Training Data
322 episodes · Page 9 of 14
#2377: Is Geopolitical Neutrality a Sustainable AI Strategy?
How DeepSeek carved a niche with efficiency, neutrality, and innovative dialogue handling — and what it means for AI's future.
#2374: How Granular Can MoE Experts Get?
Exploring the limits of expert granularity in Mixture of Experts models—how narrow can segmentation go before efficiency or accuracy suffers?
#2368: The Multi-Stage Pipeline Behind Netflix's Recommendations
Unpacking the multi-stage AI pipeline behind Netflix, Spotify, and Amazon’s "you might also like" suggestions—from candidate generation to real-tim...
#2357: Microsoft's Phi: When Data Quality Beats Model Size
Explore Microsoft AI's Phi family of small language models, designed for edge deployment and high efficiency.
#2335: Staking Out the Middle: UK's Post-Brexit AI Strategy
The UK’s £500M Sovereign AI Fund is a bold move to boost domestic AI startups with compute access, visas, and strategic partnerships. How does it s...
#2316: Who’s Building AI’s Next Training Data?
How boutique dataset firms are reshaping AI training, from rights-cleared content to domain-specific precision.
#2315: How to Update AI Models Without Starting Over
Exploring the challenge of updating AI models with new knowledge without costly full retraining.
#2311: Danish AI: Bridging the Localization Gap
How does AI handle Danish? Explore the challenges and progress in making AI tools work for small-language populations.
#2307: Inside Frontier LLM Training: Stages, Costs, and Checkpoints
Discover the multi-stage process of training frontier large language models, from pretraining to post-training, and why checkpoints are the key to ...
#2306: Can LLM Councils Truly Capture Diverse Worldviews?
Exploring whether LLM councils can achieve genuine worldview diversity or if alignment processes erase meaningful differences.
#2272: The AI Transcription Sweet Spot
Does higher-quality audio make AI transcription worse? New research reveals a surprising "sweet spot" for bitrate, challenging a core assumption of...
#2246: Constitutional AI: Anthropic's Theory of Safe Scaling
How Anthropic's Constitutional AI replaces human raters with AI self-critique guided by explicit principles—and what it assumes about the future of...
#2239: How AI Benchmarks Became Broken (And What's Replacing Them)
The tests we use to measure AI progress are contaminated, saturated, and gamed. Here's what's actually working.
#2224: Why AI Can't Crack the Voynich Manuscript
A fifteenth-century text has defeated cryptanalysts, linguists, and AI models alike. What does its resistance tell us about language, encoding, and...
#2196: The Invisible Workforce Behind AI
Annotation is the invisible foundation of AI—and a $17B industry by 2030. Here's what dataset curators actually need to know about the tools, platf...
#2195: Nash's Real Genius (And Why the Movie Got It Wrong)
The bar scene in A Beautiful Mind is mathematically wrong—and it obscures Nash's actual breakthrough. We trace the real ideas from his 1950 papers ...
#2188: Is Emergence Real or Just Bad Metrics?
The debate over whether AI models exhibit genuine emergent abilities or just appear to because of how we measure them—and why it matters for safety...
#2136: The Brutal Problem of AI Wargame Evaluation
Most AI wargame simulations skip evaluation entirely or rely on token expert reviews. This is the field's biggest credibility problem.
#2109: AI Is Forcing You to Use React
AI tools are reshaping developer stacks, favoring React and Postgres over niche frameworks.
#2092: Why AI Thinks You're American (Even When You're Not)
Even when we tell Gemini we're in Jerusalem, it defaults to US-centric assumptions. We explore the root causes of this persistent AI bias.
#2089: Open-Source vs. Military ATR: The Drone Recognition Gap
A public GitHub model spotted by a listener reveals the massive gap between hobbyist AI and lethal military drone detection systems.
#2076: Is Pure NLP Dead? The Hidden Scaffolding of AI
Modern AI didn't appear from nowhere. Discover how decades of linguistic rules and statistical models built the foundation for today's LLMs.
#2066: The Transformer Trinity: Why Three Architectures Rule AI
Why did decoder-only models like GPT dominate AI, while encoders and encoder-decoders still hold critical niches?
#2064: Why GPT-5 Is Stuck: The Data Wall Explained
The "bigger is better" era of AI is over. Here's why the industry hit a data wall and shifted to a new scaling law.