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Google Developers Day US - Theorizing from Data

GoogleDeveloperDay · 26.9K views · 2007-06-05 · 52:52

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Overview

Peter Norvig's 2007 Google talk on theorizing from data, covering what huge datasets can do for language understanding, translation, information extraction and inference. It argues that more data often beats cleverer models.

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HN recommends it as the clear explanation of why Google's statistical, data-first approach works.

Recommended by 3 HN people across 3 months · HN mentions 2007–2009

Topics

Machine learning & deep learningComputer vision, speech & NLPData science & statistics
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peter norvigbig datastatistical nlpmachine translationgoogle

What people said

henning · 2008-03 · Strong recommendation

One talk in particular in which he focuses on this idea, including with facts and figures from research literature, is a talk he gave a while ago called "Theorizing from Data": [VIDEO] . You should definitely watch it if you haven't seen it before. Norvig states his opinion slightly differently: of course being smart about algorithms is good. But until you get a lot of it, you often can't even fairly evaluate different algorithms. It's only when you're no longer getting significant gains from more data that you should then start thinking about being an algorithm smartypants.

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timcederman · 2009-08 · Recommendation

If you really think that's all they're doing then you have very little understanding in just how subtly awesome some of the stuff they're doing is. Peter Norvig does a better job explaining some of it than I ever could. [VIDEO]

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dood · 2007-12 · Recommendation

[ [VIDEO] ] Its a good talk, though maybe should be called "theorizing from massive amounts of data". Makes you wonder what Google are keeping under wraps for now, and how the powerset approach can compete, except maybe for domain-specific stuff.

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Sources

Video by GoogleDeveloperDay, played through YouTube.

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