Tuesday, July 18, 2017

Cracking the Black Box

Jul 2017, phys.org

"Deep learning" and "neural networks" are terms that have become firmly planted in our popular lexicon. They all refer to the same thing, which is an artificial brain-like thing that teaches itself via feedback loops. I talked about this in Hidden Scents because the way our brain decodes olfactory information is a lot like the way these deep learning networks process their own big data.

This deep learning approach is way more effective than traditional computing for lots of problems like facial recognition or natural language translation. They're also really good at handling Big Data, you  know, like all that stuff cybercriminals keep stealing for ransom? Thing is, once these networks 'figure out' how to do whatever it is that they do, we have no idea how they did it.

Usually, with traditional programming, we write the code, so we know what it does and how. With this, the network essentially writes its own program, and since it seems to know what it's doing, we don't ask how. We just take the results.

Until now. This is one of the researchers, quoted in phys.org:

"We catalogued 1,100 visual concepts—things like the color green, or a swirly texture, or wood material, or a human face, or a bicycle wheel, or a snowy mountaintop," says David Bau, an MIT graduate student in electrical engineering and computer science and one of the paper's two first authors. "We drew on several data sets that other people had developed, and merged them into a broadly and densely labeled data set of visual concepts. It's got many, many labels, and for each label we know which pixels in which image correspond to that label."

Here is the link to their work.

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