300 episodes

Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders.

Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader.

Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning, computer science, data science and more.

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Sam Charrington

    • Technology

Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders.

Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader.

Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning, computer science, data science and more.

    Metric Elicitation and Robust Distributed Learning with Sanmi Koyejo - #352

    Metric Elicitation and Robust Distributed Learning with Sanmi Koyejo - #352

    The unfortunate reality is that many of the most commonly used machine learning metrics don't account for the complex trade-offs that come with real-world decision making. This is one of the challenges that Sanmi Koyejo, assistant professor at the University of Illinois, has dedicated his research to address. Sanmi applies his background in cognitive science, probabilistic modeling, and Bayesian inference to pursue his research which focuses broadly on “adaptive and robust machine learning.”

    • 55 min
    High-Dimensional Robust Statistics with Ilias Diakonikolas - #351

    High-Dimensional Robust Statistics with Ilias Diakonikolas - #351

    Today we’re joined by Ilias Diakonikolas, faculty in the CS department at the University of Wisconsin-Madison, and author of the paper Distribution-Independent PAC Learning of Halfspaces with Massart Noise, recipient of the NeurIPS 2019 Outstanding Paper award. The paper is regarded as the first progress made around distribution-independent learning with noise since the 80s. In our conversation, we explore robustness in ML, problems with corrupt data in high-dimensional settings, and of course, the paper.

    • 34 min
    How AI Predicted the Coronavirus Outbreak with Kamran Khan - #350

    How AI Predicted the Coronavirus Outbreak with Kamran Khan - #350

    Today we’re joined by Kamran Khan, founder & CEO of BlueDot, and professor of medicine and public health at the University of Toronto. BlueDot has been the recipient of a lot of attention for being the first to publicly warn about the coronavirus that started in Wuhan. How did the company’s system of algorithms and data processing techniques help flag the potential dangers of the disease? In our conversation, Kamran talks us through how the technology works, its limits, and the motivation behind the wor

    • 50 min
    Turning Ideas into ML Powered Products with Emmanuel Ameisen - #349

    Turning Ideas into ML Powered Products with Emmanuel Ameisen - #349

    Today we’re joined by Emmanuel Ameisen, machine learning engineer at Stripe, and author of the recently published book “Building Machine Learning Powered Applications; Going from Idea to Product.” In our conversation, we discuss structuring end-to-end machine learning projects, debugging and explainability in the context of models, the various types of models covered in the book, and the importance of post-deployment monitoring. 

    • 42 min
    Algorithmic Injustices and Relational Ethics with Abeba Birhane - #348

    Algorithmic Injustices and Relational Ethics with Abeba Birhane - #348

    Today we’re joined by Abeba Birhane, PhD Student at University College Dublin and author of the recent paper Algorithmic Injustices: Towards a Relational Ethics, which was the recipient of the Best Paper award at the 2019 Black in AI Workshop at NeurIPS. In our conversation, break down the paper and the thought process around AI ethics, the “harm of categorization,” how ML generally doesn’t account for the ethics of various scenarios and how relational ethics could solve the issue, and much more.

    • 41 min
    AI for Agriculture and Global Food Security with Nemo Semret - #347

    AI for Agriculture and Global Food Security with Nemo Semret - #347

    Today we’re excited to kick off our annual Black in AI Series joined by Nemo Semret, CTO of Gro Intelligence. Gro provides an agricultural data platform dedicated to improving global food security, focused on applying AI at macro scale. In our conversation with Nemo, we discuss Gro’s approach to data acquisition, how they apply machine learning to various problems, and their approach to modeling.

    • 1 hr 6 min

Customer Reviews

Minter Dial ,

AI brought to the masses

Sam Charrington has helped bring AI to the masses with his insightful podcasts filled with great guests.

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Excellent Perspectives in Machine Learning

Love this podcast. Give it a try.

joemac doeintosh ,

Umm shlip

Great podcast. Way to many “ums”
“Ah” and “likes” and lip smacks. If you’re hypersensitive or misophonic, oh boy/girl god bless you. Um ah anyone um in the profession looking for Um a relative podcast to there um profession this is um is it! Smack!

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