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Peter Norvig - The Unreasonable Effectiveness of Data

UBC Computer Science · 73.9K views · 2011-10-11 · 1:02:57

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Overview

Peter Norvig's UBC distinguished lecture on how massive amounts of simple data, such as for language modeling, can yield understanding that hand-built models could not.

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Recommended as a favorite tech talk showing how huge data availability changes how problems are approached.

Recommended by 3 HN people across 3 months · HN mentions 2012–2026

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What people said

ChicagoBoy11 · 2016-10 · Strong recommendation

Peter Norvig on the "Unreasonable Effectiveness of Data" [VIDEO] I think it is so easy for us to discuss the impact of big data and quickly get into the weeds, but I think in this talk Norvig does an especially great job in making you truly appreciate the seismic impact that the availability of massive quantities of data can have on your way to think about problems. This is one of the first things I ever saw of him, and I've been in love ever since.

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gpcz · 2013-01 · Recommendation

If you're looking for more examples and information about using simple algorithms with lots of data, I recommend Peter Norvig's "The Unreasonable Effectiveness of Data": [VIDEO]

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stiff · 2012-05 · Recommendation

I am not sure if I disagree with the central thesis of the article, but the argumentation seems rather weak in many places, for example he says: Let's take the example of the number of people who walk into a store and buy a certain product. By studying 10 customers you can get an estimate of the probability of the purchase and predict sales; by studying 100 customers you get a better estimate. By analyzing 1000 customers you have a very good estimate. You could study a million customers, but the results are unlikely to be vastly different from the results of analyzing 1000 customers and probably not worth the expense. This makes sense only for very low-dimensional data sets, but most real-life problems are very high-dimensional and in high-dimensional spaces a million customers (or any other number of samples you might reasonably expect to gather) might still very well be only a small sample. So, there are situations where because of various constraints you will never be able to get to this level where more data stops yielding better results. You can watch a nice presentation from Peter Norvig about the unreasonable effectiveness of data: [VIDEO] Or read about the well-known curse of dimensionality: [LINK]

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Video by UBC Computer Science, played through YouTube.

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