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.
This session from Okta and Auth0 introduces a comprehensive framework for securing AI agents, covering identity establishment, delegated API access via Token Vault, user consent for risky operations using Asynchronous Authorization (CIBA), and integration with MCP servers.
A discussion on the evolving cybersecurity landscape, covering the persistent threat of ransomware gangs adapting with AI, the critical failures in identity security highlighted by the Zestix case, the emergence of AI agents as a new class of insider threats, and the physical-world risks demonstrated by hacking humanoid robots.
In a discussion with Astro Teller, Emily Ma from X (formerly Google X) delves into the multifaceted problem of food waste and the moonshot projects developed to tackle it. They explore Project Delta, an initiative that created an "air traffic control" system for surplus food, and Project Chorus, a broader supply chain moonshot designed to give every object a voice through advanced sensors and software.
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.
Explores the mechanics of zero-click attacks, which require no user interaction, and details how the integration of autonomous AI agents can amplify these threats. The summary covers historical examples like Pegasus and proposes a multi-layered defense strategy, including AI firewalls, the principle of least privilege, and a zero-trust architecture.
Dhanji Prasanna, CTO of Block, discusses the company's AI transformation, centered on their open-source agent, Goose. He details how Goose leverages the Model Context Protocol (MCP) to automate complex workflows, saving engineers 8-10 hours weekly. Prasanna also explains Block's strategic shift to a functional organizational structure to accelerate AI adoption and shares his vision for the future, where swarms of smaller AI models will outperform today's monolithic LLMs.
Co-founders Aaron Wang and John Rytel discuss Alex, their AI recruiting partner that automates interviews and administrative tasks. They cover how AI is fixing the broken hiring market, the technical challenges of building conversational agents, and their vision for a future where AI supercharges, rather than replaces, human recruiters.
Guanhua Wang from Microsoft's DeepSpeed team explains ZeRO++, a system that tackles the communication bottleneck in large-scale LLM training. By quantizing weights and gradients, ZeRO++ reduces communication volume by 4x, leading to training speedups of over 2x, particularly in low-bandwidth and small-batch-size environments.
A deep dive into the evolution from static chatbots to dynamic, agentic AI systems. Philipp Schmid of Google DeepMind explores how to design, build, and evaluate AI agents that leverage structured outputs, function calling, and workflow orchestration with Google Gemini, covering key agentic patterns and the future of AI development.
A talk on designing a dependable AI execution layer that handles thousands of operations without constant supervision. The system is built to be precise, responsible, and action-oriented, avoiding common LLM pitfalls like burnout, memory bloat, and overthinking.
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