Please turn JavaScript on
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) icon

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

Subscribe to The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)’s news feed.

Click on “Follow” and decide if you want to get news from The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) via RSS, as email newsletter, via mobile or on your personal news page.

Subscription to The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) comes without risk as you can unsubscribe instantly at any time.

You can also filter the feed to your needs via topics and keywords so that you only receive the news from The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) which you are really interested in. Click on the blue “Filter” button below to get started.

Website title: TWIML | The Voice of Machine Learning and Artificial Intelligence

Is this your feed? Claim it!

Publisher:  Unclaimed!
Message frequency:  0.08 / day

Message History

In this episode, Sam talks with Dev Rishi, GM of AI at Rubrik, about what happens when agents move beyond answering questions and start taking action across tools, systems, and business processes.

We explore why the enterprise playbook of static guardrails plus human approval starts to break down in the agent era. Agents are useful because they can plan, call tools, up...


Read full story

As context windows grow into the millions of tokens, many AI practitioners are questioning whether retrieval-augmented generation (RAG) is still necessary. If modern models can ingest entire libraries of documents, why bother with retrieval at all?

In this episode, Alex Bowcut, Head of Engineering at Sphere, explains why the answer depends on the application. Sphere us...


Read full story

In this episode, Jure Leskovec, co-founder and chief scientist at Kumo and professor of computer science at Stanford, joins us to explore two fronts of his work: AI for science and relational deep learning. We begin with AI Virtual Cell, a multiscale effort to learn data-driven representations from proteins to cells to patients using single-cell RNA-seq data, protein language...


Read full story