The Computational Limits of Deep Learning
About this episode
Subscribe: Apple, Android, Spotify, Stitcher, Google, and RSS.
In this episode of the Data Exchange I speak with Neil Thompson, Research Scientist at Computer Science and Artificial Intelligence Lab (CSAIL) and the Initiative on the Digital Economy, both at MIT. I wanted Neil on the podcast to discuss a recent paper he co-wrote entitled “The Computational Limits of Deep Learning” (summary version here). This paper provides estimates of the amount of computation, economic costs, and environmental impact that come with increasingly large and more accurate deep learning models.
Download the 2020 NLP Survey Report and learn how companies are using and implementing natural language technologies.
Detailed show notes can be found on The Data Exchange web site.
Subscribe to The Gradient Flow Newsletter.
Get every episode summarized
Each time The Data Exchange with Ben Lorica publishes, we email you a written briefing from the transcript — the topics, who appeared, and any specific claims, with the ad reads skipped.
Email me new episodesFree for 3 shows. No card needed.
Hosts & guests
No transcript yet
This episode has not been transcribed. Request it and it moves to the front of the queue.
More episodes
More from The Data Exchange with Ben Lorica
Your AI Agent Is Costing You More Than You Think
The Data Exchange with Ben Lorica
Reasoning Doesn't Start With Language
The Data Exchange with Ben Lorica
An Agent Is Just an LLM in a For-Loop
The Data Exchange with Ben Lorica
The Bloomberg Terminal for AI Compute
The Data Exchange with Ben Lorica