Today we’re joined by Nir Bar-Lev, co-founder and CEO of ClearML.
In our conversation with Nir, we explore how his view of the wide vs deep machine learning platforms paradox has changed and evolved over time, how companies should think about building vs buying and integration, and his thoughts on why experiment management has become an automatic buy, be it ... Show More
Jan 29
The Evolution of Reasoning in Small Language Models with Yejin Choi - #761
Today, we're joined by Yejin Choi, professor and senior fellow at Stanford University in the Computer Science Department and the Institute for Human-Centered AI (HAI). In this conversation, we explore Yejin’s recent work on making small language models reason more effectively. We ... Show More
1h 6m
Dec 17
Rethinking Pre-Training for Agentic AI with Aakanksha Chowdhery - #759
Today, we're joined by Aakanksha Chowdhery, member of technical staff at Reflection, to explore the fundamental shifts required to build true agentic AI. While the industry has largely focused on post-training techniques to improve reasoning, Aakanksha draws on her experience lea ... Show More
52m 54s
May 2024
2882: From Chess Grandmaster to ML Innovator: Tal Shaked's Journey
<p>Are machines really capable of thinking like humans, or are we merely programming them to mimic our own patterns? Today on Tech Talks Daily, we delve into this intriguing question with Tal Shaked, an American chess grandmaster and Chief Machine Learning Officer at Moloco, a le ... Show More
29m 26s
Mar 2022
Bayesian Machine Learning with Ravin Kumar (Ep. 191)
<p>This is one episode where passion for math, statistics and computers are merged.
I have a very interesting conversation with Ravin, data scientist at Google where he uses data to inform decisions.</p>
<p>He has previously worked at Sweetgreen, designing systems that would b ... Show More
31m 12s
Feb 2024
#179 Why ML Projects Fail, and How to Ensure Success with Eric Siegel, Founder of Machine Learning Week, Former Columbia Professor, and Bestselling Author
We are in a Generative AI hype cycle. Every executive looking at the potential generative AI today is probably thinking about how they can allocate their department's budget to building some AI use cases. However, many of these use cases won't make it into production.In a similar ... Show More
48 m
Sep 2021
Declarative Machine Learning Without The Operational Overhead Using Continual
<div class="wp-block-jetpack-markdown"><h2>Summary</h2>
<p>Building, scaling, and maintaining the operational components of a machine learning workflow are all hard problems. Add the work of creating the model itself, and it’s not surprising that a majority of companies th ... Show More
1h 11m
