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Google Developers Day US - Theorizing from Data

GoogleDeveloperDay

3.4K subscribers • 2 videos on Hallway

YouTube
  • Recommended by 3 people: 3 on Hacker News.
  • Presented by Peter Norvig
  • May be out of date. Uploaded 19 years ago, in an area where tools change fast.
  • An opinion. It argues one side of a question engineers disagree on.

Intermediate. Assumes working knowledge.

26K views19 years ago3 recommendations

AI and machine learningComputer scienceClassic machine learningData engineering and warehouses

Peter Norvig explains how massive datasets can revolutionize language understanding, translation, and inference, arguing that more data often beats cleverer models.

"Theorizing from Data: Avoiding the Capital Mistake Peter Norvig ""It is a capital mistake to theorize before one has data."" Sir Arthur Conan Doyle's words from 1891 remain true today. Researchers in computational linguistics and information retrieval now have a million times more data than was available 30 years ago. This talk explores what this data can do for problems in language understanding, translation, information extraction, and inference, and extrapolates to what more data may bring in the future. "

Why it's here

3 people recommend this

What people say. Recommended as a classic talk demonstrating that large datasets often outperform complex algorithms in natural language processing.

henningon Hacker News • Recommends

On “More data usually beats better algorithms”

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": link to this 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.

doodon Hacker News • Recommends

On “Google Is All About Large Amounts of Data”

[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.

timcedermanon Hacker News • Recommends

On “Ask HN: Access to the Corpus - What Would You Do?”

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. link to this video

ntoshevon Hacker News • Mentions

On “Awaiting the Day When Everyone Writes Software ”

…s just to say that programming would benefit a lot from ability to apply common sense to mundane things. For some dramatic evidence that imprecise is better and it really works, look at the success of Google and the non-delivery of the Semantic Web. Google's approach is probably best explained by Peter Norvig in his "Theorizing from Data" lecture: link to this video State of the art in natural language processing is achieved with statistical inference, not building precise linguistic models. See e.g. Marti Hearst's text here: link Back to programming languages, I believe that a lot of the Ruby/Rails success is due to mimicking natural language in DSLs. Rails goes as far as using pluralization. DSLs b…

doodon Hacker News • Mentions

On “Google Squared is Live”

I like that they show you the source pages, confidence, and alternative sources for the data. However, it suffers the classic Google app problem; it does interesting stuff, but just looks ugly, and is generally unappealing. Hard for me to explain why, but despite my interest in the technology I just didn't enjoy using it at all. But it is interesting stuff, I'd been wondering when something like this would emerge since watching this Norving talk from 2007 [video ].