We spend so much effort trying to engineer intelligence, when we would get a lot farther reverse engineering intelligence. Whenever AI makes a big advance the analog was already known my neuroscientists. There is also clearly no comprehension of the importance of the topological (circuitry) defining a neural network. We always assume a fully connected network, and draw the out as such, but we don't stop to consider that many of those Wijk interactions are completely spurious, meaning they have no information bearing role. If you strip them away you'll start to reveal the underlying circuit at work. I've published theoretical results using artificial gene networks, but the results should be similar for ANNs. http://m.msb.embopress.org/content/4/1/213.abstract
Suddenly, a leopard print sofa appears
101–110 of 114 posts
Re: Suddenly, a leopard print sofa appears
#102Earlier quoted context omitted.
There's an alife program called DarwinBots where small bots powered by mutating code compete against each other to survive and reproduce. Given enough time, you'd expect the to develop clever behaviors, but instead they just fuzz-tested the sim and locked in on exploits of bugs or environment settings. They only got a bit more clever when connecting different sims running on different conditions. Eyes already use dif…
Given enough time, you'd expect the to develop clever behaviors, but instead they just fuzz-tested the sim and locked in on exploits of bugs or environment settings. Classic A-life! Also, not so different from the spirit of actual biology. They only got a bit more clever when connecting different sims running on different conditions. Diversity is very important for evolution on many levels. What many don't realize (e…
If you don't have a sufficiently complex genotype phenotype mapping and the system is not evolvabke (See Gunter Wager's work) the you shouldn't expect more complex phenotype. Understanding a genetic representation is going to be an important step toward open ended evolutionary systems.
Re: Suddenly, a leopard print sofa appears
#103This article would not come as a surprise to anyone who works with ConvNets. Sadly, that might not the case for those outside of the field, largely due to media's inadequate coverage of our advances (but this is common outside our field too). No one in the field really believes ConvNets see better than humans. They are very good single glance texture recognizers. It's as if you flashed an image and looked at it for a…
No, I actually would not think I saw a leopard. Humans are really good at recognizing things with faces and legs. Those humans that didn't have the ability to recognize a leopard in a split second were already eaten thousands of years ago.
Re: Suddenly, a leopard print sofa appears
#104Earlier quoted context omitted.
the limit is that training cannot force abstraction. you can only reach abstraction if you have enough neuron space and the data set is big enough to avoid over-fitting textures. the problem is.. human vision doesn't work just by feeding a bitmap. we have structure to decode space relationships, shapes and maybe even shadow/light relations. no way we gonna see classificator working on color arrays matching our vision…
Seems simple enough to feed a NN with that abstracted data. However, the advantage to the texture approach is it's abstracted from a lot of other information. You don't want a classifier to say sofa, when it's a picture of a person on a sofa.
but then you're biasing it toward your perception:
http://www.bespokesofalondon.co.uk/assets/Uploads/bespoke-so...
anyway it does work perfectly if that's what you need, but most proponent are trying to use deep nn to classify 'as good as humans do'
Re: Suddenly, a leopard print sofa appears
#105TinEye tells me that the leopard print "sofa" is actually a dog bed ;) [0]
[0] http://www.tineye.com/search/4c4ce7b6558e8d3c4dd443439e80556...
Re: Suddenly, a leopard print sofa appears
#106Thank you for this article; very thought provoking. My nitpick: > When each student was given a heavy book of MNIST database, hundreds of pages filled with endless hand-written digit series, 60000 total, written in different styles, bold or italic, distinctly or sketchy. > ... > So, are you going to say that was not the case? I understand the point the author is making. Human brains are really good at taking limited…
Humans don't need to do data augmentation over several thousands of frames just to learn to recognize an object or image.
In the words of Joshua Tenenbaum and coauthors, "human children learning names for object concepts routinely make strong generalizations from just a few examples".
You can check this out for yourself on the brilliant illustration that went with it: http://i.imgur.com/5axtXSo.png From Tenenbaum J.B. et al, "How to grow a mind: statistics, structure, and abstraction," March 2011, Science, DOI:10.1126/science.1192788.
Re: Suddenly, a leopard print sofa appears
#107On the other hand, nobody has been upset that humans are constantly misidentifying that jaguar print sofa as a leopard print.
Well-played!
I myself have researched leopard spots since I painted our toilet floor in them. It's a lead sheet, and the paint had worn off, which probably wasn't the healthiest thing. My housemates had filled the toilet with memorabilia from an African trip, so leopard-print paintjob it was.
Which entailed looking up leopardprint online. Very little of which actually looks like leopard rosettes, and now I have a problem with almost anything trying to pass itself off as leopardprint. Anyway, I can't say that my paintjob is a particularly good reproduction, but at least it's 'spiritually correct'... :)
Re: Suddenly, a leopard print sofa appears
#108Obviously these classifiers do often focus on patterns, rather than shapes, and that's probably something that could be worked on, but I don't think an image classifier can possibly be expected to, at the level it is operating, identify the leopard-print sofa all on its own. Clearly there's a higher order process at work than image recognition here - after all, when a human is faced with a sofa-shaped object with a l…
Obviously!
Re: Suddenly, a leopard print sofa appears
#109Somehow tangent but this made me think about this quote found on HN last year: Context: Evolutionary algorithms and analog electronic circuits > One thing stands out when you try playing with evolutionary systems. Evolution is _really_ good at gaming the system. Unless you are very careful at specifying all of the constraints that you care about you can end up with a solution that is very clever but not quite what yo…
There's an alife program called DarwinBots where small bots powered by mutating code compete against each other to survive and reproduce. Given enough time, you'd expect the to develop clever behaviors, but instead they just fuzz-tested the sim and locked in on exploits of bugs or environment settings. They only got a bit more clever when connecting different sims running on different conditions. Eyes already use dif…
> They only got a bit more clever when connecting different sims running on different conditions.
Part of the reason why a lot of these nets are trained with added noise, as well as drop-out (randomly disabling 50% of the hidden neurons, every training step).
Especially the drop-out tactic is particularly effective at preventing "exploits" of the neural net type, which otherwise appear in the form of large correlated weights (really big weights depending on other really big opposite weights to cancel out--it works, but it doesn't help learning).
Either way, adding noisy hurdles helps because exploits are usually edge cases, and noise makes them less dependable, as the region of fitness space very close to an exploitable spot, is usually not very high-ranking at all (which is why you don't want your classifiers ending up there).
Re: Suddenly, a leopard print sofa appears
#110Earlier quoted context omitted.
Not just 3D shapes, but understand actions as they develop in time with recurrent neural networks.
What if you took these same Neural Networks as they exist now, and tweaked the input and the parameters slightly. For the input, use individual frames of an hour long video of a leopard (in order), and instead of having it just identify whether or not there is a leopard, have it identify what in each image is the leopard, and have it try to predict the next frame. It seems that this is more like the way that we learn…
I think a recurrent neural network is more suitable here as the size of context for each example might be different, instead of fixing the frame size.