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Google Developers Day US - Theorizing from Data

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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Why HN readers recommended it

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

This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.

Video & recommendation details

Creator / channel
GoogleDeveloperDay
Topic
data-driven machine learning
Format
talk
Level
intermediate
HN recommenders in dataset
3
HN mentions in dataset
5
Recommendation years in dataset
2007–2009

These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.

From the original HN conversations

Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.

  • 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 …

    henning on Hacker News · 2008-03
    In More data usually beats better algorithms

  • [ [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.

    dood on Hacker News · 2007-12
    In Google Is All About Large Amounts of Data

  • 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]

    timcederman on Hacker News · 2009-08
    In Ask HN: Access to the Corpus - What Would You Do?

Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.

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