Peter Norvig - The Unreasonable Effectiveness of Data
UBC Computer Science3 HN recommenders · recommended in 3 months, 2012-2026
74K views · 15 years ago · lecture · evergreen
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.
Recommended as a favorite tech talk showing how huge data availability changes how problems are approached.
52:52Google Developers Day US - Theorizing from Data
GoogleDeveloperDay
27K views • 19 years ago
25:59Peter Norvig at Startup School 08
startupschool
6K views • 17 years ago
27:26reddit.com Interviews Peter Norvig
Reddit
47K views • 16 years ago
25:57GPT3: An Even Bigger Language Model - Computerphile
Computerphile
446K views • 6 years ago
1:21:28Lecture 01 - The Learning Problem
caltech
1.5M views • 14 years ago
1:11:41Lecture 1 | Natural Language Processing with Deep Learning
Stanford University School of Engineering
820K views • 9 years ago
20:39AI Language Models & Transformers - Computerphile
Computerphile
354K views • 7 years ago
17:07Duolingo -- the next chapter in human computation | Luis von Ahn | TEDxCMU 2011
TEDx Talks
394K views • 15 years ago
1:31:13A Hackers' Guide to Language Models
Jeremy Howard
580K views • 3 years ago
1:08:40Lecture 1 | Machine Learning (Stanford)
Stanford
2.7M views • 18 years ago
59:48[1hr Talk] Intro to Large Language Models
Andrej Karpathy
4.3M views • 2 years ago
1:19:08CS231n Winter 2016: Lecture1: Introduction and Historical Context
Andrej Karpathy
462K views • 10 years ago
11:48John Rauser keynote: "Statistics Without the Agonizing Pain" -- Strata + Hadoop 2014
O'Reilly
61K views • 12 years ago
1:01:51Lecture 1: Introduction to Information Theory
Jakob Foerster
411K views • 12 years ago
2:13:35Let's build the GPT Tokenizer
Andrej Karpathy
1.3M views • 2 years ago
1:07:09Richard Sutton – Father of RL thinks LLMs are a dead end
Dwarkesh Patel
804K views • 1 year ago
38:59StrictlyVC in conversation with Sam Altman, part two (OpenAI)
Connie Loizos
254K views • 3 years ago
8:25Large Language Models from scratch
Graphics in 5 Minutes
375K views • 4 years ago
57:21An Observation on Generalization
Simons Institute for the Theory of Computing
210K views • 3 years ago
57:57Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition
Stanford University School of Engineering
2.9M views • 9 years ago
5 comments from Hacker News
That happens often with domain names, but then you get expertsexchange.com, penisland.net, whorepresents.com, therapistfinder.com, a Dutch pre-match analysis site voorspel.nl, or a site about the game overspel.nl. Peter Norvig - The Unreasonable Effectiveness of Data ▶ this video
Peter Norvig on the "Unreasonable Effectiveness of Data" ▶ this 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.
This is old news: Peter Norvig, The Unreasonable Effectiveness of Data (2011): ▶ this video and a previous article from 2009: link
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": ▶ this video
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: ▶ this video Or read about the well-known curse of dimensionality: link