Oct 23, 2026

Self-replicating AI, your doctor’s chatbot & agents at dinner

A look at three developments shaping the near future of AI: Anthropic's research on small models that could copy themselves across cheap consumer devices, a JAMA study showing how doctors are adopting clinical chatbots for treatment decisions, and an experiment in which AI agents booked a founders' dinner and then networked with one another. The panel weighs security risks in the home, the trade-offs of speed versus verification in medicine, and how much autonomy agents should have when dealing with other agents.

Oct 02, 2026

Stop Renting Your AI's Memory — Dylan Couzon, Qdrant

Dylan Couzon, developer relations engineer at Qdrant, argues that frontier-class models now run on hardware people can own, but the memory that makes an assistant personal still lives in someone else's cloud. He covers why owning inference gives autonomy while owning memory gives continuity, the CPU, RAM, and disk model for LLMs, why retrieval beats stuffing everything into the prompt, and a live offline demo of a drone building searchable memory with Qdrant Edge, plus opt-in shared memory.

Sep 18, 2026

A Worm With 302 Neurons Inspired Their Architecture — Ramin Hasani, Liquid AI

Liquid AI began by drawing inspiration from the C. elegans worm to develop Liquid Neural Networks, focusing on continuous-time, differentiable systems for efficient intelligence. Facing scaling challenges, they developed STAR, a meta-AI system for architectural search, optimizing hybrid models for quality, memory, latency, and speed, leading to their Liquid Foundation Models (LFMs). Their core focus is on bringing AI to the edge (on-device, in-car) for cost reduction, privacy, and offline capabilities, with successful deployments at Mercedes-Benz and Shopify. Liquid is also building a platform for 'sovereign AI,' enabling enterprises to customize and own their intelligence end-to-end, and researching massively multimodal systems, efficiency across the AI stack, and novel data types like DNA.

Sep 15, 2026

How Physical AI Learns Across Language, Video and Action — Ming-Yu Liu

Ming-Yu Liu from NVIDIA details Cosmos 3, a novel omnimodal world model capable of video description, generation, and robot action synthesis. He elaborates on its architecture, which integrates a vision language model with a bidirectional diffusion generator. Liu defines world models as a collection of tools comprising forward dynamics, inverse dynamics, and policy, trained synergistically. The discussion covers learning robot skills from human video, the crucial role of neural simulators like Cosmos Dreams for policy verification and accelerating development in robotics, and the importance of safety for future humanoid robots. The episode concludes by outlining the Super, Nano, and Edge model sizes and NVIDIA's commitment to open-sourcing Cosmos's code, models, and data.

Aug 07, 2026

Compression at the Edge — Chris Alexiuk, NVIDIA

This panel discussion explores the critical role of model compression, particularly quantization, in democratizing AI. It delves into how massive models like GLM 5.2 can be shrunk by over 80% without equivalent performance loss, thanks to techniques like mixed-precision quantization and understanding uneven layer importance. The discussion covers NVIDIA's NVFP4 format, challenges posed by new model architectures, the preference for KL divergence over accuracy benchmarks, and the vision of future AI running efficiently on all local devices.

Aug 05, 2026

Building the First Data Centers in Space

Philip Johnston, co-founder and CEO of StarCloud, discusses their pioneering efforts to build data centers in space to address AI's energy demands and terrestrial constraints. He details StarCloud-1's groundbreaking mission, which launched an Nvidia H100 GPU into orbit, and the engineering challenges overcome, such as thermal management and radiation hardening. The conversation covers their ambitious plan for a 88,000-satellite constellation, the shift in investor sentiment towards hard tech, and key advice for deep tech founders.