Jul 28, 2026

The Next Frontier of AI Is Spatial Intelligence | Fei-Fei Li on a16z

Fei-Fei Li and Yunzhu Li discuss World Labs' acquisition of SceniX, focusing on building "spatial intelligence" and "large world models" to enable robots to understand and interact with the physical world. They elaborate on SceniX's "real-to-sim-to-real" pipeline, emphasizing how simulation, coupled with generative models like Marble, addresses the data bottleneck in robotics by providing consistent, scalable, and efficient training and evaluation environments. The conversation covers the role of counterfactual reasoning, the development of robotics foundation models, and the strategic focus on semi-structured environments for pragmatic, reliable robot deployment.

Jul 28, 2026

Green AI: Making Machine Learning Environmentally Sustainable • Charles Humble • YOW! 2025

Charles Humble explores the significant environmental impact of AI, particularly generative AI, on global carbon emissions. He offers practical, actionable strategies across the entire AI lifecycle—from project planning and data collection to training and deployment—to reduce this footprint. Key recommendations include questioning the necessity of AI solutions, choosing open-source models, leveraging carbon-aware computing for demand shifting, employing model compression techniques like distillation and quantization, and integrating sustainability as a fundamental architectural principle in software engineering.

Jul 24, 2026

Building Closed-Loop Evals for a Multimodal Agent at Scale — Soumya Gupta & Jai Chopra, Uber

This talk details how Uber Eats designed and implemented a multimodal AI agent to enhance food photography for independent merchants, addressing challenges like maintaining authenticity, merchant brand, and marketplace diversity while operating at scale. It covers the intricate evaluation strategies for routing and image editing agents, including continuous learning loops, managing drift, countering reward hacking, and balancing creative freedom with rigid safety guardrails. The speakers explain how they built a closed feedback loop combining offline human labeling, internal dogfooding, and online production signals to ensure robust and adaptive performance.

Jul 23, 2026

AI, Corporate Responsibility & Democratic Legitimacy: Extended Q&A • Joanna Bryson • GOTO 2025

Joanna Bryson challenges popular AI assumptions, positing current generative AI as powerful tools for cultural knowledge compression, not autonomous intelligences. She emphasizes that AI's capabilities are nearing the human knowledge frontier, requiring focus on human coordination and governance. Bryson critically examines AI's impact on mental health and law, advocating for data-driven regulation and comprehensible systems. She calls for engineering activism, asserting human agency over technological determinism and stressing the importance of transparency and critical thinking in shaping AI's future.

Jul 21, 2026

2026 State of AI Engineering — Barr Yaron, Amplify Partners

Barr Yaron's 2026 AI engineering survey reveals key trends: audio and image generation are rapidly gaining traction, while cost is now a primary engineering constraint. Agents are evolving to take actions within systems, but control mechanisms remain primitive. Evaluation (eval) is still the top infrastructure challenge. AI positively impacts job satisfaction and experimentation but also raises concerns about technical skill erosion and non-developers shipping code, fundamentally changing engineering culture. Predictions include a likely AGI declaration within five years and a shift away from Transformers as state-of-the-art.

Jul 21, 2026

Is Fine-Tuning Still Needed? LLMs, RAG, & LoRA

This summary explores the evolving role of fine-tuning in modern AI workflows, comparing it with advanced techniques like RAG, LoRA, and enhanced generative AI capabilities. It discusses the historical benefits, current limitations due to rapidly advancing frontier models, and outlines a practical decision framework for customizing machine learning models and designing efficient AI systems.