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Yann LeCun's comment on AlphaGo and true AI

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

#201

Post by viraptor »

megaman821 wrote: post #85
viraptor wrote: post #41

Earlier quoted context omitted.

I'm not sure why you connect that behaviour to empathy. There are two simple rules here that apply to almost every possible situation, as well as the one you presented. They aren't even connected to human emotions. That's pure economy. 1. Minimise work (plugging into socket has lower cost / effort than what you described) 2. Minimise irreversible changes (or cost of reversing them) There are so many people with low e…

These rules cannot be applied in every situation. Then you back to writing a bunch of rules. If you told a general AI to take care of a puppy for a few days, it would end up putting a diaper on it and keeping it in a crate 24 hours a day. That would be the least amount of work to take care of the puppy, and minimize the chance of the puppy hurting itself or damaging anything else.

This is close to what happens when you leave your pet at a cheap pet hotel though. General purpose humans do it the same way.

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

#202

Post by jacquesm »

zardo wrote: post #199
jacquesm wrote: post #140

Earlier quoted context omitted.

> the 'problem' of Go is not nearly solved. And it is very likely that it will never be, the number of combinations is simply too large.

That doesn't (necessarily) mean a proof can't be found that a certain ruleset leads to optimal play. Tic-tac-toe can be solved without examing the entire state space of the game.

That's mostly because of trivial symmetry and even taking that into account Go space is in the most literal sense astronomic, simple numbers don't work in describing just how large it is.

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

#203

Post by wrp »

gracenotes wrote: post #83
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,…

I took a closer read through the AlphaGo paper today. There are some other features that make it not general. In particular, the initial input to the neural networks is a 19×19×48 grid, and the layers of this grid include information like: - How many turns since a move was played - Number of liberties (empty adjacent points) - How many opponent stones would be captured - How many of own stones would be captured - Num…

I'm having trouble identifying what algorithmic innovation AlphaGo represents. It looks like a case of more_data + more_hardware. Some are making a big deal of the separate evaluation and policy networks. So, OK, you have an ensemble classifier.

The most theoretically interesting thing to me is the use of stochastic sampling to reduce the search space. Is there any discussion of how well pure Monte Carlo tree search performs here compared to the system incorporating extensive domain knowledge?

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

#204

Post by mannykannot »

sago wrote: post #133
mannykannot wrote: post #132

Earlier quoted context omitted.

As I have said elsewhere, I will agree we have achieved true AI when a program, uncoached, creates a persuasive argument that it is intelligent. One nice feature of this test is that you don't have to define intelligence precisely beforehand. Of course, that does not give a specification for developers to work to, but that is the case we have now, anyway.

Nice to know your criteria. There have been many. The trick is not to decide when you'll 'agree' (whatever your agreement is worth), but to form a consensus in the discussion. I've known people who've been unable to create a persuasive argument that they are intelligent (or unwilling), and I've know intelligent dogs unable to argue for anything, persuasive or not. I don't fancy your chances of having your definition…

The possible downside to appealing to consensus is that it doesn't always agree with you. That doesn't bother me in the slightest, but perhaps that's because I think people will recognize AI when it shows up.

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

#205

Post by solipsism »

diskcat wrote: post #47
megaman821 wrote: post #32

It seems to me one of the higher hurdles for creating a general purpose intelligence, is human empathy. Without it you are left with creating a nearly infinite-length rules engine. When you ask your AI maid to vacuum your house, you would prefer it not to plow through closet door to grab the vacuum, rip your battery out of your car and hardwire to the vacuum, and then proceed to clean your carpets. If you don't want…

i don't think empathy is an appropriate word for this. It is defined as the ability to share the feelings of others. While in this case, what you want the robots to know is what most people would call 'common sense'.

What a strange response. You read that as me comparing human capabilities to roomba's?

The point, which you couldn't have missed more blatantly, is that one doesn't need to program every conceivable situation into an automaton for it to be able to behave properly in a whole range of normal situations.

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

#206

Post by calvins »

hacknat wrote: post #18
quonn wrote: post #16

Earlier quoted context omitted.

Well, it just seems unlikely that psychology will produce significant insights here, because it mostly just looks at how the brain behaves. While this can be insightful, I doubt that it will explain intelligence in the end, because this happens a layer below. That's precisely what neuroscience covers. The other approach (just thinking about the problem and trying to to build a AI from first principles) is CS. So I wo…

I think someone needs to come up with a good theory of what intelligence even is, then we can try to discover its mechanism(s).

Shane Legg (a DeepMind founder) and Marcus Hutter collected and categorized 70 different definitions of intelligence: http://arxiv.org/abs/0706.3639

Their attempt at a definition that synthesizes all the others is:

> Intelligence measures an agent's ability to achieve goals in a wide range of environments. - S. Legg and M. Hutter

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

#207

Post by Natsu »

SilasX wrote: post #91
Natsu wrote: post #86

Earlier quoted context omitted.

I want to see an AI that can improve itself by developing new algorithms for arbitrary tasks. I wonder how far off we are from that now?

You know, if you're at the point where you can give a human-readable spec of the problem and the AI can make a passable attempt at it, that's basically the Turing Test -- hence why I think it deserves its status as holy grail. Something that passes would really give the impression of "there's a ghost inside here".

Rather than a ghost, I wonder if we'll ever have the average person looking at brains and thinking "there's a program inside here."

And then to reverse it, imagine that the world really is some kind of massive simulation... and that there are backups of the save()-ed :)

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

#208

Post by svalorzen »

Smerity wrote: post #166
svalorzen wrote: post #110

Earlier quoted context omitted.

- 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 o…

Thanks for the good discussion :) + The issue with MCTS was not that it couldn't be extended to non-determinism (you're correct re: POMCP) but that it requires a simulator which produces at least a reasonably accurate model of the world. This simulator is almost always hand engineered. Determinism and perfect information simplify both the task and the creation of the simulator. The state in Go also contains all histo…

Simulators should be fast, sure, however that they should be deterministic to be fast is something I do not believe. We have millions of programs already that make use of pseudo random generated numbers, and they don't seem to be suffering performance problems because of that.

And about the state and memory concern, mcts does not care directly about it, since for it the simulator is used as a black box and its internals are irrelevant to it. Instead, as long as any environment configuration can be described in terms of state (essentially a unique state->number conversion must be possible - and even then not always) mcts will work. And since it also does not care about the size of the state space, the concern that having memory as one of the factors in the state would be problematic is also unfounded.

I also disagree on the specificity of AlphaGO. Mcts has been used successfully in many fields after its initial usage and tuning for Go. I did my thesis on similar algorithms. In the same way, it does not matter whether AlphaGO can be directly used on other problems. What matters is the new idea of using NNs in order to improve substantially and with little overhead the value estimations used by mcts to explore the decision tree. This is the true breakthrough. The fact that the first implementation of this idea is a Go playing program is irrelevant, it's more like a showcase of the goodness of the approach.

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

#209

Post by sakopov »

hughperkins wrote: post #101
sakopov wrote: post #11

Earlier quoted context omitted.

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.

Ah, yes. This's a great example. I see what you're saying.

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

#210

Post by nicops »

jimbokun wrote: post #150
nicops wrote: post #145

Earlier quoted context omitted.

It's progress, very neat progress, but it's not even progress in the sense that there are new kind of problems being solved. AI for Go and similar games has existed for a zillion times, but, given the huge space of the game of Go, it had never been that good at playing it as to beat a human. Not to diminish that this isn't an achievement, but let's put it into context. To talk about being closer to AGI, a different k…

"AI solving different -new- kinds of problems." Which of course, it does. Do you honestly believe Go and other games are the only AI problems where progress is being made?

ok, but we were talking about alphaGo here, and what does it represent as an example of progress in AI.
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