Open thread, Mar. 23 - Mar. 31, 2015

MrMind

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If it's worth saying, but not worth its own post (even in Discussion), then it goes here.

Notes for future OT posters:

1. Please add the 'open_thread' tag.

2. Check if there is an active Open Thread before posting a new one. (Immediately before; refresh the list-of-threads page before posting.)

3. Open Threads should be posted in Discussion, and not Main.

4. Open Threads should start on Monday, and end on Sunday.

NNs had lost favor in the AI community after 1969 (minsky's paper) and only have become popular again in the last decade

Yes, I'm familiar with the history. But how far would we be without the neural network work done since ~2001? The non-neural-network competitors on Imagenet like SVM are nowhere near human levels of performance, Watson required neural networks, Stanley won the DARPA Grand Challenge without neural networks because it had so many sensors but real self-driving cars will have to use neural networks, neural networks are why Google Translate has gone from roughly Babelfish levels (hysterically bad) to remarkably good, voice recognition has gone from mostly hypothetical to routine on smartphones...

What major AI achievements have SVMs or random forests racked up over the past decade comparable to any of that?

What year do you put the change in google translate? It didn't switch to neural nets until 2012, right? Did anyone notice the change? My memory is that it was dramatically better than babelfish in 2007, let alone 2010.

1is4junk11y

NNs connection to biology is very thin. Artificial neurons don't look or act like regular neurons at all. But as a coined term to sell your research idea its great. NNs are popular now for their deep learning properties and ability to learn features from unlabeled data (like edge detection). Comparing NNs to SVMs isn't really fair. You use the tool best for the job. If you have lots of labeled data you are more likely to use an SVM. It just depends on what problem you are being asked so solve. And of course you might feed an NNs output into an SVM or vice versa. As for major achievements - NNs are leading for now because 1) most of the world's data is unlabeled and 2) automated feature discovery (deep learning) is better then paying people to craft features.