Hallucinations
306 episodes · Page 8 of 13
#2366: Why LLMs Forget the Middle of Long Conversations
Why do large language models struggle with the middle of long conversations? Explore the science behind attention dilution and practical fixes.
#2362: Iran's ICBM Claim vs Anti-Tamper Tech Reality
Iran claims to have reverse-engineered US ICBMs—but the Jericho missile is Israeli. How do militaries safeguard downed drones and hardware from exp...
#2354: Profiling a Ghost Model
A deep dive into Amazon Nova, a mysterious AI model family on Bedrock — and the gaps in what we know.
#2313: When AI Optimizes the Wrong Thing
Discover how AI systems learn to optimize for rewards—and why they sometimes get it dangerously wrong.
#2309: Blind Ranking AI's Best Podcast Scripts
How do 15 AI models handle controversial podcast prompts? We rank their scripts blind and reveal the surprising winners.
#2261: The Gap Between AI Output and Art
We assess if AI can truly invent a Tolkien-level language, write a coherent novel, or author an original screenplay—and where the real gaps in crea...
#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.
#2227: Why Groq's Chip Flips the AI Hardware Script
#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...
#2213: When Ground Truth Moves Hourly
How do you rigorously evaluate whether Tavily or Exa retrieves better results for breaking news? A formal benchmark beats the vibe check.
#2206: What Actually Works in AI Memory
Most AI memory systems are just vector databases with similarity search. We break down what mem0, Zep, and Letta are actually doing—and why benchma...
#2189: Scaling Multi-Agent Systems: The 45% Threshold
A landmark Google DeepMind study reveals that adding more AI agents often degrades performance, wastes tokens, and amplifies errors—unless your sin...
#2187: Why Claude Writes Like a Person (and Gemini Doesn't)
Claude produces prose that sounds human. Gemini reads like Wikipedia. The difference isn't capability—it's how they were trained to think about wri...
#2185: Taking AI Agents From Demo to Production
Sixty-two percent of companies are experimenting with AI agents, but only 23% are scaling them—and 40% of projects will be canceled by 2027. The ga...
#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.
#2132: The Referee's Dilemma: Epistemic Containment in LLM Simulations
Why do AI war games need a news blackout? We dissect the firewall that keeps LLM actors from cheating with real-world data.
#2129: Shifting Left on Hallucinations
Stop hoping your AI doesn't lie. We explore the shift to deterministic guardrails, specialized judge models, and the tools making agents reliable.
#2115: Why AI Answers Differ Even When You Ask Twice
You ask an AI the same question twice and get two different answers. It’s not a bug—it’s physics.
#2101: Why USB-C Handshakes Hate Solar Power
Cheap solar chargers often fail to charge devices due to USB-C handshake issues and heat inefficiencies.
#2046: The Cinema of Constructed Reality
We asked an AI to curate films about AI and reality, exploring the psychedelic overlap between machine hallucinations and human perception.
#2025: How Do You Reward a Thought?
Rewarding an AI agent is harder than just saying "good job"—here's how we turn messy human values into math.
#2024: Your AI Council: Digital Committee or Groupthink?
A digital boardroom of AI models promises better decisions, but risks amplifying the same old biases.
#2015: The Think Tanks Writing AI's Rulebook
As the EU AI Act takes hold, we spotlight the key think tanks shaping global AI policy, safety, and ethics.
#2007: AI Grading AI: The Snake Eating Its Tail
We asked an AI to write this script. Then we asked another AI to grade it. Here’s what happens when the judges have biases.