321–330 of 384 posts
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We've drawn a couple of circles and we want to draw the rest of the owl, and you're telling us there's only one step.
Yes. Because we don't know it is an owl we should draw. It's equally likely AI is just three circles combined.
>> Deep learning algorithms have proven to be better than humans at spotting lung cancer, a development that if applied at scale could save more than 30,000 patients per year. It's not easy to scale deep learning because deep neural nets have a very strong tendency to overfit to their training dataset and are very bad at generalising outside their training dataset. In a medical context this means that, while a partic…
>>the same classifier will be much worse in the same task on images from a different hospital (or even from a different department in the same hospital). Has anybody figured out why this is the case? Could it be socioeconomic factors? Or the presence of different toxic pollutants across different communities?
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>> Accidentally (?) she managed to create the best representation of AI I have seen in art: all that counts is that you call it AI even if it is a simple algorithm. Backpropagation, which most researchers will agree is an AI algorithm, is a "simple algorithm". So are many other AI algorithms, some of which are simple enough to be understood so well that most people don't recognise them as AI anymore: search algorithm…
Sure, backprop is fairly simple, but the thing it produces is somewhat complex, and we seem to find it hard to explain “why” the weights it finds work (even though we understand clearly how it finds weights that do work), right? That seems sufficiently “black-box-ish” to me?
This happened right before the first AI Winter in the late 80s: AI (in the form of expert systems) solved a number of hard problems and was hyped as being able to solve every problem. Reality set in when we figured out: 1. It didn't scale and 2. Getting 80% of the problem solved was easy, but getting that last 20% was very, very hard. Maybe several orders of magnitude harder than the first 80%. Nowadays we don't seem…
You're always going to see #2. Use cases get marketable before they get fully solved so you'll always see pretty good but not great examples. In the mean time there are a lot of things that are solved by neural nets that we take for granted such as voice and face recognition.
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Could you please explain in what sense deep learning is "applied computational statistics"? What about classical planning, SAT solvers, automated theorem proving, game-playing agents and classical search? Could you please explain how one or more of those are "applied computational statistics"? Further- I don't understand the comment about "agency". Could you clarify? Why is "agency" required for a technique or an alg…
I don’t know anything about the underlying algorithms for the examples you rattled off, but deep learning trains a graph of neuron weights such that they are statistically optimized to minimize error in computed output labels for some domain of input data. Very much “applied computational statistics”.
I'm not sure what you mean about "neuron weights that are statistically optimised". Modern-era, deep neural nets train their weights with backpropagation, which is basically an application of the chain rule, from calculus. They do not use statistics for that.
For example, calculating the mean of a set of values or calculating the pearson correlation coefficient of two variables are computations typical in statistics.
Could you please clarify what you mean by (applied) "computational statistics", so that I don't have to double-guess you?
Edit: Do you really not know what a SAT solver is? Not to be rude but if that is the case, from where do you draw your confidence about the correct terminology to use for AI?
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> a Dota bot that's pretty good as long as you give the bot a huge advantage Excuse my ignorance but what is the huge advantage? And what happens if you don't give the bot that?
The details require a little knowledge of Dota, but essentially the bot only knows how to play a much simplified version of the game. They play with a reduced number of playable heroes (5 of the 100+). Each hero changes the dynamic of the game and many heroes have unique interactions with each other, so this is a very substantial simplification. Additionally, Dota is a game where mechanics (the ability to quickly and…
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I don’t know anything about the underlying algorithms for the examples you rattled off, but deep learning trains a graph of neuron weights such that they are statistically optimized to minimize error in computed output labels for some domain of input data. Very much “applied computational statistics”.
The examples I gave are classic AI algorithms that are very easy to look up on wikipedia. They do not compute any statistics. I'm not sure what you mean about "neuron weights that are statistically optimised". Modern-era, deep neural nets train their weights with backpropagation, which is basically an application of the chain rule, from calculus. They do not use statistics for that. For example, calculating the mean…
>> Deep learning algorithms have proven to be better than humans at spotting lung cancer, a development that if applied at scale could save more than 30,000 patients per year. It's not easy to scale deep learning because deep neural nets have a very strong tendency to overfit to their training dataset and are very bad at generalising outside their training dataset. In a medical context this means that, while a partic…
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My point wasn't about the particular form Weiner's cybernetics ended up taking; I was only commenting on his optimism regarding neural networks in the late forties and early fifties (you can say it's about his vision of Cybernetics rather than it's actual manifestation).
Sorry for a German's nitpicking, but it seems to be not a typo: The man's name is Wiener.