8 ms·

Artificial Intelligence Is Already Weirdly Inhuman

86 points by rl3 11y ago

joshmarinacci 11y ago
I don't find this surprising. In fact, I would find the opposite surprising. It would be very surprising if AI was human-like. Human intelligence is designed to power a human body, with fingers of a certain length and eyes of a certain stereo field of view. I don't think we will ever develop a human like AI until we give it a human like body to live in.
- soong 11y ago
  Exactly! Our brain may be our center of computation but the models it develops of the outside world and hence about itself are based on the signals it recieves from the senses.
- sp332 11y ago
  I definitely agree. People treat human-like intelligence as if it were the only possible kind, as if human-ness were a natural consequence of intelligence.
- Hansi 11y ago
  Just clicked on comments to post exactly the same point although I'm not sure the body would be sufficient, I think a lot of other factors must be in place too.
- saidajigumi 11y ago
  > I don't think we will ever develop a human like AI until we give it a human like body to live in. This is not a new thought; there's quite a bit that's been studied and written on the importance of embodiment to cognition, perception, etc. Cognitive Scientists and a few other disciplines have dug into this a fair bit. And I agree about the article. This quote is just ridiculous: That suggests humanity shouldn’t assume our machines think as we do. It presupposes that anyone with actual knowledge of AI, neural nets, etc. believes that we're hot on the path of human-style artificial cognitive capabilities with our current tools. At best, we may have started building small components that in a future form, might eventually be assembled, Society of Mind[1] style, into something that eventually begins to resemble general AI. [1] https://en.wikipedia.org/wiki/Society_of_Mind https://en.wikipedia.org/wiki/Society_of_Mind
- simonh 11y ago
  > I don't think we will ever develop a human like AI until we give it a human like body to live in. Perhaps, but also it doesn't automatically follow that if they look like us they must think like us, whatever Hollywood says.
crazypyro 11y ago
(My take on this problem, feel free to correct any assumptions I make that are wrong) I think this is because neural nets are not actually "intelligent" in terms of the commonly accepted definition of intelligence. They are dumb. They try, usually with simple probabilistic techniques and input element-wise transforms, to mimic some function that produces approximations for a given set of inputs and outputs. The training data and the test data will always have underlying differences which will create gaps in the data generating distribution, assuming a reasonably large-sized set of data. This is contrary to the assumptions that (every?) machine learning algorithm makes, usually referred to as the i.i.d assumptions. The test and training data are assumed to be independent and identically distributed. Since practically no real world data sets are perfectly identically distributed, there will always be gaps in the learned model and the real model. Beyond that, the set of training data can never fully encompass the entire domain of possible input/outputs, else what need would we have for a machine to predict new ones? The oddities that researchers find are actually problems in the relationship of their training data to their testing data because the i.i.d assumptions are never actually true. We can only try to get as close as possible. The solution to this problem is just nigh impossible, so we try to reduce it as much as possible.
- hiddencost 11y ago
  That's pretty good. I wouldn't say things are impossible, but there're a lot of conversations happening about the problems with our current approach. Leon Bottou gave a great talk at ICML that covers stuff like this. https://www.google.com/url?sa=t&source=web&rct=j&url=http://icml.cc/2015/invited/LeonBottouICML2015.pdf&ved=0CBwQFjAAahUKEwjTpe3_tZXHAhVGEpIKHSOkB84&usg=AFQjCNEqCYMpVjZQs9-NXseTdgg2mCKDFA&sig2=tsR6pnvDP8GnJnFcoGMbQQ https://www.google.com/url?sa=t&source=web&rct=j&url=http://...
  - crazypyro 11y ago
    Thanks for that slidedeck! It was really informative and enlightening to have some of these vague, hard-to-explain ideas that I've been wondering about cemented by Bottou.
- noskynethere 11y ago
noir_lord 11y ago
We saw this in Chess engines about 20 years back (when they started getting seriously strong) they play Chess incredibly well but looks very little like what a human playing does. It was fascinating to watch the changes in chess theory through that period as the machines validated or invalidated concepts and ideas that GM's had posited but been unable to prove one way or the other. John Speelman wrote an intro to The Mammoth Book of Chess (that thing was awesome,I think I wore out three copies) which (paraphrasing horribly) that Chess is a realm of pure thought and that computers play it the way an alien might. The best engines on commodity hardware now require offering pawn odds for a top human to even have a chance and their strength is accelerating[1] (stockfish now has a distributed test architecture to test for regressions which is incredibly cool[2]) [1] http://www.chess.com/article/view/how-rybka-and-i-tried-to-beat-the-strongest-chess-computer-in-the-world http://www.chess.com/article/view/how-rybka-and-i-tried-to-b... (Rybka was/is one of the strongest engines and in concert with a human it still couldn't beat Stockfish). [2] https://stockfishchess.org/get-involved/ https://stockfishchess.org/get-involved/
- zzalpha 11y ago
  Plus, computer chess, with its alien moves, opened up human thinking on chess, which ties in with the article's point about neural nets causing humans to see our own world differently.
  - noir_lord 11y ago
    Indeed (was just editing to clarify on that :) ). It led to something of a renaissance in chess theory. What I'd love to see (and have been tempted to take a crack at) is an engine built purely to play like a strong human with variable strength (things like not always taking the strongest move but not playing like god and then blundering either, not always avoiding mate in 27 plies and such). Shredder does it pretty well but it still feels like a machine, ChessMaster with The King got the closest I've seen.
    - tcbawo 11y ago
      That would be interesting. It would have to simulate human distraction and emotional ebbs and flows. I imagine ot taking into account feelings like impatience, boredom, and overeagerness.
Qantourisc 11y ago
Another reason that neural nets do crazy things: they have little knowledge. We see a dog, by looking at it's shape etc. The neural net by looking at features. A solution to this I think would be splitting up the taks, like "finding eyes", "is this fur", etc ... freeze these networks, and use these as input. This would prevent most errors given in these examples. A lot of animals know what eyes look like regardless of the species, not saying nature always has the best solution. But it probably had a good reason to have a specialized-build-in "head/eyes" detector.
- seiji 11y ago
  We see a dog, by looking at it's shape etc. The neural net by looking at features. Have you ever seen a kid learn language? At first, every four legged animal is either a cat or a dog. Every car is a truck (or every truck is a car). The specific categories get learned over time, but they aren't in any sense "natural."
- igravious 11y ago
  You remember that bit in Hitchhiker's Guide where Arthur Dent is explaining to the ship's onboard Nutri-matic thing (I think it was) all about what a cup of tea is. After getting back many different versions of a drink that was nearly but not quite unlike tea, Arthur explained about tea leaves and climates and tea trade and tea ceremonies and on and on. I'm sure my recollection and retelling do not do it justice. My point is, what we've got now is crude visual-feature pattern matching and recognition. What you're asking for is conceptualisation. Wiring up concepts to sense-data is our trick. We are reverse engineering ourselves bit by bit. You make the next step sound so reasonable, and maybe perhaps it is.
brock_r 11y ago
I see a cheetah in the static, too...
- mkehrt 11y ago
  Me too. I'm glad I'm not the only one.
- igravious 11y ago
  It's right there stalking through the lush pixelated grass. Quite unmistakable, I agree.
- pluma 11y ago
  Fellow robots...
adamzubi 11y ago
http://googler700.blogspot.com/ http://googler700.blogspot.com/
Quanticles 11y ago
This article relies on carefully constructed images that maximize one particular outcome by summing up lots of small errors into it. For it to work, the pixels have to be very accurately tweaked. If the tweaks were off by one pixel, the whole thing would fall apart. The assumption is that this cannot be done to a person. But there is no way to put in a pixel-level "exploit of sorts" into a person to test that theory. The real answer is probably that a little bit of noise on the input probably disrupts the exploit. It could never happen to a person because eyes have noise. At the same time, it could never happen to a robot either because cameras have noise.
- eru 11y ago
  From the article: Such screwy results can’t be explained away as hiccups in individual computer systems, because examples that send one system off its rails will do the same to another. After he read “Deep Neural Networks Are Easily Fooled,” Dileep George, cofounder of the AI research firm Vicarious, was curious to see how a different neural net would respond. On his iPhone, he happened to have a now-discontinued app called Spotter, a neural net that identifies objects. He pointed it at the wavy lines that Clune’s network had called a starfish. “The phone says it’s a starfish,” George says. Spotter was examining a photo that differed from the original in many ways: George’s picture was taken under different lighting conditions and at a different angle, and included some pixels in the surrounding paper that weren’t part of the example itself. Yet the neural net produced the same extraterrestrial-sounding interpretation. “That was pretty interesting,” George says. “It means this finding is pretty robust.” In fact, the researchers involved in the “starfish” and “ostrich” papers made sure their fooling images succeeded with more than one system. “An example generated for one model is often misclassified by other models, even when they have different architectures,” or were using different data sets, wrote Christian Szegedy, of Google, and his colleagues.4 “It means that these neural networks all kind of agree what a school bus looks like,” Clune says. “And what they think a school bus looks like includes many things that no person would say is a school bus. That really surprised a lot of people.”
deleted 11y ago
[deleted]
white-flame 11y ago
I do have my doubts that plain neural networks will ever be able to achieve conceptual understanding. I have an affinity for classical, rational AI in that you can correct it and it will take that correction and instantly apply it to is knowledge base. It can also explain why it came to a conclusion. (though obviously this style has its very real limitations) NNs and other current statistical/connectionist approaches don't really have this capability, which I see as a necessary part of human-level intelligence. They are trained to "get a feel for" particular inputs to indicate particular outputs. If you were to personify the NN analyzing the dog/"ostrich" picture, and ask it why it thinks it's an ostrich, it could only reply with "I dunno, it feels like it's an ostrich". The only way to correct it is to retrain it with careful checks so that it behaviorally "gains a sense" of what looks like a dog vs ostrich more reliably. Many language/word based features operate similarly. Watson, Siri, Google search, all sort of map strengths of relations with your input words & patterns to some associated results that it just sort of statistically was reinforced with. These can yield information that is of real use for a human to further evaluate and act on, but I wouldn't trust such a system to directly act on those associations; they're wrong too often. But there is no possibility of actual conceptual discourse with NNs as we know them, to correct them, to inform them, or to ask them to explain their results. This is a fundamental barrier to achieving human-style intelligence. This is not to say that there aren't NN-based possibilities that might work, like tacking together multiple interacting NNs, each which have the possibility to specialize on concepts and influence other NNs. But too much has focused on single NNs and direct input-to-output "monkey-see, monkey-do" training. It's a manifestation of the Chinese room problem.
- pygy_ 11y ago
  > I do have my doubts that plain neural networks will ever be able to achieve conceptual understanding. What do you think you're made of?
  - white-flame 11y ago
    There are many specialized areas of the human brain. It's not a single, unidirectional, backpropagated, simple-coefficient, neural net. It is not a recurrent neural network, nor is it a single instance of any particular NN-derived or NN-ish network.
hellofunk 11y ago
Sigh. The phrase "neural network" is getting tossed around these days with some type of sensationalist flair, an almost romanticized notion of this impending explosion of super-human phenomena. As Alex Smola says with much frustration in one his classes, "it's only math!" It's a fancy term for straightforward mathematics. Bloggers are so often making them out to be much more than they are.
- kdoherty 11y ago
  There's something to be said for both sides of this. I think you're right about the way NN's have been blown out of proportion. I find as I work across fields they're the most abused and misunderstood ML technique, since everyone and their dog has heard of NN's at this point. But on the other hand, there's a lot that can be said for simple building blocks combining to form something very complex. The biological "inspiration" behind NN's make them an attractive starting point for investigating this line of thought.
- mindcrime 11y ago
  Sigh. The phrase "neural network" is getting tossed around these days with some type of sensationalist flair, an almost romanticized notion of this impending explosion of super-human phenomena. It's all cyclical. This is at least the second, if not the third, wave of hype for Neural Networks. I remember a period back in the mid to late 90's when this stuff was quite the rage.
faragon 11y ago
How vs why I started learning genetic algorithms (random permutations selected/promoted for picking better combinations of Hamiltonian paths for solving NP-C problems obtaining near-optimal solutions quickly) and neural networks (mainly backpropagation, using different topologies) circa 1995 (college). It was like magic: you learn how to use those techniques easily in order to solve problems. However, the why was not so evident: while in the case of genetic algorithms you can understand that in a space of solutions with many similar cost solutions could allow you to pick a "good one" easily, in the case of neural networks the idea I got was like some a "magic box" which was supposed to do some interpolation/extrapolation that effectively work like generating a convex surface for giving a "solution"/"location" for a given input. It was like you got alien technology simply to make it work for simple things, but not being able to really understand why it was working (except for trivial/small networks). Do people really understand what complex neural networks do? I.e. is still trial-error only, or are built on purpose?
- IndianAstronaut 11y ago
  >Do people really understand what complex neural networks do From a biological sense, the only neural net we have a decent understanding of is the C elegans nematode worm with a few hundred neurons. Large neural networks like that of our brain are beyond our understanding and I suspect the same is the case for ANNs.
pokkanome 11y ago
Neural Networks do not think. They perform a series of computations, and arrive at a result. It is not intelligence. It is a very useful tool for solving problems that can be quantified and that we can generate a lot of data from, but ultimately, it is still human intelligence that is interpreting the problem and result.
- ThrustVectoring 11y ago
  Human brains do not think. They perform a series of computations, and arrive at a result. It is not intelligence. It is a very useful tool for solving problems that can be translated into neural inputs.
  - stephengillie 11y ago
    Sufficiently advanced robotics is indistinguishable from life. - Adapted from Arthur C. Clarke.
  - pokkanome 11y ago
    And what evidence do you have that human neurons do any kind of computation at all? Generally, they are stimulated by some sensation until they reach a certain threshold that causes them to fire. That is the basic kind of functionality that nodes in a neural network try to simulate. But human neurons are not dependent on numbers and change in much more complex ways than a few parameters. The brain requires a lot less data than these networks to learn new concepts. And the concepts that these networks learn are all ideas that humans came up with. A network does not hold an opinion, it takes in inputs and gives outputs. I do not mean to say that we cannot make a network to simulate a brain, but that is not what we have right now.
    - ThrustVectoring 11y ago
      And what evidence do you have that silicon transistors do any kind of computation at all? Generally, they are stimulated by some voltage until they reach a certain threshold that causes them to change state.
kordless 11y ago
What if you showed the algorithm/network/whatever itself? You could 'take a picture of it' and then train it to know that's itself, which would then change what it looks like. Keep doing that until it doesn't change or you fail trying.
- eru 11y ago
  You could get into a loop. Though, what are you trying to accomplish here?
  - kordless 11y ago
    Consciousness?
rcthompson 11y ago
I think the "static is a cheetah" example just highlights that the neural net is not identifying the best features with which to identify a cheetah. Or alternatively, if, during training, the neural net was only fed pictures of nature with or without cheetahs in them, then what it's really telling you is not the probability that a picture contains a cheetah, but rather the conditional probability that a picture contains a cheetah given that it is a picture of nature. In other words, that picture of static is most likely well outside the domain of the training set, so classifying it involves a large extrapolation, with all the attendant amplification of errors. Perhaps what we need is a classifier that can tell when a picture is significantly outside of its training experience and say "I've never seen anything like that before" instead of giving an arbitrary classification.
kazinator 11y ago
If you don't know why a neural network suddenly reports that video static is a cheetah, the main reason for that is that you also don't know, in the first place, why it reports that a picture of a cheetah is a cheetah! To emphasize our lack of understanding of the false classifications is misleading.
deleted 11y ago
[deleted]
h0l0cube 11y ago
There is one important point that seems to be lost every time an article uses adversarial examples to justify why neural nets are deficient: not even a human is perfect at understand an image at a blink. When we see an image we decode a stream of impulses. Saccades will follow observing in detail number of areas of the image at different orientations, and so our interpretation of the image will come from numerous samples, not a single image. IMHO, it's actually quite amazing that such primitive software neural networks can understand an image in a blink, in one 'sample'. Conversely, it's not inhuman to see pictures in clouds, Rorschach tests, or even static.
ilaksh 11y ago
We are trying to emulate some human capabilities. In doing so we have created a complex system which can be almost as hard to predict as a human. That's not surprising, that's hust physics. Having a few odd classicifations is not surprising either. It hasn't been trained like a human and is being asked to select a class where 'randon squiggly' isn't an option. To get human-like intelligence we need to develop them more as virtually embodied agents. Sort of like kids.
aliameer 11y ago
http://googler700.blogspot.com/2015/04/learning-artificial-intelligence.html http://googler700.blogspot.com/2015/04/learning-artificial-i...

86 points by rl3 11y ago

I don't find this surprising. In fact, I would find the opposite surprising. It would be very surprising if AI was human-like. Human intelligence is designed to power a human body, with fingers of a certain length and eyes of a certain stereo field of view. I don't think we will ever develop a human like AI until we give it a human like body to live in.

soong 11y ago
Exactly! Our brain may be our center of computation but the models it develops of the outside world and hence about itself are based on the signals it recieves from the senses.
sp332 11y ago
I definitely agree. People treat human-like intelligence as if it were the only possible kind, as if human-ness were a natural consequence of intelligence.
Hansi 11y ago
Just clicked on comments to post exactly the same point although I'm not sure the body would be sufficient, I think a lot of other factors must be in place too.
saidajigumi 11y ago
> I don't think we will ever develop a human like AI until we give it a human like body to live in. This is not a new thought; there's quite a bit that's been studied and written on the importance of embodiment to cognition, perception, etc. Cognitive Scientists and a few other disciplines have dug into this a fair bit. And I agree about the article. This quote is just ridiculous: That suggests humanity shouldn’t assume our machines think as we do. It presupposes that anyone with actual knowledge of AI, neural nets, etc. believes that we're hot on the path of human-style artificial cognitive capabilities with our current tools. At best, we may have started building small components that in a future form, might eventually be assembled, Society of Mind[1] style, into something that eventually begins to resemble general AI. [1] https://en.wikipedia.org/wiki/Society_of_Mind https://en.wikipedia.org/wiki/Society_of_Mind
simonh 11y ago
> I don't think we will ever develop a human like AI until we give it a human like body to live in. Perhaps, but also it doesn't automatically follow that if they look like us they must think like us, whatever Hollywood says.

crazypyro 11y ago

(My take on this problem, feel free to correct any assumptions I make that are wrong) I think this is because neural nets are not actually "intelligent" in terms of the commonly accepted definition of intelligence. They are dumb. They try, usually with simple probabilistic techniques and input element-wise transforms, to mimic some function that produces approximations for a given set of inputs and outputs. The training data and the test data will always have underlying differences which will create gaps in the data generating distribution, assuming a reasonably large-sized set of data. This is contrary to the assumptions that (every?) machine learning algorithm makes, usually referred to as the i.i.d assumptions. The test and training data are assumed to be independent and identically distributed. Since practically no real world data sets are perfectly identically distributed, there will always be gaps in the learned model and the real model. Beyond that, the set of training data can never fully encompass the entire domain of possible input/outputs, else what need would we have for a machine to predict new ones? The oddities that researchers find are actually problems in the relationship of their training data to their testing data because the i.i.d assumptions are never actually true. We can only try to get as close as possible. The solution to this problem is just nigh impossible, so we try to reduce it as much as possible.

hiddencost 11y ago
That's pretty good. I wouldn't say things are impossible, but there're a lot of conversations happening about the problems with our current approach. Leon Bottou gave a great talk at ICML that covers stuff like this. https://www.google.com/url?sa=t&source=web&rct=j&url=http://icml.cc/2015/invited/LeonBottouICML2015.pdf&ved=0CBwQFjAAahUKEwjTpe3_tZXHAhVGEpIKHSOkB84&usg=AFQjCNEqCYMpVjZQs9-NXseTdgg2mCKDFA&sig2=tsR6pnvDP8GnJnFcoGMbQQ https://www.google.com/url?sa=t&source=web&rct=j&url=http://...
- crazypyro 11y ago
  Thanks for that slidedeck! It was really informative and enlightening to have some of these vague, hard-to-explain ideas that I've been wondering about cemented by Bottou.
noskynethere 11y ago

noir_lord 11y ago

We saw this in Chess engines about 20 years back (when they started getting seriously strong) they play Chess incredibly well but looks very little like what a human playing does. It was fascinating to watch the changes in chess theory through that period as the machines validated or invalidated concepts and ideas that GM's had posited but been unable to prove one way or the other. John Speelman wrote an intro to The Mammoth Book of Chess (that thing was awesome,I think I wore out three copies) which (paraphrasing horribly) that Chess is a realm of pure thought and that computers play it the way an alien might. The best engines on commodity hardware now require offering pawn odds for a top human to even have a chance and their strength is accelerating[1] (stockfish now has a distributed test architecture to test for regressions which is incredibly cool[2]) [1] http://www.chess.com/article/view/how-rybka-and-i-tried-to-beat-the-strongest-chess-computer-in-the-world http://www.chess.com/article/view/how-rybka-and-i-tried-to-b... (Rybka was/is one of the strongest engines and in concert with a human it still couldn't beat Stockfish). [2] https://stockfishchess.org/get-involved/ https://stockfishchess.org/get-involved/

zzalpha 11y ago
Plus, computer chess, with its alien moves, opened up human thinking on chess, which ties in with the article's point about neural nets causing humans to see our own world differently.
- noir_lord 11y ago
  Indeed (was just editing to clarify on that :) ). It led to something of a renaissance in chess theory. What I'd love to see (and have been tempted to take a crack at) is an engine built purely to play like a strong human with variable strength (things like not always taking the strongest move but not playing like god and then blundering either, not always avoiding mate in 27 plies and such). Shredder does it pretty well but it still feels like a machine, ChessMaster with The King got the closest I've seen.
  - tcbawo 11y ago
    That would be interesting. It would have to simulate human distraction and emotional ebbs and flows. I imagine ot taking into account feelings like impatience, boredom, and overeagerness.

Qantourisc 11y ago

Another reason that neural nets do crazy things: they have little knowledge. We see a dog, by looking at it's shape etc. The neural net by looking at features. A solution to this I think would be splitting up the taks, like "finding eyes", "is this fur", etc ... freeze these networks, and use these as input. This would prevent most errors given in these examples. A lot of animals know what eyes look like regardless of the species, not saying nature always has the best solution. But it probably had a good reason to have a specialized-build-in "head/eyes" detector.

seiji 11y ago
We see a dog, by looking at it's shape etc. The neural net by looking at features. Have you ever seen a kid learn language? At first, every four legged animal is either a cat or a dog. Every car is a truck (or every truck is a car). The specific categories get learned over time, but they aren't in any sense "natural."
igravious 11y ago
You remember that bit in Hitchhiker's Guide where Arthur Dent is explaining to the ship's onboard Nutri-matic thing (I think it was) all about what a cup of tea is. After getting back many different versions of a drink that was nearly but not quite unlike tea, Arthur explained about tea leaves and climates and tea trade and tea ceremonies and on and on. I'm sure my recollection and retelling do not do it justice. My point is, what we've got now is crude visual-feature pattern matching and recognition. What you're asking for is conceptualisation. Wiring up concepts to sense-data is our trick. We are reverse engineering ourselves bit by bit. You make the next step sound so reasonable, and maybe perhaps it is.

brock_r 11y ago

I see a cheetah in the static, too...

mkehrt 11y ago
Me too. I'm glad I'm not the only one.
igravious 11y ago
It's right there stalking through the lush pixelated grass. Quite unmistakable, I agree.
pluma 11y ago
Fellow robots...

adamzubi 11y ago

http://googler700.blogspot.com/ http://googler700.blogspot.com/

Quanticles 11y ago

This article relies on carefully constructed images that maximize one particular outcome by summing up lots of small errors into it. For it to work, the pixels have to be very accurately tweaked. If the tweaks were off by one pixel, the whole thing would fall apart. The assumption is that this cannot be done to a person. But there is no way to put in a pixel-level "exploit of sorts" into a person to test that theory. The real answer is probably that a little bit of noise on the input probably disrupts the exploit. It could never happen to a person because eyes have noise. At the same time, it could never happen to a robot either because cameras have noise.

eru 11y ago
From the article: Such screwy results can’t be explained away as hiccups in individual computer systems, because examples that send one system off its rails will do the same to another. After he read “Deep Neural Networks Are Easily Fooled,” Dileep George, cofounder of the AI research firm Vicarious, was curious to see how a different neural net would respond. On his iPhone, he happened to have a now-discontinued app called Spotter, a neural net that identifies objects. He pointed it at the wavy lines that Clune’s network had called a starfish. “The phone says it’s a starfish,” George says. Spotter was examining a photo that differed from the original in many ways: George’s picture was taken under different lighting conditions and at a different angle, and included some pixels in the surrounding paper that weren’t part of the example itself. Yet the neural net produced the same extraterrestrial-sounding interpretation. “That was pretty interesting,” George says. “It means this finding is pretty robust.” In fact, the researchers involved in the “starfish” and “ostrich” papers made sure their fooling images succeeded with more than one system. “An example generated for one model is often misclassified by other models, even when they have different architectures,” or were using different data sets, wrote Christian Szegedy, of Google, and his colleagues.4 “It means that these neural networks all kind of agree what a school bus looks like,” Clune says. “And what they think a school bus looks like includes many things that no person would say is a school bus. That really surprised a lot of people.”

deleted 11y ago

[deleted]

white-flame 11y ago

I do have my doubts that plain neural networks will ever be able to achieve conceptual understanding. I have an affinity for classical, rational AI in that you can correct it and it will take that correction and instantly apply it to is knowledge base. It can also explain why it came to a conclusion. (though obviously this style has its very real limitations) NNs and other current statistical/connectionist approaches don't really have this capability, which I see as a necessary part of human-level intelligence. They are trained to "get a feel for" particular inputs to indicate particular outputs. If you were to personify the NN analyzing the dog/"ostrich" picture, and ask it why it thinks it's an ostrich, it could only reply with "I dunno, it feels like it's an ostrich". The only way to correct it is to retrain it with careful checks so that it behaviorally "gains a sense" of what looks like a dog vs ostrich more reliably. Many language/word based features operate similarly. Watson, Siri, Google search, all sort of map strengths of relations with your input words & patterns to some associated results that it just sort of statistically was reinforced with. These can yield information that is of real use for a human to further evaluate and act on, but I wouldn't trust such a system to directly act on those associations; they're wrong too often. But there is no possibility of actual conceptual discourse with NNs as we know them, to correct them, to inform them, or to ask them to explain their results. This is a fundamental barrier to achieving human-style intelligence. This is not to say that there aren't NN-based possibilities that might work, like tacking together multiple interacting NNs, each which have the possibility to specialize on concepts and influence other NNs. But too much has focused on single NNs and direct input-to-output "monkey-see, monkey-do" training. It's a manifestation of the Chinese room problem.

pygy_ 11y ago
> I do have my doubts that plain neural networks will ever be able to achieve conceptual understanding. What do you think you're made of?
- white-flame 11y ago
  There are many specialized areas of the human brain. It's not a single, unidirectional, backpropagated, simple-coefficient, neural net. It is not a recurrent neural network, nor is it a single instance of any particular NN-derived or NN-ish network.

hellofunk 11y ago

Sigh. The phrase "neural network" is getting tossed around these days with some type of sensationalist flair, an almost romanticized notion of this impending explosion of super-human phenomena. As Alex Smola says with much frustration in one his classes, "it's only math!" It's a fancy term for straightforward mathematics. Bloggers are so often making them out to be much more than they are.

kdoherty 11y ago
There's something to be said for both sides of this. I think you're right about the way NN's have been blown out of proportion. I find as I work across fields they're the most abused and misunderstood ML technique, since everyone and their dog has heard of NN's at this point. But on the other hand, there's a lot that can be said for simple building blocks combining to form something very complex. The biological "inspiration" behind NN's make them an attractive starting point for investigating this line of thought.
mindcrime 11y ago
Sigh. The phrase "neural network" is getting tossed around these days with some type of sensationalist flair, an almost romanticized notion of this impending explosion of super-human phenomena. It's all cyclical. This is at least the second, if not the third, wave of hype for Neural Networks. I remember a period back in the mid to late 90's when this stuff was quite the rage.

faragon 11y ago

How vs why I started learning genetic algorithms (random permutations selected/promoted for picking better combinations of Hamiltonian paths for solving NP-C problems obtaining near-optimal solutions quickly) and neural networks (mainly backpropagation, using different topologies) circa 1995 (college). It was like magic: you learn how to use those techniques easily in order to solve problems. However, the why was not so evident: while in the case of genetic algorithms you can understand that in a space of solutions with many similar cost solutions could allow you to pick a "good one" easily, in the case of neural networks the idea I got was like some a "magic box" which was supposed to do some interpolation/extrapolation that effectively work like generating a convex surface for giving a "solution"/"location" for a given input. It was like you got alien technology simply to make it work for simple things, but not being able to really understand why it was working (except for trivial/small networks). Do people really understand what complex neural networks do? I.e. is still trial-error only, or are built on purpose?

IndianAstronaut 11y ago
>Do people really understand what complex neural networks do From a biological sense, the only neural net we have a decent understanding of is the C elegans nematode worm with a few hundred neurons. Large neural networks like that of our brain are beyond our understanding and I suspect the same is the case for ANNs.

pokkanome 11y ago

Neural Networks do not think. They perform a series of computations, and arrive at a result. It is not intelligence. It is a very useful tool for solving problems that can be quantified and that we can generate a lot of data from, but ultimately, it is still human intelligence that is interpreting the problem and result.

ThrustVectoring 11y ago
Human brains do not think. They perform a series of computations, and arrive at a result. It is not intelligence. It is a very useful tool for solving problems that can be translated into neural inputs.
- stephengillie 11y ago
  Sufficiently advanced robotics is indistinguishable from life. - Adapted from Arthur C. Clarke.
- pokkanome 11y ago
  And what evidence do you have that human neurons do any kind of computation at all? Generally, they are stimulated by some sensation until they reach a certain threshold that causes them to fire. That is the basic kind of functionality that nodes in a neural network try to simulate. But human neurons are not dependent on numbers and change in much more complex ways than a few parameters. The brain requires a lot less data than these networks to learn new concepts. And the concepts that these networks learn are all ideas that humans came up with. A network does not hold an opinion, it takes in inputs and gives outputs. I do not mean to say that we cannot make a network to simulate a brain, but that is not what we have right now.
  - ThrustVectoring 11y ago
    And what evidence do you have that silicon transistors do any kind of computation at all? Generally, they are stimulated by some voltage until they reach a certain threshold that causes them to change state.

kordless 11y ago

What if you showed the algorithm/network/whatever itself? You could 'take a picture of it' and then train it to know that's itself, which would then change what it looks like. Keep doing that until it doesn't change or you fail trying.

eru 11y ago
You could get into a loop. Though, what are you trying to accomplish here?
- kordless 11y ago
  Consciousness?

rcthompson 11y ago

I think the "static is a cheetah" example just highlights that the neural net is not identifying the best features with which to identify a cheetah. Or alternatively, if, during training, the neural net was only fed pictures of nature with or without cheetahs in them, then what it's really telling you is not the probability that a picture contains a cheetah, but rather the conditional probability that a picture contains a cheetah given that it is a picture of nature. In other words, that picture of static is most likely well outside the domain of the training set, so classifying it involves a large extrapolation, with all the attendant amplification of errors. Perhaps what we need is a classifier that can tell when a picture is significantly outside of its training experience and say "I've never seen anything like that before" instead of giving an arbitrary classification.

kazinator 11y ago

If you don't know why a neural network suddenly reports that video static is a cheetah, the main reason for that is that you also don't know, in the first place, why it reports that a picture of a cheetah is a cheetah! To emphasize our lack of understanding of the false classifications is misleading.

deleted 11y ago

[deleted]

h0l0cube 11y ago

There is one important point that seems to be lost every time an article uses adversarial examples to justify why neural nets are deficient: not even a human is perfect at understand an image at a blink. When we see an image we decode a stream of impulses. Saccades will follow observing in detail number of areas of the image at different orientations, and so our interpretation of the image will come from numerous samples, not a single image. IMHO, it's actually quite amazing that such primitive software neural networks can understand an image in a blink, in one 'sample'. Conversely, it's not inhuman to see pictures in clouds, Rorschach tests, or even static.

ilaksh 11y ago

We are trying to emulate some human capabilities. In doing so we have created a complex system which can be almost as hard to predict as a human. That's not surprising, that's hust physics. Having a few odd classicifations is not surprising either. It hasn't been trained like a human and is being asked to select a class where 'randon squiggly' isn't an option. To get human-like intelligence we need to develop them more as virtually embodied agents. Sort of like kids.

aliameer 11y ago

http://googler700.blogspot.com/2015/04/learning-artificial-intelligence.html http://googler700.blogspot.com/2015/04/learning-artificial-i...