Yann LeCun's comment on AlphaGo and true AI • Hacker News Board

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

#81

Post by megaman821 » Mon, Mar 14, 2016, 6:09 AM UTC

solipsism wrote: post #42

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…

If you don't want to create a list of rules for every conceivable situation, the AI will need to have some understanding human emotions and desires My roomba does a good job vacuuming around table legs. Also sofa legs. Also stationary human legs. Also lamps. Also random poles sticking out of the floor. I imagine it would even do a good job vacuuming around a stalactite that made it to the floor. Are you saying someon…

There is a common situation that a Roomba does horribly at, toys on the floor that a person would just pick up or move before vacuuming.

Also, Roombas don't have any capacity to learn. They are just executing their bump and clean algorithm (except for the newest one which actually maps out rooms).

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

#82

Post by rdlecler1 » Mon, Mar 14, 2016, 6:13 AM UTC

We keep trying to engineer AI rather than reverse engineering it. The thing with living organisms is that the neural network underlying the intelligence of living organisms is a product of evolutionary design of an organism situated in the real physical world with laws of physics and space and time. This is where the bootstrapping comes in. Unsupervised learning is built on top of this. Trying to sidestep this could prove difficult to get to General AI.

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

#83

Post by gracenotes » Mon, Mar 14, 2016, 6:14 AM UTC

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

- Number of liberties after this move is played

- Whether a move at this point is a successful ladder capture

- Whether a move at this point is a successful ladder escape

- Whether a move is legal and does not fill its own eyes

Again, before the neural nets even get involved. Some of these layers are repeated 8 times for symmetry. I would say for some of these, AlphaGo got some domain-specific help in a non-general way.

It is of course still groundbreaking academically. The architecture is a state-of-the-art deep learning setup and we learned a ton about how Go and games in general work. The interaction between supervised and reinforcement learning was interesting, especially how the latter behaved worse in practice in selecting most likely moves.

disclaim: Googler, not in anything AI.

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

#84

Post by dsharlet » Mon, Mar 14, 2016, 6:14 AM UTC

Smerity wrote: post #73

dsharlet wrote: post #67

Earlier quoted context omitted.

> The hardware requirements were stunning (280 GPUs and 1920 CPUs for the largest variant) and were an integral part to how well AlphaGo performed Is that really true? Demis stated that the distributed version of AlphaGo beats a single machine version only 75% of the time. That's still stronger than virtually all human players, and probably would still beat Lee Sedol at least once.

The best discussion in terms of hardware adjusted algorithmic process I've seen is by Miles Brundage[1]. The additional hardware was the difference between amateur and pro level skills. It's also important to note that the non-distributed AlphaGo still had quite considerable compute power. Now was a sweet spot in time. This algorithm ten years ago would likely have been pathetic and in ten years time will likely supe…

I don't really buy his argument. Lots of other companies with plenty of resources have been attacking this problem, including Facebook and Baidu. People have been talking about Go AIs for decades. If it was just a matter of throwing a few servers at Crazy Stone or another known Go algorithm, it would have been done already.

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

#85

Post by megaman821 » Mon, Mar 14, 2016, 6:16 AM UTC

viraptor wrote: post #41

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'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.

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

#86

Post by Natsu » Mon, Mar 14, 2016, 6:24 AM UTC

ktRolster wrote: post #78

grumpy-buffalo wrote: post #48

I wish the term "true AI" were replaced with "strong AI" or "artificial general intelligence" or some such term. We already have true AI - it's a vast, thriving industry. AlphaGo is obviously a true, legitimate, actual, real, nonfictional example of artificial intelligence, as are Google Search, the Facebook Newsfeed, Siri, the Amazon Echo, etc.

We already have true AI - it's a vast, thriving industry. Or how about calling that vast, thriving industry "weak AI," or "clever algorithms," which is what they really are. The original definition of AI was what we now call strong AI, but after some lesser problems were solved without actually creating strong AI, we had to come up with some name for those.

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?

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

#87

Post by sytelus » Mon, Mar 14, 2016, 6:24 AM UTC

If you look at how child learns, it's huge amount of supervised learning. Parents spend lots of time in do and don't and giving specific instructions on everything from how to use toilet to how to construct a correct sentence. Lots of language development, object identification, pattern matching, comprehension, math skills, motor skills, developing logic - these activities has huge amount of supervised training that runs day after day and year after year. There is sure unsupervised elements like ability to recognize phonemes in speech, tracking objects, inference despite of occlusion, ability to stand up and walk, make meaningful sounds, identify faces, construct sequence of actions to achieve goal, avoiding safety risks from past experiences and so on. However, typical child goes through unparalleled amount of supervised learning. There was an incidence of a child who got locked up in a room for over a decade and she didn't developed most of the language, speech or social skills. It seems unsupervised learning can't be all of the cake.

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

#88

Post by Smerity » Mon, Mar 14, 2016, 6:26 AM UTC

dsharlet wrote: post #84

Smerity wrote: post #73

Earlier quoted context omitted.

The best discussion in terms of hardware adjusted algorithmic process I've seen is by Miles Brundage[1]. The additional hardware was the difference between amateur and pro level skills. It's also important to note that the non-distributed AlphaGo still had quite considerable compute power. Now was a sweet spot in time. This algorithm ten years ago would likely have been pathetic and in ten years time will likely supe…

I don't really buy his argument. Lots of other companies with plenty of resources have been attacking this problem, including Facebook and Baidu. People have been talking about Go AIs for decades. If it was just a matter of throwing a few servers at Crazy Stone or another known Go algorithm, it would have been done already.

The companies may have plenty of resources but those resources were not solely dedicated to this problem. You mention Facebook and they were indeed on the verge of putting time into this with - though their team is far smaller (1-2 people) and they still used less compute resources. From the linked Miles article:

"Facebook’s darkfmcts3 is the only version I know of that definitely uses GPUs, and it uses 64 GPUs in the biggest version and 8 CPUs (so, more GPUs than single machine AlphaGo, but fewer CPUs). ... Darkfmcts3 achieved a solid 5d ranking, a 2-3 dan improvement over where it was just a few months earlier..."

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

#89

Post by sytelus » Mon, Mar 14, 2016, 6:37 AM UTC

grumpy-buffalo wrote: post #48

I wish the term "true AI" were replaced with "strong AI" or "artificial general intelligence" or some such term. We already have true AI - it's a vast, thriving industry. AlphaGo is obviously a true, legitimate, actual, real, nonfictional example of artificial intelligence, as are Google Search, the Facebook Newsfeed, Siri, the Amazon Echo, etc.

Interestingly just ~5 years ago the team "AI" was frowned upon and people insisted in using the term "machine learning". The reasoning was that "AI" is just too convoluted a term because people will insist on comparing with humans and then whole question of consciousness invariably arises which renders scientific inquiry into undesirable debates.

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

#90

Post by Animats » Mon, Mar 14, 2016, 6:41 AM UTC

What we need next are more systems which can predict "what is likely to happen if this is done". Google's automatic driving systems actually do that. Google tries hard to predict the possible and likely actions of other road users. This is the beginning of "common sense".