Suddenly Terrence Davis appears!
Suddenly, a leopard print sofa appears
91–100 of 114 posts
Re: Suddenly, a leopard print sofa appears
#92Somehow 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…
Darwinbots uses actual computer code to control the robots. This makes it really hard for evolution to work with. Most mutations just break the code, and very very few mutations create anything interesting. And the simulation is too slow to explore millions of different possibilities to make up for the difficulty. What makes it worse is they are usually asexual.
However I think that's ok. Most of the fun with darwinbots is programming your own bots. They used to be (still are?) competitions where people wrote their own bots and had them compete under different conditions.
Re: Suddenly, a leopard print sofa appears
#93Thank 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…
Forget seeing a symbol once, you can recognise and represent a symbol without ever having seen it. Test your humanness; draw these symbols: "Like an E but rotated so the prongs point upwards" "Like a snake but with two heads. Snakes down, up, down, up, down." "Like a walking stick with the handle pointing left and looping back around." (answer for A: Russian letter Sha) (answer for B: Kannada letter Uu) (answer for C…
Convnets can do this. Geoffrey Hinton has a wonderful lecture, where he trained a digit recognizer on everything but 7's and 8's (IIRC.) He then let another convnet tell it which numbers looked more or less like 7's and 8's. E.g. "that 9 looks kind of like an 8. That 1 looks kind of like a 7", etc.
And then it was able to correctly recognize 7's and 8's, despite never having actually seen one. I'm simplifying somewhat, but it was super cool.
I don't know why people are so focused on one-shot learning, or think that NNs can't do it. Neural networks learn features from lots of (possibly unlabelled) data. That's the whole point. Once you have those features, you can use them for all sorts of things. You can show it an image, and then measure how close other images are too it. Thereby learning from a single example.
Re: Suddenly, a leopard print sofa appears
#94This 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…
Hmm, in your opinion do you think this would be a good technique then for digitizing paper maps? And if so, could you point in the direction of a library or textbook you'd recommend?
Do you mean cartographic images ? Like a map of your city or state -> machine learning -> some data like google maps?
Re: Suddenly, a leopard print sofa appears
#95Awhile ago I tried an interesting experiment. I fed a famous psychology image into a bunch of different image recognition systems.
This image: https://i.imgur.com/2aCqMx2.png
And here are the results: https://imgur.com/a/8ndyq
This doesn’t really prove anything, but I thought it was interesting. It is of course, unreasonable to expect ML algorithms to perform decently, so far outside of the space they were trained on.
But I suspect that part of the reason they don’t do well is that they are purely feed forward. Humans also don’t see the image at first. It takes time to find the pattern, and then everything clicks into place and you can’t unsee it.
This might have something to do with recurrency. But more importantly, information feeds down the hierarchy as well as up. Features above, give information back down to features below. So once you see the dog, that tells the lower level features that they are seeing legs and heads, which says they are seeing outlines of more basic 3 dimensional shapes, and so on.
I think it also requires a descent understanding of 3d space, to fit the observed pattern to 3d models which could have produced it. I’m not certain if regular NNs observing static images, are optimal for learning that.
More here: https://www.reddit.com/r/MachineLearning/comments/399ooe/tes...
Re: Suddenly, a leopard print sofa appears
#96> So I guess, there's still a lot of work to be done. And I think this is the most interesting part. One of the most depressing things about all of the "this image recognition algorithm performs better than humans on this task" is the idea that we've pretty much solved the problem, and it's just a matter of some more optimization and tweaking to handle a few edge cases. This kind of problem, where the dominant soluti…
This reminds me of one of Richard Feynman's famous quotes: “We are trying to prove ourselves wrong as quickly as possible, because only in that way can we find progress.”
Indeed, discovering these "broken" edge cases is exactly what we need to converge upon a more correct solution.
Re: Suddenly, a leopard print sofa appears
#97OP here, and thank you kind sirs and ladies for you feedback. I'd just like to answer the recurring objection: yes, our visual experience contains a lot of frames and that seemingly refutes my MNIST example; however, you do forget about the other part of a supervised dataset, namely labels. Do we have a label provided to each thing we see in our life? Obviously not. How much time do you need to familiarize yourself w…
> I guess a single math class was enough for all of you to recognize all the digits the next day.
I'm curious what makes you think that. My experience with what's going on at my sons school is telling me that the children spends a massive amount of time on getting recognition of digits and letters right.
Re: Suddenly, a leopard print sofa appears
#98Earlier quoted context omitted.
Might you by chance be familiar with Rodney Brooks' work on subsumption architectures [1]? If not, I would summarize the underlying idea (my words not his) as "don't try to jump too many layers of abstraction in one go" [2]. So I wonder to what extent you would consider this a predictable outcome from the classifier in question not being part of a subsumptive architecture --- which at a guess would look like - glance…
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.
Re: Suddenly, a leopard print sofa appears
#99For context, ImageNet does have a sofa category for labels :/ http://image-net.org/search?q=sofa
The Caffe models are only trained on the 1000 category subset of ImageNet used for the competition: http://image-net.org/challenges/LSVRC/2014/browse-synsets There are no sofa's in this list, the closest thing I can find is a "studio couch, day bed": http://imagenet.stanford.edu/synset?wnid=n04344873
Right. The "studio couches" are couches.
Re: Suddenly, a leopard print sofa appears
#100OP here, and thank you kind sirs and ladies for you feedback. I'd just like to answer the recurring objection: yes, our visual experience contains a lot of frames and that seemingly refutes my MNIST example; however, you do forget about the other part of a supervised dataset, namely labels. Do we have a label provided to each thing we see in our life? Obviously not. How much time do you need to familiarize yourself w…
> I guess a single math class was enough for all of you to recognize all the digits the next day. I'm curious what makes you think that. My experience with what's going on at my sons school is telling me that the children spends a massive amount of time on getting recognition of digits and letters right.
When I was living in China I had difficulty recognizing handwritten 9's and 1's. Their 9's are less half-circular than Westerner's are, and the 1's have a long stroke the top that looks like a sloppily written 7 to me. I would frequently look at a handwritten number and have to analyze what it was.