Microsoft Research Podcast – Details, episodes & analysis

Podcast details

Technical and general information from the podcast's RSS feed.

Microsoft Research Podcast

Researchers across the Microsoft research community

Technology

Frequency: 1 episode/13d. Total Eps: 200

An ongoing series of conversations bringing you right up to the cutting edge of Microsoft Research.

Site

RSS

Apple

Recent rankings

Latest chart positions across Apple Podcasts and Spotify rankings.

Apple Podcasts

🇺🇸 USA - technology
26/07/2025
#96
🇺🇸 USA - technology
23/07/2025
#86
🇺🇸 USA - technology
22/07/2025
#81
🇺🇸 USA - technology
21/07/2025
#77
🇺🇸 USA - technology
20/07/2025
#61
🇺🇸 USA - technology
19/07/2025
#52
🇨🇦 Canada - technology
18/07/2025
#91
🇺🇸 USA - technology
18/07/2025
#48
🇨🇦 Canada - technology
27/04/2025
#96
🇨🇦 Canada - technology
09/03/2025
#80

Spotify

No recent rankings available

Shared links between episodes and podcasts

Links found in episode descriptions and other podcasts that share them.

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https://www.microsoft.com/research
74 shares
https://www.microsoft.com/en-us/research/people/allorens/
7 shares
https://www.microsoft.com/en-us/research/people/teevan/
7 shares

https://www.linkedin.com/in/brian-houck-b4123132/
1 share
https://www.linkedin.com/in/mbrodsky-ph-d/
1 share
https://www.linkedin.com/in/ginger-hudson-1a6b52
1 share

https://github.com/universal-ner/universal-ner
1 share
https://github.com/QizhiPei/FABind
1 share
https://github.com/microsoft/LongRoPE
1 share

RSS feed quality and score

Technical evaluation of the podcast's RSS feed quality and structure.

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RSS feed quality

To improve

Score global : 42%

Publication history

Monthly episode publishing history over the past years.

Year

Latest published episodes

Recent episodes with titles, durations, and descriptions.

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Abstracts: November 14, 2024

Thursday, November 14, 2024 • Duration 13:30

The efficient simulation of molecules has the potential to change how the world understands biological systems and designs new drugs and biomaterials. Tong Wang discusses AI2BMD, an AI-based system designed to simulate large biomolecules with speed and accuracy.

Read the paper

Get the code

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Collaborators: Prompt engineering with Siddharth Suri and David Holtz

Monday, November 11, 2024 • Duration 55:26

Researcher Siddharth Suri and professor David Holtz give a brief history of prompt engineering, discuss the debate behind their recent collaboration, and share what they found from studying how people’s approaches to prompting change as models advance.

Learn more:

As Generative Models Improve, People Adapt Their Prompts | Publication, July 2024
AI, Cognition, and the Economy (AICE) | Initiative page

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What’s Your Story: Emre Kiciman

Thursday, August 1, 2024 • Duration 40:05

Emre Kiciman shares how some keen observations and a desire to have front-end impact led him to make the jump from systems and networking to computational social science and now causal analysis and large-scale AI—and how systems thinking still impacts his work.

Learn more:

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101 - Going meta: learning algorithms and the self-supervised machine with Dr. Philip Bachman

Wednesday, December 4, 2019 • Duration

Deep learning methodologies like supervised learning have been very successful in training machines to make predictions about the world. But because they’re so dependent upon large amounts of human-annotated data, they’ve been difficult to scale. Dr. Phil Bachman, a researcher at MSR Montreal, would like to change that, and he’s working to train machines to collect, sort and label their own data, so people don’t have to.

Today, Dr. Bachman gives us an overview of the machine learning landscape and tells us why it’s been so difficult to sort through noise and get to useful information. He also talks about his ongoing work on Deep InfoMax, a novel approach to self-supervised learning, and reveals what a conversation about ML classification problems has to do with Harrison Ford’s face.