THE VIDEO SHELF · rigor in machine learning
Ali Rahimi's talk at NIPS(NIPS 2017 Test-of-time award presentation)
Ali Rahimi's NIPS 2017 Test-of-Time award talk, in which he argues that modern machine learning has become alchemy, working without being rigorously understood. He calls for more rigor and understanding in the field.
Why HN readers recommended it
HN points to it as the sharpest, most-quoted critique of deep learning practice, and cites it whenever ML reliability or understanding is debated.
This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.
Video & recommendation details
- Creator / channel
- ObservantVids
- Topic
- rigor in machine learning
- Format
- talk
- Level
- intermediate
- HN recommenders in dataset
- 3
- HN mentions in dataset
- 4
- Recommendation years in dataset
- 2018–2025
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.
Ali Rahimi's NIPS 2017 Test of Time award talk. "Artificial intelligence is not like electricity (quoting Andrew Ng), but alchemy" [VIDEO]
vowelless on Hacker News · 2018-01
In Ask HN: What’s your favorite talk from 2017?This article is a little too glib in my opinion, preferring citations and statements to substance and explanations. For a more cutting and insightful critique, watch Ali Rahimi's short talk at NIPS 2017 (where he was presenting for a paper that won the "Test of …
ssivark on Hacker News · 2018-02
In Greedy, Brittle, Opaque, and Shallow: The Downsides to Deep Learning>In no way can I believe that the corporation doesn’t have a good understanding of exactly how it can manipulate the AI systems it leverages to achieve its goals. See this talk which compares modern AI work to alchemy (i.e. it works, unreliably, and we …
ShredKazoo on Hacker News · 2023-02
In Only You Can Stop an AI Apocalypse
Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.