This episode unpacks large event models—AI that can understand, represent, and forecast real-world event sequences over time, not just generate text. We explore how LEMs extract underlying rules with schema induction, marry neural nets with symbolic planners for safety, and use sparse attention to manage massive timelines. We discuss real-world uses in public safety and healthcare, the safety nets that keep predictions grounded in reality, and imagine how a personal LEM could optimize your day.

Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.

Sponsored by Embersilk LLC

A deep dive into how AI agents move from answering questions to taking real buying actions on your behalf. We break down the surge of agentic commerce, the infrastructure that makes it possible (and the ‘invisibility’ problem), real-world wins from Klarna to IKEA, and a practical playbook to launch a simple agent in 10 days. If you want to know how data readiness and plug‑and‑play models are reshaping shopping, this episode is for you.

Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.

Sponsored by Embersilk LLC

We dive into Meta AI's Autodata framework—an autonomous system that designs, tests, and iterates its own training data. From challenger models and weak/strong solvers to meta-optimization that removes negative grading, we explore how AI becomes its own data scientist, the co-improvement of humans and machines, and what this could mean for personalized, scalable education.

Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.

Sponsored by Embersilk LLC

A radical exploration of a zero-data, self-learning AI that discovers physics and math from first principles. We unpack the ‘superlearner’ idea—an agent trained purely by reinforcement in a digital sandbox, rewarded for uncovering truths and solving constraints, with no human text or code to bias it. From Darwinian ambitions to communication via outcomes rather than language, we examine how such an intelligence could transcend human knowledge and what it means for collaboration with something we may not be able to translate. We end with the provocative question: what is the very first entirely new concept this ineffable intelligence would invent?

Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.

Sponsored by Embersilk LLC

At Stanford's Future of Mathematics Symposium (May 1–2, 2026), AI shifts from calculator to collaborator while formal methods guard every step of the proof. This episode unpacks frontier reasoning, human–AI partnerships, and the visions of leaders like Tao, Barrett, Luong, and Bubeck as we move toward AI-assisted mathematical discovery—and the translation of new insights into language our human brains can understand.

Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.

Sponsored by Embersilk LLC

Stripe has introduced Link’s wallet for agents and Stripe Issuing for agents to provide secure financial infrastructure for autonomous AI. These tools allow digital assistants to make purchases using one-time-use virtual cards or Shared Payment Tokens without ever seeing a user's actual banking details. The Link CLI serves as a developer interface to manage these transactions, offering features like spend request creation, authentication, and automated polling for approvals. Every transaction requires explicit user authorization through the Link app to ensure human control over AI spending. This ecosystem simplifies agentic commerce by handling complex fund flows and merchant abstractions for developers. Ultimately, these resources aim to build the economic infrastructure necessary for AI agents to participate safely in the global economy.

Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.

Sponsored by Embersilk LLC

Explore OpenAI’s April 2026 study The Goblin Problem, where a nerdy personality cue in GPT-5.x triggered a cascade of goblin-themed prompts. We break down how reinforcement learning and supervised fine-tuning amplified a tiny feature, why safety hinges on controlling such quirks, and how the team retired the persona to restore reliable behavior. A look at the implications for AI training, auditing, and the future of model governance.

Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.

Sponsored by Embersilk LLC

We unpack NVIDIA’s latest Nemitron 3 Nano Omni model—a compact 3B Mixture-of-Experts architecture that processes vision, audio, and text in one pass, eliminating the old relay-race latency. Learn how MoE routing preserves accuracy, delivers up to nine times higher throughput, and supports open weights for local or edge deployment. We explore practical use cases—like real-time UI interpretation on 1080p screens—and discuss how this complements larger models, shaping the next generation of responsive AI agents and workflows.

Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.

Sponsored by Embersilk LLC