Data Science at Home
Cutting through AI bullsh*t. Come join the discussion on Discord! https://discord.gg/4UNKGf3
Episodes
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Modern propaganda isn’t random noise. It’s a repeatable, engineered algorithm that starts with ideology, weaponizes identity, and manufactures conflict. Once you see the pattern, you can’t unsee it. What happens with AI?
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Some of the most asked questions on the channel. Here answered.
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Can NPCs in videogames leverage new LLM-based tech? What are the benefits? What are the costs?
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What is the state of AI and videogames? Who is considering it? What are the big fails so far? This and much more is covered in this 1st episode of AI and videogames.
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Rebuilding a defense industrial base takes 20 years and costs trillions. Tech sovereignty takes 3 years and political will. Europe is doing the hard thing and refusing the easy one. Here's why — and who's profiting from that refusal.
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Internet followed nature. Until it didn't. And became the disaster we know.
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Paragon's spyware hacked fully updated iPhones without a single click. Apple just spent $2B on tech that reads your silent speech. And your iCloud? Governments can request it with paperwork. This is what the privacy brand actually buys you.
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Your employees aren't being hacked. They're doing it themselves, one ChatGPT prompt at a time. What is it? And how to fix the biggest corporate espionage of all times.
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...Money has always been yours to spend freely. That's about to change. This episode breaks down programmable money, the technology that turns your wallet into a permission system.
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Right now, millions of people are simultaneously chatting with a system that remembers nothing, knows nothing, and resets after every message. The engineering keeping that illusion alive is actually the impressive part.
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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 ...
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.
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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.
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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 ...
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 co...
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. 🔥📊
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This episode is brought to you by Statistical Horizons At&n...
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.
Sanjoy Chowdhury reveals AI's hidden weakness: while systems can see objects and hear sounds perfectly, they can't reason across senses like humans do. His research at University of Maryland College Park, including the Meerkat model and AVTrustBench, exposes why AI recognizes worried faces and thunder separately but fails to connect them—and what this means for self-driving cars and me...
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