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Lawrence Hrubes

Mathematicians and Blue Crabs - NYTimes.com - 1 views

www.nytimes.com/...ematicians-and-blue-crabs.html

math science ecology models prediction environment statistics animals

shared by Lawrence Hrubes on 03 May 15 - No Cached
  • The math behind these formulas may be elegant, but applying them is more complicated.
  • Although a definitive cause has yet to be identified, one thing is clear: Mathematical models failed to predict it.
  • For instance, it was long believed that a blue crab’s maximum life expectancy was eight years. This estimate was used, indirectly, to calculate crab mortality from fishing. Derided by watermen, the life expectancy turned out to be much too high; this had resulted in too many crab deaths being attributed to harvesting, thereby supporting charges of overfishing.
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  • Randomness is built into biological processes, so predicting a population is never going to be like calculating the interest on a bank account. The best we can do is use available science to make educated guesses about various outcomes. “The models we use are not universally predictive in the sense that Newton’s laws are; they are more like the weather forecast,” says Dr. Miller.
Lawrence Hrubes

The Great A.I. Awakening - The New York Times - 1 views

www.nytimes.com/...the-great-ai-awakening.html

language translation technology AI brain

shared by Lawrence Hrubes on 22 Dec 16 - No Cached
  • Translation, however, is an example of a field where this approach fails horribly, because words cannot be reduced to their dictionary definitions, and because languages tend to have as many exceptions as they have rules. More often than not, a system like this is liable to translate “minister of agriculture” as “priest of farming.” Still, for math and chess it worked great, and the proponents of symbolic A.I. took it for granted that no activities signaled “general intelligence” better than math and chess.
  • A rarefied department within the company, Google Brain, was founded five years ago on this very principle: that artificial “neural networks” that acquaint themselves with the world via trial and error, as toddlers do, might in turn develop something like human flexibility. This notion is not new — a version of it dates to the earliest stages of modern computing, in the 1940s — but for much of its history most computer scientists saw it as vaguely disreputable, even mystical. Since 2011, though, Google Brain has demonstrated that this approach to artificial intelligence could solve many problems that confounded decades of conventional efforts. Speech recognition didn’t work very well until Brain undertook an effort to revamp it; the application of machine learning made its performance on Google’s mobile platform, Android, almost as good as human transcription. The same was true of image recognition. Less than a year ago, Brain for the first time commenced with the gut renovation of an entire consumer product, and its momentous results were being celebrated tonight.
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