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 circus where VCs who can't code are funding companies that barely understand their own technology. From blockchain déjà vu to the "ChatGPT wrapper" economy—this episode will make you question every AI valuation you've ever seen.

Fair warning: We're naming names and calling out the hype. Don't listen if you work at a "revolutionary AI startup" that's just OpenAI's API with a pretty interface.

#AIBubble #VentureCapital #TechReality #StartupBullshit

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 to long-range dependencies. Today we explain how vortex shedding, the Strouhal number, and turbulent flows might change everything in AI.

Sponsors

This episode is brought to you by Statistical Horizons At Statistical Horizons, you can stay ahead with expert-led livestream seminars that make data analytics and AI methods practical and accessible. Join thousands of researchers and professionals who’ve advanced their careers with Statistical Horizons. Get $200 off any seminar with code DATA25 at https://statisticalhorizons.com

References

https://samim.io/p/2025-01-18-vortextnet/

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 current hype cycle could kill the field we're trying to save. Want to dive deeper? Head to datascienceathome.com for detailed show notes, code examples, and exclusive deep-dives into the papers we discuss.

Subscribe to our newsletter for weekly breakdowns of cutting-edge research delivered straight to your inbox—no fluff, just science!

📧 Join the conversation!

Our Discord community is full of ML engineers, researchers, and AI enthusiasts discussing papers, sharing projects, and helping each other level up. Whether you're debugging your first neural net or training your tenth transformer, there's a place for you.

Link in the show notes! 💬

Newsletter https://datascienceathome.substack.com/subscribe

Website https://datascienceathome.com

References

CEO is obsolete

AI is the new blockchain

Dr Eliseo Ferrante NYU

https://nyuad.nyu.edu/en/academics/divisions/science/faculty/eliseo-ferrante.html

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 that's making his own creation obsolete. In this episode, we explore why static test data can't survive the AI era, what it means to "negotiate" datasets with an agent instead of scripting them, and whether we're heading toward a future where sandbox environments vanish entirely. From the hidden failures of legacy mocks to the security implications of agent-driven synthesis, Mark reveals what happens when data generation becomes a conversation—not a pipeline.

Sponsors

Tonic.ai Synthetic data solutions for software and AI development. Accelerate engineering velocity and ensure compliance with AI-powered data synthesis

This episode is brought to you by Statistical Horizons At Statistical Horizons, you can stay ahead with expert-led livestream seminars that make data analytics and AI methods practical and accessible. Join thousands of researchers and professionals who’ve advanced their careers with Statistical Horizons. Get $200 off any seminar with code DATA25 at https://statisticalhorizons.com

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 leaders ship smarter.

In this conversation, he breaks down when AI actually makes sense, where AWS costs spiral out of control, and why your "cool demo" keeps dying before launch. If you're tired of AI hype and ready for straight answers, hit play. Join the conversation! Our Discord community is full of ML engineers, researchers, and AI enthusiasts discussing papers, sharing projects, and helping each other level up. Whether you're debugging your first neural net or training your tenth transformer, there's a place for you.

Newsletter https://datascienceathome.substack.com/subscribe

Website https://datascienceathome.com

References

http://schematical.com

https://cloudwargames.com

https://schematical.com/posts/we-need-ai_20241028

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 by 44%—with full code & proofs! Meanwhile OpenAI drops product ads, not papers. We explore CALM & why open science matters. 🔥📊

Sponsors

This episode is brought to you by Statistical Horizons At Statistical Horizons, you can stay ahead with expert-led livestream seminars that make data analytics and AI methods practical and accessible. Join thousands of researchers and professionals who’ve advanced their careers with Statistical Horizons. Get $200 off any seminar with code DATA25 at https://statisticalhorizons.com

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 to long-range dependencies. Today we explain how vortex shedding, the Strouhal number, and turbulent flows might change everything in AI.

Sponsors

This episode is brought to you by Statistical Horizons At Statistical Horizons, you can stay ahead with expert-led livestream seminars that make data analytics and AI methods practical and accessible. Join thousands of researchers and professionals who’ve advanced their careers with Statistical Horizons. Get $200 off any seminar with code DATA25 at https://statisticalhorizons.com

References

https://samim.io/p/2025-01-18-vortextnet/