THE VIDEO SHELF · static analysis and precision medicine
Matt Might - Winning the War on Error Solving the Halting Problem and Curing Cancer
Matt Might's Curry On talk on ending errors in code, both software and the human genome. He covers conservative approximation of the halting problem via static analysis and a computational approach to precision medicine.
Why HN readers recommended it
Recommended as a jaw-dropping, must-see talk that gives an amazing intro to static analysis alongside Might's personal story.
This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.
Video & recommendation details
- Creator / channel
- Curry On!
- Topic
- static analysis and precision medicine
- Format
- talk
- Level
- intermediate
- HN recommenders in dataset
- 3
- HN mentions in dataset
- 3
- Recommendation years in dataset
- 2017–2019
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.
Matt Mights "Winning the War on Error: Solving the Halting Problem and Curing Cancer" is one that is well worth watching. [VIDEO] previous discussion on it: [LINK]
dpeck on Hacker News · 2017-12
In Ask HN: What's your favorite tech talk from 2017?Matt Might (the subject of this article) gave a jaw-dropping talk a year ago, named "Winning the War on Error - Solving the Halting Problem and Curing Cancer" [1]. At least you can't blame him for the lack of ambition! But the best part is …
the_grue on Hacker News · 2018-09
In A Battle Plan for a War on Rare Diseases: Conversation with Matt MightIf you haven't seen it, Might's talk "Winning the War on Error: Solving the Halting Problem and Curing Cancer" is amazing, a must-see. It tells some of the same story, also is an introduction to static analysis..and cancer. [VIDEO]
yesenadam on Hacker News · 2019-03
In How To: Get Tenure – Matt Might
Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.