podcast Archives - Page 2 of 34 - Data Science at Home Podcast

February 3, 2026

February 3, 2026

More powerful deep learning with transformers (Ep. 84) (Rebroadcast)

Some of the most powerful NLP models like BERT and GPT-2 have one thing in common: they all use the transformer architecture. Such architecture is built on top of another important concept already known to the community: self-attention.In this episode I ...

February 3, 2026

Your Favorite AI Startup is Probably Bullshit (Ep. 298) [RB]

The brutal truth about why Silicon Valley is blowing billions on glorified autocomplete while pretending it’s the next iPhone. We’re diving deep into the AI investment […]

February 3, 2026

Why AI Researchers Are Suddenly Obsessed With Whirlpools (Ep. 297) [RB]

VortexNet uses actual whirlpools to build neural networks. Seriously. By borrowing equations from fluid dynamics, this new architecture might solve deep learning’s toughest problems—from vanishing gradients […]

February 3, 2026

AGI: The Dream We Should Never Reach (Ep. 296)

Also on YouTube Two AI experts who actually love the technology explain why chasing AGI might be the worst thing for AI’s future—and why the […]

December 23, 2025

When Data Stops Being Code and Starts Being Conversation (Ep. 297)

Mark Brocato built Mockaroo—the tool that taught millions of developers how to fake data. Now, as Head of Engineering at Tonic.ai, he’s building the AI agent […]

November 26, 2025

Your AI Strategy is Burning Money: Here’s How to Fix It (Ep.295)

Most companies don’t have an AI problem. They have a decision-making problem. Matt Lea, founder of Schematical and CloudWarGames, has spent nearly 20 years helping tech […]

November 12, 2025

From Tokens to Vectors: The Efficiency Hack That Could Save AI (Ep. 294)

LLMs generate text painfully slow, one low-info token at a time. Researchers just figured out how to compress 4 tokens into smart vectors & cut costs […]

October 30, 2025

Why AI Researchers Are Suddenly Obsessed With Whirlpools (Ep. 293)

VortexNet uses actual whirlpools to build neural networks. Seriously. By borrowing equations from fluid dynamics, this new architecture might solve deep learning’s toughest problems—from vanishing gradients […]

October 23, 2025

The Scientists Growing Living Computers in Swiss Labs (Ep. 292)

Fred Jordan, Co-CEO of FinalSpark, takes us inside the radical world of biological computing, where real neurons extracted from human tissue are being trained to solve […]