Is this AI Winter 2.0? I was hopeful that logic programming would have developed more and spread to a larger audience at this point.
AI winter is well on its way
341–350 of 518 posts
Re: AI winter is well on its way
#342Earlier quoted context omitted.
I think chess may actually be the worst lab. Decisions made in chess are done so with perfect knowledge of the current state and future possibilities. Most decisions are made without perfect knowledge.
For chess, the future possibilities are so vast, you can't call them "perfect knowledge" with a straight face.
Chess was already easy for computers. That's why Arimaa came to be.
Re: AI winter is well on its way
#343I'm a scientist from a field outside ML who knows that ML can contribute to science. But I'm also really sad to see false claims in papers. For example, a good scientist can read an ML paper, see claims of 99% accuracy, and then probe further to figure out what the claims really mean. I do that a lot, and I find that accuracy inflation and careless mismanagement of data mars most "sexy" ML papers. To me, that's what'…
I'm in the same situation and it's really worrying.
Deep learning is the method of choice for a number of concrete problems in vision, nlp, and some related disciplines. This is a great success story and worthy of attention. Another AI winter will just make it harder to secure funding for something that may well be a good solution to some problems.
Re: AI winter is well on its way
#344We are beginning to see some sweet differential embeddings of discrete things like stacks and context free grammars. This is where deep learning gets really fun because it is learning to program.
Re: AI winter is well on its way
#345Earlier quoted context omitted.
The brain has a number of functional parts that we don't understand all that well. Research on the brain hits a wall every now and then, but you never hear the phrase "Neuroscience Winter". We're starting to train models that match biological brain behavior, at least in some crude functional/structural sense. Maybe this is just a string of coincidences, but my guess is that discoveries of analogous biological/model c…
Neuroscience has been in constant winter, mainly because the methods are too crude and small scale, and that by observing 100 neurons out of billions makes it impossible to tell the whole story. The deep mind papers are interesting, but they are just a start imho. It may be that they are focusing on a mere coincidence. Nevertheless it is exciting to see progress in that direction, thats why i don't think DL research…
I'd like to see neuroscience take more of a fundamental role in grounding/situating deep learning approaches. VGG is often mentioned as being roughly analogous to the visual cortex, but it differs in important ways. There's all kinds of why questions there. E.g. Why does deep learning work better with (e.g. ReLU) activation based on pooling while biological models use an inhibitory mechanism? Theory on why certain activations work better than others in DL is a little weak imho. Right now ML practitioners just throw a lot of parameter combinations at the wall and see what sticks. That's fine, but it's not really indicative of a robust understanding of model behavior.
Re: AI winter is well on its way
#346Earlier quoted context omitted.
I think chess may actually be the worst lab. Decisions made in chess are done so with perfect knowledge of the current state and future possibilities. Most decisions are made without perfect knowledge.
For chess, the future possibilities are so vast, you can't call them "perfect knowledge" with a straight face.
This is not what the terminology "perfect knowledge" means. Perfect knowledge (more often called "perfect information") refers to games in which all parts of the game state are accessible to every other player. In theory, any player in the game has access to all information contained in every game state up to the present and can extrapolate possible forward states. Chess is a very good example of a game of perfect information, because the two players can readily observe the entire board and each other's moves.
A good example of a game of imperfect information is poker, because players have a private hand which is known only to them. Whereas all possible future states of a chess game can be narrowed down according to the current game state, the fundamental uncertainty of poker means there is a combinatorial explosion involved in predicting future states. There's also the element of chance in poker, which further muddies the waters.
Board games are often (but not always) games of perfect and complete information. Card games are typically games of imperfect and complete information. This latter term, "complete information", means that even if not all of the game state is public, the intrinsic rules and structure of the game are public. Both chess and poker are complete, because we know the rules, win conditions and incentives for all players.
This is all to say that games of perfect information are relatively easy for a computer to win, while games of imperfect information are harder. And of course, games of incomplete information can be much more difficult :)
Re: AI winter is well on its way
#347Earlier quoted context omitted.
When you can solve chess, you can solve a whole class of decision-making problems If this were true, there would be a vast demand for grandmasters in commerce, government, the military... and there just isn’t. Poker players suffer from similar delusions about how their game can be generalised to other domains.
I think batmansmk doesn't mean "when X is good at chess, X is automatically good at lots of other things", but "the traits that make you a good chess player (given enough training) also make you good at lots of other things (given enough training)".
Second world chess champion Emanuel Lasker spent a couple years studying Go and by his own report was dejected by his progress. Maybe he would have eventually reached high levels, but I've always found this story fascinating.
Re: AI winter is well on its way
#348I was recently "playing" with some radiology data. I had no chance to identify diagnoses myself with untrained eyes, something that probably takes years for a decent radiologist to master. Just by using DenseNet-BC-100-12 I ended up with 83% ROC AUC after a few hours of training. In 4 out of 12 categories this classifier beat best human performing radiologists. Now the very same model with no other change than adjusting number of categories could be used in any image classification task, likely with state-of-art results. I was surprised when I applied it to another, completely unrelated dataset and got >92% accuracy right away.
If you think this is a symptom of AI winter, then you are probably wasting time on outdated/dysfunctional models or models that aren't suited for what you want to accomplish. Looking e.g. at Google Duplex (better voice synchronization than Vocaloid I use for making music), this pushed state-of-art to unbelievable levels in hard-to-address domains. I believe the whole SW industry will be living next 10 years from gradual addition of these concepts into production.
If you think Deep (Reinforcement) Learning is going to solve AGI, you are out of luck. If you however think it's useless and won't bring us anywhere, you are guaranteed to be wrong. Frankly, if you are daily working with Deep Learning, you are probably not seeing the big picture (i.e. how horrible methods used in real-life are and how you can easily get very economical 5% benefit of just plugging in Deep Learning somewhere in the pipeline; this might seem little but managers would kill for 5% of extra profit).
Re: AI winter is well on its way
#349Earlier quoted context omitted.
> What decision-making problems are there, in the same class, that I'd pay for? an awful lot of graph and optimization problems. See for instance some examples in https://en.wikipedia.org/wiki/A*_search_algorithm
Perfect information problem solving is not interesting anymore. Did they manage to extend it to games with hidden and imperfect information? (Say, chess with fog of war also known as Dark Chess. Phantom Go. Pathfinding equivalent would be an incremental search.) Edit: I see they are working on it, predictive state memory paper (MERLIN) is promising but not there yet.
Strongly disagree. There are a lot of approximation algorithms and heuristics in wide use - to the tune of trillions of dollars, in fact, when you consider transportation and logistics, things like asic place & route, etc. These are all intractable perfect info problems that are so widespread and commercially important that they amplify the effect of even modest improvements.
(You said problems, not games...)
Re: AI winter is well on its way
#350Warning 23 year old CS grad angst ridden post: I'm very sick of the AI hype train. I took a PR class for my last year of college, and they couldn't help but mention it. LG Smart TV ads mention it, Microsoft commercials, my 60 year old tech illiterate Dad. Do any end users really know what it's about? Probably not, nor should that matter, but it's very triggering to see something that was once a big part of CS turned…
>I get triggered
You should be more considerate than to be throwing around this term.