Cyrus Clarke, a researcher at the MIT Media Lab, connected an OpenClaw agent to a 900-pin shape display and asked it to discover its own identity rather than complete tasks. The talk covers the agent's first behaviors, the public reaction, the philosophical influences behind his approach to AI embodiment, and a closed-loop system that taught the agent a gesture-based body language that now responds faster than its language model.
Jason Ma, CTO of Dyna Robotics, discusses overcoming the 80-90% success rate ceiling in generalist robots for commercial viability. He details how Dyna-1 achieved a 99.4% success rate in napkin folding through a novel reward model for progress scoring, enabling scalable supervision and a human-in-the-loop active learning cycle for targeted error recovery, alongside their extensive pre-training data pyramid and robust model architecture.
Grant Miller explains the paradox of "super agents" in AI systems – their immense power comes with significant risks. He details concerns like privilege abuse and expanded attack surfaces, contrasting them with the benefits of unified organizational intelligence and workflow coordination. The solution proposed is an architecture of "agent swarms," guided by principles of least agency, isolation, observability, and human oversight, to build secure and scalable agentic systems.
This panel discussion explores the evolving AI tech stack and its impact on product-market fit, ethical governance, and business transformation. Experts from Humana, Catenate, In-Telecom, and Spacer AI explain how AI-powered prompts are replacing static dashboards, detail multi-model API strategies for cost efficiency, and delve into the critical role of human-in-the-loop oversight for autonomous agents to ensure data security and responsible AI deployment. They emphasize that true value lies in domain expertise and solving real-world pain points, not just building 'shiny toys'.
Tanmai Gopal, co-founder of PromptQL and creator of Hasura GraphQL, shares insights on building secure and effective 'company brains' for AI agents. He highlights the surprising metric of a continuously rising edit count as a sign of health, dismisses common pitfalls like siloed knowledge, and proposes a human-in-the-loop, unified wiki approach with granular access control and dynamic credential injection to prevent leaks and foster collaborative knowledge creation.
Flora Liu from Notion's GTM engineering team discusses transforming a fragmented Go-to-Market system into a unified, agent-driven platform. She details how Notion tackles challenges like dispersed customer data and unstructured insights by building a four-layered architecture (Know, Decide, Act, Learn) where humans and AI agents operate on the same substrate, leveraging Snowflake, DynamoDB, and Notion itself to create durable, self-improving workflows and boost sales and marketing effectiveness.