Data Science at Home

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.   Buy me a coffee https://ko-fi.com/datascience   ✨ Connect with us! Personal newsletter: https://defragzone.substack.com 📩 Newsletter: https://datascienceathome.substack.com 🎙 Podcast: Available on Spotify, Apple Podcasts, and more. 🐦 Twitter: @DataScienceAtHome 📘LinkedIn: https://www.linkedin.com/in/fragadaleta/ Instagram: https://www.instagram.com/datascienceathome/ Facebook: https://www.facebook.com/datascienceAH LinkedIn: https://www.linkedin.com/company/data-science-at-home-podcast Discord Channel: https://discord.gg/4UNKGf3   NEW TO DATA SCIENCE AT HOME? Welcome! Data Science at Home explores the latest in AI, data science, and machine learning. Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews, and discussions. Learn more at https://datascienceathome.com   SEND US MAIL! We love hearing from you! Send us mail at: hello@datascienceathome.com Don’t forget to like, subscribe, and hit the 🔔 for updates on the latest in AI and data science!  

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.   Buy me a coffee https://ko-fi.com/datascience   ✨ Connect with us! Personal newsletter: https://defragzone.substack.com 📩 Newsletter: https://datascienceathome.substack.com 🎙 Podcast: Available on Spotify, Apple Podcasts, and more. 🐦 Twitter: @DataScienceAtHome 📘LinkedIn: https://www.linkedin.com/in/fragadaleta/ Instagram: https://www.instagram.com/datascienceathome/ Facebook: https://www.facebook.com/datascienceAH LinkedIn: https://www.linkedin.com/company/data-science-at-home-podcast Discord Channel: https://discord.gg/4UNKGf3   NEW TO DATA SCIENCE AT HOME? Welcome! Data Science at Home explores the latest in AI, data science, and machine learning. Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews, and discussions. Learn more at https://datascienceathome.com   SEND US MAIL! We love hearing from you! Send us mail at: hello@datascienceathome.com Don’t forget to like, subscribe, and hit the 🔔 for updates on the latest in AI and data science!  

A deep look at programmable money and how digital cash could turn wallets into permission systems. Contrasts unconditional physical cash with centrally controlled CBDCs. Explores claimed benefits like crime reduction and faster payments alongside risks such as instant freezes, expiries, surveillance, and political control. Compares approaches in Europe, China, and beyond.

A deep dive into how large language models are served at scale, including model weight sizes, GPU setups, and context window limits. The conversation covers trade-offs between latency, throughput, memory, and cost. Learn about model parallelism, KV cache mechanics, continuous batching, prompt caching, and practical patterns for short, mid, and long-term memory.

A sharp takedown of the AI funding frenzy and why many startups are glorified wrappers around existing models. A critique of investors mistaking pattern matching for technical understanding. Parallels drawn to past hype cycles like blockchain. Discussion of inflated valuations, the raise-build-sell playbook, and where real research gets drowned out by buzzword products.

They unpack a neural architecture inspired by whirlpools and fluid dynamics. They explore how vortex dynamics, Strouhal tuning, and complex-valued layers could tackle vanishing gradients and long-range dependencies. They discuss adaptive damping, dynamic memory via attractor states, and practical hurdles for putting physics-inspired networks into practice.

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  

In this episode, Matt Lee, founder of Schematical and CloudWarGames, shares nearly 20 years of wisdom on AI and cloud infrastructure. He reveals that many companies struggle with decision-making rather than AI challenges. Matt explains when AI truly adds value, how to cut costs with strategic model choices, and the importance of manual testing before automation. He discusses common pitfalls in AI spending and offers practical advice on framing AI projects in financial terms to win over CTOs and boards.

Dive into the game-changing concept of Continuous Autoregressive Language Models, which compress tokens into vectors for speedier text generation. Discover how this innovation can slash AI costs by 44%. The discussion covers the implications of a likelihood-free training method and introduces the concept of semantic bandwidth for denser information. Also explored are the environmental benefits of more efficient models and the importance of open science versus corporate secrecy in research. Perfect for tech enthusiasts eager for the latest in AI advancements!