An expert overview of post-training techniques for language models, covering the entire workflow from data generation and curation to advanced algorithms like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning (RL), along with practical advice on evaluation and iteration.
This talk introduces Anaximander, a system designed to bridge the gap between traditional, GUI-driven Geographic Information System (GIS) workflows and modern, code-heavy machine learning practices. Anaximander integrates geospatial foundation models directly into QGIS, allowing experts to interactively orchestrate, run, and evaluate models for tasks like semantic segmentation and object detection on satellite imagery.
At Applied Compute, efficient Reinforcement Learning is critical for delivering business value. This talk explores the transition from inefficient synchronous RL to a high-throughput asynchronous 'Pipeline RL' system. The core challenge is managing 'staleness'—a side effect of in-flight weight updates that can destabilize training. The speakers detail their first-principles systems model, based on the Roofline model, used to simulate and find the optimal allocation of GPU resources between sampling and training, balancing throughput with algorithmic stability and achieving significant speedups.
A deep dive into the key takeaways from CES 2026, covering the surge in humanoid robotics and the evolution of software-defined vehicles, followed by a nuanced analysis of the shifting US-China export controls on advanced AI chips.
Robert from OpenAI discusses the critical role of structured evaluations (evals) and graders for developing advanced collaborative agents. He explores the limitations of 'vibe-based' assessments, introduces a maturity model for evals, and presents a comprehensive rubric for measuring agent performance beyond simple accuracy, connecting these concepts to the power of Reinforcement Fine-Tuning (RFT).
Karan Goel, CEO of Cartesia, discusses the fundamental limitations of Transformer architectures, arguing they behave more like retrieval systems than learning systems. He explains how State Space Models (SSMs) enable compression and abstraction, and why Cartesia is tackling multimodal intelligence by first solving for voice AI, aiming to develop a transferable 'recipe' for end-to-end representation learning.
Nikesh Arora, CEO of Palo Alto Networks, shares his unconventional journey and leadership philosophy. He provides a masterclass in building a multi-platform company through strategic M&A, explains why founders should sometimes ignore customers, and reveals how to lead with conviction while managing imposter syndrome.
Stewart Butterfield, co-founder of Slack and Flickr, shares the product frameworks and leadership principles that guided his success. He delves into concepts like "utility curves" for feature investment, the "owner's delusion" in product design, and why focusing on "comprehension" is often more important than reducing friction. He also introduces powerful mental models for organizational effectiveness, such as combating "hyper-realistic work-like activities" and applying Parkinson's Law to team growth.
Intuit CEO Sasan Goodarzi discusses the operational playbook for reinventing a 40-year-old company, from its slow transition to SaaS to its early adoption of AI. He shares insights on winning the SMB market by treating small businesses like consumers, building effective channel partnerships, and developing a platform strategy. Goodarzi also details his leadership philosophy, emphasizing that grit and curiosity are more critical than raw talent.
Steve Yegge and Gene Kim discuss the current limitations of AI coding assistants, predicting a shift from simple code completion "power tools" to sophisticated, agent-based "CNC machines" that will automate the entire software development lifecycle. They explore the cultural resistance from senior engineers, the transformative impact on team structures, and the emergence of "Vibe Coding" as a new paradigm that will reshape technology organizations.
Researchers and product managers from OpenAI discuss the evolution from GPT-5 to GPT-5.1, detailing the shift to universal reasoning models. They explore the nuanced concept of model "personality," the technical challenges of balancing steerability with safety, and how features like Memory and improved context awareness are creating more emotionally intelligent and personalized AI interactions.
Researchers Christina Kim and Laurentia Romaniuk discuss the development of GPT-5.1, focusing on the shift to making reasoning models the default. They explore the nuanced concept of model "personality" as a blend of response style and the entire user experience, and detail the ongoing work of balancing model steerability with safety.
OpenAI's Christina Kim (Research) and Laurentia Romaniuk (Product) discuss the development of GPT-5.1, detailing the shift to universal "reasoning models" to enhance both IQ and EQ. They explore the nuances of "model personality," the technical challenges of balancing steerability with safety, and how features like Memory create a more personalized, context-aware user experience.
Co-founders Jenn Wortman Vaughan and Hanna Wallach reflect on 20 years of the Women in Machine Learning (WiML) workshop, discussing its origins, their parallel careers in responsible AI, and the future challenges of evaluating generative AI and fostering critical thought.
Jenn Wortman Vaughan and Hanna Wallach, co-founders of the Women in Machine Learning (WiML) workshop, reflect on their intersecting careers, the founding and evolution of WiML over 20 years, and their influential research in responsible AI, from interpretability and fairness to the current challenges in generative AI.
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