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

The Limitations of Deep Learning

blog.keras.io

Post reply on HN
Print view View original

141–150 of 296 posts

Re: The Limitations of Deep Learning

#141

Post by webmaven »

kensoh wrote: post #57
fspeech wrote: post #35

Earlier quoted context omitted.

The "creative" moves may very well come from the search part of the AlphaGo algorithm, though of course the networks have done their jobs of pruning the search space.

I see.. That's true. Though credit still goes to the algo for choosing that particular weird move out of the entire search space (it's just 'weird' and something you will think is a move made by a total newbie to the game). I remembered for that whole week during lunchtime I would watch the broadcast live on YouTube. How devastated I was to see Lee Sedol losing match after match. It was a moment I would never forget,…

Careful, that's the sort of thinking that led to the last 'AI Winter': assuming that if enough rule-based expert systems were built, general-purpose systems could be assembled from them and/or enough could be learned to build general-purpose systems.

Now, it is worth noting that DL models are already being assembled together (often with a coordinating DL model to switch between them). This can have the advantage of the smaller models being reusable to some extent (certainly more than expert systems ever were) but is not a panacea. The results are still essentially bespoke models rather than general purpose ones.

Deep Learning obviously has a lot more mileage left in it, given that much human mental labor is 'just' training and using our general-purpose intellects for what amount to a series of rather narrowly defined tasks, but it won't surprise me if there is a wall of some sort lurking just over the horizon that will require a different approach (albeit one that may still be called 'deep learning') to cross.

OTOH, it does seem as though the folks at DeepMind are fairly aggressively pursuing whatever is on the other side of that particular horizon:

https://deepmind.com/blog/neural-approach-relational-reasoni...

https://deepmind.com/blog/cognitive-psychology/

https://deepmind.com/blog/imagine-creating-new-visual-concep...

Re: The Limitations of Deep Learning

#142

Post by eanzenberg »

Cacti wrote: post #119
eanzenberg wrote: post #3

This point is very well made: 'local generalization vs. extreme generalization.' Advanced NN's today can locally generalize quite well and there's a lot of research spent to inch their generalization further out. This will probably be done by increasing NN size or increasing the NN building-blocks complexity.

I would really like to hear a definition of what generalization means, because I don't think we have one. Unless we're talking generalization to arbitrary distributions, which is of course unsolvable.

Usually in ML context generalization means anytime a model makes a prediction on unseen (not in the training set) inputs. Usually you do CV to see how well your model generalized because you limit the training data seen, predict unseen inputs that you know the answer to, and see how far off the prediction is.

Re: The Limitations of Deep Learning

#143

Post by unlikelymordant »

denfromufa wrote: post #31

If the deep learning network has enough layers, then can't it start incorporating "abstract" ideas common to any learning task? E.g. could we re-use some layers for image/speech recognition & NLP?

this is exactly what happens in transfer learning. A recent paper by google ( https://research.googleblog.com/2017/07/revisiting-unreasona... ) shows that pre-training on a very large image database leads to improvements in state of the art for several different image problems. This is because the weights required for one image problem are not necessarily all that different from another image problem, especially in the early layers. There may not be as much common ground beteen images and e.g. NLP. Perhaps at much higher abstraction levels, but we aren't there yet.

Re: The Limitations of Deep Learning

#144

Post by ThomPete »

reckoner2 wrote: post #112
ThomPete wrote: post #101

Earlier quoted context omitted.

One can say that the human mind consist of millions of not very special parts. It's the aggregate, the complexity of which they interact that makes it special. Once you start to connect all these seemingly non-special abilities in deep learning the "magic" starts to happen. You get something that is more than the sum of it's parts. Of course it's not DL in itself thats interesting but the potential emergent complex r…

Has anyone been able to do this? Is anyone working on it? I only follow the field as a hobby, but as far as I can tell we are nowhere near getting to this point. I think the ability to combine all these parts in a way that the sum is greater than it's parts is going to require many many breakthroughs still.

The thing is that it's most likely not something anyone does per se but something that happens with enough complexity.

If you happen to believe evolutionary theory is the most convincing then we weren't built either but a byproduct of emergent complexity.

It is my belief that humans are pattern recognizing feedback loops and carriers of information. We externalized some of that into books and built libraries to be able to keep even more than humans can remember as individuals and now have technology to save even more information and even manipulate it in ways impossible up until 80 years ago or so.

I am fairly certain that a technology is part of nature and that technology based conscience is nothing like our limited conscience but something rather different. The end result will not be like humans just better but nothing like humans at all but much better at the carrying of information part.

And so with that (my personal belief) perspective in mind no one is going to be able to do it it will happen as a by-product.

Please keep in mind that I saying "we exeternalized" in the same way we say "selfish genes" it's not a conscious effort as such but rather something which happen to be favorized in the game of life.

Why that is I have no idea but I am fairly certain humans aren't the last species. But yes it's all very speculative I just haven't been able to find better explanations for now.

Re: The Limitations of Deep Learning

#145

Post by danielam »

"This ability [...] to perform abstraction and reasoning, is arguably the defining characteristic of human cognition."

He's on the right track. Of course, the general thrust goes beyond deep learning. The projection of intelligence onto computers is first and foremost wrong because computers are not able, not even in principle, to engage in abstraction, and claims to the contrary make for notoriously bad, reductionistic philosophy. Ultimately, such claims underestimate what it takes to understand and apprehend reality and overestimate what a desiccated, reductionistic account of mind and the broader world could actually accommodate vis-a-vis the apprehension and intelligibility of the world.

Take your apprehension of the concept "horse". The concept is not a concrete thing in the world. We have concrete instances of things int he world that "embody" the concept, but "horse" is not itself concrete. It is abstract and irreducible. Furthermore, because it is a concept, it has meaning. Computers are devoid of semantics. They are, as Searle has said ad nauseam, purely syntactic machines. Indeed, I'd take that further and say that actual, physical computers (as opposed to abstract, formal constructions like Turing machines) aren't even syntactic machines. They do not even truly compute. They simulate computation.

That being said, computers are a magnificent invention. The ability to simulate computation over formalisms -- which themselves are products of human beings who first formed abstract concepts on which those formalisms are based -- is fantastic. But it is pure science fiction to project intelligence onto them. If deep learning and AI broadly prove anything, it is that in the narrow applications where AI performs spectacularly, it is possible to substitute what amounts to a mechanical process for human intelligence.

Re: The Limitations of Deep Learning

#146

Post by Buttons840 »

paganel wrote: post #58
kensoh wrote: post #30

Earlier quoted context omitted.

I don't have ML or deep learning background (no Masters or PhD), adding comment from experience with backtesting trading systems. We will collect market data and design algorithms that seem to produce the kind of outcomes we want. Then test on some other data sets which the algorithms have never been applied on. Many iterations later, you can get a decent profitable algorithm. And if the 'holy grail' algo is run in m…

> The quality of the algo and I assume the deep learning model lies in the quality (breadth and depth) of the data, and how honest with himself the person choose to model it. I've only dabbled with machine-learning here and there for the past 10 years or so, but if there's one thing I've learned so far is that the data behind your ML code (and the way it is structured) is responsible for almost all the success or fai…

> focus a lot of his time on the data itself... from where he intends to collect it? how is it structured? is it reliable? is it "enough"?

What's the best books on this subject? I suppose it's a very broad topic and thus more difficult to talk about than a single "neural network" algorithm.

Re: The Limitations of Deep Learning

#147

Post by eanzenberg »

webmaven wrote: post #141
kensoh wrote: post #57

Earlier quoted context omitted.

I see.. That's true. Though credit still goes to the algo for choosing that particular weird move out of the entire search space (it's just 'weird' and something you will think is a move made by a total newbie to the game). I remembered for that whole week during lunchtime I would watch the broadcast live on YouTube. How devastated I was to see Lee Sedol losing match after match. It was a moment I would never forget,…

Careful, that's the sort of thinking that led to the last 'AI Winter': assuming that if enough rule-based expert systems were built, general-purpose systems could be assembled from them and/or enough could be learned to build general-purpose systems. Now, it is worth noting that DL models are already being assembled together (often with a coordinating DL model to switch between them). This can have the advantage of t…

We can debate, but I don't think another AI winter will happen again in my lifetime. AI work is just earning way too much money for its funding to get cut, and a lot of funding is currently private too.

Re: The Limitations of Deep Learning

#148

Post by unlikelymordant »

pishpash wrote: post #59
reader5000 wrote: post #4

Recurrent models do not simply map from one vector space to another and could very much be interpreted as reasoning about their environment. Of course they are significantly more difficult to train and backprop through time seems a bit of a hack.

Sure they do. The spaces are just augmented with timestep related dimensions.

No they aren't? RNNs have state that gets modified as time goes on. The RNN has to learn what is important to save as state, and how to modify it in response to different inputs. There is no explicit time-stamping.

Re: The Limitations of Deep Learning

#149

Post by Cybiote »

mtremsal wrote: post #140
fspeech wrote: post #35

Earlier quoted context omitted.

The "creative" moves may very well come from the search part of the AlphaGo algorithm, though of course the networks have done their jobs of pruning the search space.

My understanding is that innovation comes from reinforcement learning during self-play (rather than supervised learning of pro games), and thus goes against the best moves suggested by AlphaGo's policy network, in turn pushing it towards new options. In a sense, it seems innovation arises when the value network forces the policy network to expand the search space because an apparently unlikely move leads to downstrea…

It's not that simple. The creativity is that the combination of rollouts, policy and value networks allow for more efficient traversal of the search space. Which gets you better exploration of possible paths, meaning more options than a human considered and therefore more creativity.

Re: The Limitations of Deep Learning

#150

Post by ThomPete »

danielam wrote: post #145

"This ability [...] to perform abstraction and reasoning, is arguably the defining characteristic of human cognition." He's on the right track. Of course, the general thrust goes beyond deep learning. The projection of intelligence onto computers is first and foremost wrong because computers are not able, not even in principle, to engage in abstraction, and claims to the contrary make for notoriously bad, reductionis…

The Chinese Room argument is one of the least convincing arguments against AI. Of course the man in the room isn't conscious neither is the individual neurons in your brain. It's the whole house that become conscious.

The reality is that we just don't know.

Post reply on HN