Jordan Dalladay's Hot Take - We Should Build ...

About the Episode On this episode, I speak to Tim Herbig, long-time product management coach, speaker, and author of the new book "Real Progress". Tim has worked with companies like StepStone, Chrono24, Deutsche Telekom and Specsavers, and has spent years helping product managers ...  Show more

On this episode, I speak to Sean O'Neill. Sean is the Chief Product & Technology Officer at Syncron, and an executive product leader with a storied career spanning companies like Amazon, Tesco, and GfK. We bond over our shared history at GfK, speak about how Amazon has influenced ...  Show more

AIs built on Large Language Models have wowed by producing particularly fluent text. However, their ability to do this is limited in many languages. As the data and resources used to train a model in a specific language drops, so does the performance of the model, meaning that fo ...  Show more

A groundbreaking Harvard study trained AI on 10 million solar systems and found it perfectly predicted orbits but completely failed to understand gravity, raising questions about whether LLMs can develop true world models. While companies pour billions into scaling, Meta's Yann L ...  Show more

When we think about machine learning today we often think in terms of immense scale — large language models that require huge amounts of computational power, for example. But one of the most interesting innovations in machine learning right now is actually happening on a really s ...  Show more

Hongyi Wang, a Senior Researcher at the Machine Learning Department at Carnegie Mellon University, joins us. His research is in the intersection of systems and machine learning. He discussed his research paper, Cuttlefish: Low-Rank Model Training without All the Tuning, on tod ...