes up for this. If you scold a child for something stupid it did then it will remember this for a long-ish time. Same for teaching him things or correcting stuff. I guess you show the child some correct behaviour at a few instances and this…

That is a great point. Talking about supervised vs unsupervised vs reinforcement learning is most straightforward with tasks like language learning, audio processing, image processing, and playing discrete games. It is possible to see broad similarities between deep learning approaches and human cognition for several of these tasks. But when you start getting into tasks like the formation of narrative identity, things get very complicated.

Maybe one major difference between playing a game and forming a personality is that these early important interactions don't just adjust wirings in the cerebral cortex, the part of the brain most responsible for general intelligence. It goes straight to our emotional memory bank in the limbic system, which is all about learning an incredibly important objective function: to survive. But very high level features formed by unsupervised learning can do this, not just reptilian predator detection routines. Being scolded or corrected can have a powerful effect on future motivation. Suffice to say, artificial intelligences don't currently worry about this.

Re: Yann LeCun's comment on AlphaGo and true AI

#104

Post by scrollaway »

jacquesm wrote: post #44
argonaut wrote: post #33

Earlier quoted context omitted.

This analogy doesn't make any sense. Since automobiles were never patterned off anything in biology (they were the improvement of horse drawn carriages), I'm not sure what point you're trying to make with the analogy. I never said there weren't any developments in neural nets, I'm just saying that few ideas have been taken from neuroscience (there certainly have been some ideas, like convolutions). In fact most thing…

All it took for us to make aircraft was to look at birds and to realize something could be done (flying even though you weigh more than the air). The first wanna-be aircraft looked like birds, and some even attempted to work like them. Most of those designs went nowhere. Just like legs got replaced by the wheel in ground transportation aircraft engines and eventually jets replaced the muscles of birds. It doesn't rea…

> It doesn't really matter what sets you on a particular part, what matters is the end result.

To put it in other words: Birds are limited by evolution. They are not an optimal design - they are a successful reproductive design in a wide ecosystem where flying is a tool.

Our intelligence is no different.

This is something Feynman addressed in this beautiful talk (in the Q&A iirc): https://www.youtube.com/watch?v=EKWGGDXe5MA

Re: Yann LeCun's comment on AlphaGo and true AI

#105

Post by paganel »

argonaut wrote: post #10
jacquesm wrote: post #6

Earlier quoted context omitted.

> I know this will be controversial, psychology Why would that be controversial? It seems to make extremely good sense and even though it may be doubted in some circles I think that most people involved in AI research are painfully aware of our limited understanding of our own psyche.

I disagree with hacknat (and the reason I suspect it's controversial) because psychology is generally far too high-level (e.g. focused on things like traits and high-level behavior and vague "perceptions") and has far too little empirical rigor for engineers to be able to build any sort of actual system from insights gleaned from psychology. The recent replication crisis in psychology does little to help this reputat…

> and has far too little empirical rigor for engineers

I think this is one of the key points of the problem: we, as engineers, are expecting this problem (imitating the human psyche/getting to true AI) to be able to be defined rigorously. What if it can't?

I'm not a religious person and I don't generally believe in something inside us that it's "undefinable", but looking at our cultural and intellectual history of about 2,500 years I can see that there are lots of things that we we haven't been able to define rigorously, but which are quintessential to human psyche: poetry, telling jokes, word puns, the sentiment of nostalgia and I could go on and on.

Re: Yann LeCun's comment on AlphaGo and true AI

#106

Post by sago »

Ah, the joys of arguing about artificial intelligence without ever defining intelligence.

It is the perfect argument, everyone can forcefully make their points forever, and we'll be none the wiser whether this AI is 'true AI' or not.

Re: Yann LeCun's comment on AlphaGo and true AI

#108

Post by tarsinge »

Smerity wrote: post #56

Preface: AlphaGo is an amazing achievement and does show an interesting advancement in the field. Yet ... it really doesn't mean almost anything that people are predicting it to mean. Slashdot went so far as to say that "We know now that we don't need any big new breakthroughs to get to true AI". The field of ML/AI is in a fight where people want more science fiction than scientific reality. Science fiction is sexy,…

It is a major AI breakthrough, but not the sci-fi like. AlphaGo is a true AI for Go in the sense that it develops intuition and anticipation by evaluating probabilities, same as any human player. A Go-specific philosophical zombie in short. And when we will have superhuman philosophical zombies for each problem, what will be the point of a general AI?

Edit: typo

Re: Yann LeCun's comment on AlphaGo and true AI

#109

Post by kzhahou »

emmab wrote: post #94
drdeca wrote: post #63

Earlier quoted context omitted.

I thought full AIXI wasn't computable though?

You can do time-bound AIXI and for a large enough time bound it's sufficient for all practical situations. AIXI is not tractable, but I'm responding to the parent comment saying we don't even know what a cake is.

I don't consider this mathematical model of induction/prediction to be an accurate description of "intelligence" in the sense of "True AI" (which itself is open to interpretation).

This is the cake: human intelligence. Right now we have pieces of it, but even the end goal isn't well defined. We know the human mind makes predictions, recognizes patterns, can formulate plans, works with both concrete and fuzzy information, and so on. But we still don't understand what human intelligence really is, overall.

Re: Yann LeCun's comment on AlphaGo and true AI

#110

Post by svalorzen »

Smerity wrote: post #56

Preface: AlphaGo is an amazing achievement and does show an interesting advancement in the field. Yet ... it really doesn't mean almost anything that people are predicting it to mean. Slashdot went so far as to say that "We know now that we don't need any big new breakthroughs to get to true AI". The field of ML/AI is in a fight where people want more science fiction than scientific reality. Science fiction is sexy,…

- As all problems can be converted to Markov Decision Processes, this is a moot point. The transition may not be efficient in terms of states/actions, but since we just nearly solved a problem with more states than the atoms of the universe, this seems to be a moot point. In addition, most problems for humans are actually in the form of {state,action}. Just because now ML is popular and it's all about putting label on data does not make this less true. Other algorithms already exist (POMCP) which are direct transformations of MCTS for uncertain environments. Determinism is not needed both for MCTS nor for POMCP, so that point is void too.

- I don't see what the problem is with that. Nearly all tasks humans would currently gain in getting automated have plenty of human experience before it. This is why training in any field even exists. Sure it would be cool to send a drone in unexplored territory with no knowledge and come back later to a self-built city, but I don't see how putting that milestone a bit later is in any way a problem.

- The hardware requirements for any new interesting breakthrough to do something important have always been immense. Just as for Deep Blue, or to all the graphite experiments where we can only produce just milligrams of it at a time, or to solar panel, each new discovery innovates in a specific direction. It is just a matter of time before the new method is substantially improved and will be able to run on single machines. To expect otherwise is foolish, to complain about it is pointless.

EDIT:

This is not to contradict the general point. That we don't need new breakthroughs is utterly false in so many ways it's not even funny. However this certainly is an AI breakthough, one that many AI researchers (myself included) thought it would take AT LEAST 10 years to come to pass. Breaking 10 years in 1 seems like a breakthrough to me.

Post reply on HN