AI Startup Says It Has Defeated Captchas

Post by ehsanu1 » Mon, Oct 28, 2013, 9:58 AM UTC

waps wrote: post #39

vidarh wrote: post #35

Earlier quoted context omitted.

You probably will be far off the mark. I realized once I started tracking my food seriously that 1) those glasses I have that I though were substantially bigger than those other glasses? They held the exact same amount; 2) the amount of calories I consume could easily vary by 30% or more depending on how full I'd fill my various glasses, plates or food containers.

I don't think getting a 30% margin of error would be all that disastrous. How big is the error margin on people measuring ?

Post by perfmode » Mon, Oct 28, 2013, 10:15 AM UTC

unlikelymordant wrote: post #41

perfmode wrote: post #34

Advances in storage and compute have led to a disturbing fetishization of machine learning. While the modern Machine Learning Movement makes sense in a historical context and is a reasonable reaction to the disappointing returns from symbolic inference during the early days of AI research, it is terrifying that the research community is satisfied to rely on big data and statistical methods to carry us forward. Few am…

I'm not sure I understand. "Few among us recognize the need to prioritize the study of the human brain", a great deal of the state of the art machine learning results are based on deep learning, which are algorithms that are "neurologically inspired" as you would put it. You seem to have a problem with big data and statistical methods, but one of the main deep learning algorithms, RBMs, are statistical methods. Also,…

Post by varjag » Mon, Oct 28, 2013, 10:26 AM UTC

perfmode wrote: post #44

unlikelymordant wrote: post #41

Earlier quoted context omitted.

I'm not sure I understand. "Few among us recognize the need to prioritize the study of the human brain", a great deal of the state of the art machine learning results are based on deep learning, which are algorithms that are "neurologically inspired" as you would put it. You seem to have a problem with big data and statistical methods, but one of the main deep learning algorithms, RBMs, are statistical methods. Also,…

"Even though an amplifier and a computer are both made of transistors, they have almost nothing else in common. In the same way, a real brain and a three-row neural network are built with neurons, but have almost nothing else in common." -- Jeff Hawkins (On Intelligence) One example is that most NNs neglect the time domain. A balanced approach recognizes the importance of learning from data, but does not _rely_ on bi…

Post by perfmode » Mon, Oct 28, 2013, 10:34 AM UTC

varjag wrote: post #46

perfmode wrote: post #44

Earlier quoted context omitted.

"Even though an amplifier and a computer are both made of transistors, they have almost nothing else in common. In the same way, a real brain and a three-row neural network are built with neurons, but have almost nothing else in common." -- Jeff Hawkins (On Intelligence) One example is that most NNs neglect the time domain. A balanced approach recognizes the importance of learning from data, but does not _rely_ on bi…

As a counterpoint, nearly all human technical advances (flight, propulsion, energy, computation) did not entail examination of biological systems. And ANNs in particular share a host of problems with statistical techniques: black-boxish behavior with lack of human-accessible state introspection and poor tractability.

Post by alphydan » Mon, Oct 28, 2013, 11:45 AM UTC

waps wrote: post #36

porker wrote: post #29

It'd be interesting to know more about this approach. In particular: > One big difference in Vicarious’s approach, says cofounder Dileep George, is that its system can be trained with moving images rather than only static ones. Does this imply they teach it how the shapes of numbers change, for easier detection?

It generally means you teach it to recognize 3d shapes (a 2d image that moves = a 3d image, more or less. Yes there's a good reason why you might want to call it 2.5d, but the easy way to model a 2.5d object is in 3d). Think of it as the difference between recognizing 2 points and recognizing a Feynman diagram. This is one of the things people don't often realize you can do with algorithms. You don't need to look at…