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Meet the algorithm that can learn “everything about anything”

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Re: Meet the algorithm that can learn “everything about anything”

#33
post #3

The problem with all approaches to machine learning I see today is that they only focus on grouping and separating concepts based on certain characteristics. They seem to be all fundamentally statistical. None of them seem to work towards a fundamental understanding of what the concepts mean. I'm not sure how that could be accomplished though. Is there even a meaningful distinction to be made between beeing able to i…

I'm of the impression that concepts only have fundamental meaning to us because we can relate them to sensory input. For example, initially a child is taught what a boat is by seeing pictures, it eventually learns that boats, cars and bicycles share common characteristics and can be grouped together as vehicles. These common characteristics are all understood and identified by seeing, hearing, touching or smelling. I…

There is no question that additional data would be potentially useful to an AI. But it's a common fallacy that it's the only way to get "meaning". Data from a digital camera isn't fundamentally different than data from text. The only benefit is that visual data is far less compressed, more redundant. It's easier to find patterns in it. Natural language is a highly compressed description of things in the real world.

That said, it doesn't at all mean you can't learn anything meaningful from it. For example, you build a machine learning system to predict the missing words in a sentence. "A horse is similar to other farm animals like ____." Machines are getting better at this kind of thing, though still far from human level. Google's word2vec for example can take a word like "horse" and list the words that it is most similar to. You can subtract the representation for "man" from "king", add "woman" and it outputs "queen".

Re: Meet the algorithm that can learn “everything about anything”

#34

One of the hallmarks of bad science is overly grand claims paired with aggressive marketing. Bad times are coming for AI again.

Judging by the amount of "deep learning" submissions to HN bad times for AI are already here although maybe they never left.

In defense of the LEVAN thing, though, I didn't see any claims that this is science at all, more like an exploratory application illustrating an algorithm.

Re: Meet the algorithm that can learn “everything about anything”

#35
post #5

Earlier quoted context omitted.

I don't think there is a meaningful distinction. Do chess computers have a "fundamental understanding" of chess, which humans traditionally considered a benchmark of human intelligence/strategy? Analogous to the philosophical zombie thought experiment, I think that "real intelligence/understanding" is indistinguishable from simply being able to perform actions to accomplish the same tasks that humans traditionally co…

What do you want? Until I can just talk to a computer and it always understands what I'm asking, there's no AI is there? It's a fuzzy concept to exactly define, but it's pretty obvious what we all mean when we talk about it. Brute forcing billions of moves of chess isn't going to achieve that, so it's nothing more than a cute trick that lay people don't understand is a trick so we have to tell them it is. David Coppe…

But that's the thing. We solve a problem and then the solution is just called a "cute trick". The goalpost gets moved further. Natural language processing is getting really advanced. Soon enough you will be able to talk to a computer in a conversation. And then you will just call that a cute trick.

Re: Meet the algorithm that can learn “everything about anything”

#36

One of the hallmarks of bad science is overly grand claims paired with aggressive marketing. Bad times are coming for AI again.

Judging by the amount of "deep learning" submissions to HN bad times for AI are already here although maybe they never left. In defense of the LEVAN thing, though, I didn't see any claims that this is science at all, more like an exploratory application illustrating an algorithm.

I think the increase in deep-learning links in this particular community is partly because Google recently dropped a huge pile of money on a deep-learning startup (Deep Mind), which gave it more visibility in the startup scene. Though in that case, are we talking about a separate AI frenzy, or is it just an appendage of the current startup/acquisitions frenzy?

Re: Meet the algorithm that can learn “everything about anything”

#37
post #5
post #3

The problem with all approaches to machine learning I see today is that they only focus on grouping and separating concepts based on certain characteristics. They seem to be all fundamentally statistical. None of them seem to work towards a fundamental understanding of what the concepts mean. I'm not sure how that could be accomplished though. Is there even a meaningful distinction to be made between beeing able to i…

I don't think there is a meaningful distinction. Do chess computers have a "fundamental understanding" of chess, which humans traditionally considered a benchmark of human intelligence/strategy? Analogous to the philosophical zombie thought experiment, I think that "real intelligence/understanding" is indistinguishable from simply being able to perform actions to accomplish the same tasks that humans traditionally co…

The fundamental problem humans will have with strong AI is that it won't be able to properly rationalize it's actions. Sure you'll have a statistical model for why it did what it did but that means about as much to anybody as a numerical reification of the quantum states of every particle of a person's mind at the time they made a decision.

Re: Meet the algorithm that can learn “everything about anything”

#39
post #20
post #13

(Disclaimer: I am not an native english speaker) The title of the article and of the algorithm (learn everything about anything) is a bit misleading. You might believe that it learns everything, period. Actually it's more about finding every variation of a "concept", I quote their website: "a fully automated method that given any concept, e.g., horse, discovers an exhaustive vocabulary for it that explains all variat…

I wonder. If they have an exhaustive vocabulary would it be possible to generate a picture of what the system believes an object to look like? I know that there is something called generative models in machine learning and my guess is that it could be applied here.

It's possible, but generally generative models have to be trained in a specific way. If not, you could do something like for every layer of the neural net, you train another NN which can "predict" the layer below it, it's input. Then you can work your way down each layer to try to find an input which would produce that output.

Another way is to use some kind of optimization to find an input which produces that pattern (e.g. backprop to the pixels themselves.) This will give you the image that most strongly triggers that output. Not necessarily a typical example.

Re: Meet the algorithm that can learn “everything about anything”

#40
This doesn't really help the symbol grounding problem: it uses pre-human-sorted data (who use their own ability to match symbols and meaning) to form its associative network. So, it's using human consciousness as part of the input to form its own consciousness. You could argue that humans use other humans' consciousness to develop its own, but now you have the infinite regress which seem to be the fate of all symbol grounding contemplations. Surely there has to be a starting point, some "axioms" you initially accept about the world to start the process. Maybe these are embedded in our DNA and have evolved to be a practical start (eg: sharp sensory input from nerves on your skin is automatically linked with pain, which we automatically avoid).
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