[Music] Hi everyone. Thanks for that welcome. Uh, as you just heard, my name is James Low.I'm head of AI engineering at the Incubator for AI. We're a small team of experts uh, in the UK government. We were created by 10 Downing Street to deliver public good using AI and we do that via experimentation and product building.The UK government delivers uh for its citizens. It spends over a trillion pounds delivering for its over 70 million citizens. So there's a lot to play for.At the incubator for AI, uh we del ...

Market Trends & Industry Dynamics - McKinsey 报告指出，全球 30 个最发达经济体中，职位数量超过了能够胜任的人数，过去十年中，职位增长率超过人口增长率 420% [2][3] - 物理 AI 对于解决休闲、酒店、医疗保健、建筑、交通运输、制造业等行业的问题至关重要，这些行业不能仅靠像 ChatGPT 这样的聊天机器人来解决 [3][4] - 英伟达 Project Groot 是将人形机器人和其他形式的机器人技术引入世界的战略，涵盖了计算基础设施、软件和所需的研究 [11] Robotics Foundation Model & Technology - 英伟达的 GR 101 机器人基础模型是开源且高度可定制的，其一大特点是跨具身性，该模型包含 20 亿参数 [1][12] - 机器人数据策略包括：少量且昂贵的真实世界数据（机器人执行真实任务），大量非结构化的互联网视频数据（人类解决任务），以及理论上无限的合成数据 [14][16][17][18] - Project Groot 的数据解决方案包括数据金字塔策略，强调通过模拟和世界基础模型来增强和倍增高质量数据 [13][18][19] - Groot N1 系统引入了双系统架构，系统一快速执行任务（120 赫兹），系统二缓慢规划复杂任务，灵感来源于 Daniel Kahneman 的《思考快与慢》 [23][24][25] - Groot N1 采用扩散 Transformer 块，结合视觉编码器、VLM（视觉语言模型）和文本分词器处理图像和文本输入，并通过动作解码器生成可用于特定机器人的动作向量 [27][28][29][30] - 机器人学习的两种主要方式是模仿学习（通过复制人类专家）和强化学习（通过试错最大化奖励），Groot N1 结合使用了这两种方法 [32][33][36] Deployment & Compute Infrastructure - 物理 AI 生命周期包括生成数据、使用数据和部署，英伟达称之为“三大计算机问题”，涉及不同计算特征：模拟阶段（OVX Omniverse），训练阶段（DGX），边缘部署阶段（AGX） [9][10]

[Music] My name is Stefania. I'm so glad you made it until the yeah uh last day of the conference and came to the robotics track. So, we're going to start with a live demo uh and then we'll switch to the presentation just like to to kind of like swap things around.So, I'm going to try to connect the microscope over here. Uh and let's see the other camera and some sensors. So, my talk is about real time uh science co-scientist.So, think about pair programmers. How many of you use any form of copilot for codi ...

[Music] Uh my name is Prii and I am part of the engineering team at Temporal. How many people here have heard of Temporal. Perfect.Great. So Temporal is the company that takes reliability incredibly seriously. so seriously that our mascot is a tardigrade. Does anybody know what a tardigrade is.Yes, some folks. Well, it is what is also called a water bear and is the most resilient animal known to humankind. And so that's how seriously we take reliability.Definitely stop by our booth for some stickers and som ...

https://www.linkedin.com/in/markmyshatyn/ ...

Coding agents are transforming how software gets built, tested, and deployed, but engineering teams face a critical challenge: how to embrace this automation wave without sacrificing trust, control, or reliability. In this 80 minute workshop, you’ll go beyond toy demos and build production-minded AI agents using Dagger, the programmable delivery engine designed for real CI/CD and AI-native workflows. Whether you're debugging failures, triaging pull requests, generating tests, or shipping features, you'll le ...

A no fluff, all tactics discussion. More AI engineers should build startups, the world needs more software. But there’s a way to raise VC and it’s hard to do it if you’ve never seen it done. We are going to walk through the exact playbook to raise your first round of funding. We will show you real pitch decks, real cold emails and real term sheets so when you go out to raise your first round of funding, you are setup to do it. Every AI Engineer should be equip to start their own company and this session mak ...

Accuracy scores and leaderboard metrics look impressive—but production-grade AI requires evals that reflect real-world performance, reliability, and user happiness. Traditional benchmarks rarely help you understand how your LLM will perform when embedded in complex workflows or agentic systems. How can you realistically and adequately measure reasoning quality, agent consistency, MCP integration, and user-focused outcomes? In this practical, example-driven talk, we'll go beyond standard benchmarks and dive ...

The goal of mechanistic interpretability is to reverse engineer neural networks. Having direct, programmable access to the internal neurons of models unlocks new ways for developers and users to interact with AI — from more precise steering to guardrails to novel user interfaces. While interpretability has long been an interesting research topic, it is now finding real-world use cases, making it an important tool for AI engineers. About Mark Bissell Mark Bissell is an applied researcher at Goodfire AI worki ...

SGLang Overview - SGLang is an open-source, high-performance serving framework for large language models (LLMs) and large vision models (VLMs) [5] - SGLang supports day zero releases for new models from labs like Quen and DeepSeek, and has a strong open-source community [7] - The project has grown rapidly, from a research paper in December 2023 to nearly 15,000 GitHub stars in 18 months [9] Usage and Adoption - Base 10 uses SGLang as part of its inference stack for various models [8] - SGLang is also used by XAI for their Glock models, inference providers, cloud providers, research labs, universities, and product companies like Koser [8] Performance Optimization - SGLang's performance can be optimized using flags and configuration options, such as CUDA graph settings [20] - Eagle 3, a speculative decoding algorithm, can be used to improve performance by increasing the token acceptance rate [28][42][43] - The default CUDA graph max batch size on L4 GPUs is eight, but it can be adjusted to improve performance [31][36] Community and Contribution - The SGLang community is active and welcomes contributions [7][54] - Developers can get involved by starring the project on GitHub, filing issues, joining the Slack channel, and contributing to the codebase [9][54][55] - The codebase includes the SGLang runtime, a domain-specific front-end language, and a set of optimized kernels [58]