NYT – Google Researchers Are Learning How Machines Learn


  • Machines are starting to learn tasks on their own
  • done through so-called neural networks, which are complex computer algorithms that learn tasks by analyzing vast amounts of data
  • It is not always easy to tell how the machines arrive at their conclusions
    • Google took a small step toward addressing this issue with the unveiling of new research that offers the rough outlines of technology that shows how the machines are arriving at their decisions
      • “Even seeing part of how a decision was made can give you a lot of insight into the possible ways it can fail,”
  • A.I. researchers are now developing ways to better understand neural networks … called this “artificial neuroscience.” (leave it to academics to come up with yet another new term)
  • neural networks are algorithms that are really series of mathematical operations, and each operation represents a neuron
    • Google’s new research aims to show — in a highly visual way — how these mathematical operations perform discrete tasks, like recognizing objects in photos
    • wants to provide tools that show what each neuron is trying to identify
  •  could also help identify why a neural network is prone to mistakes and, in some cases, explain how it learned this behavior
  • work is still in its infancy
  • as these networks get more complicated, it is going to be fundamentally difficult to understand why they make decisions — is kind of like trying to understand why humans make decisions

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