AI engineering skills are helping researchers, developers, and many others -- not just those with an 'AI Engineering' job title -- get more done. In this presentation, I will share the results of our study mapping out the four most important AI engineering skills: (1)Building and deploying applications, (2)Software engineering foundations, (3)Using coding agents, and (4)Shaping the build. I will also discuss how these skills are changing not just software development, but how we conduct research.

Speaker Bio: Andrew Ng is the Founder of DeepLearning.AI, Managing General Partner at AI Fund, Managing Partner at AI Aspire, Executive Chairman of LandingAI, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer in machine learning and online education, Dr. Ng has changed countless lives through his work in AI. Over 8 million people have taken an AI class from him. He was the founding lead of the Google Brain team, which helped Google transform into a modern AI company. He served as VP & Chief Scientist at Baidu, where he led a 1,300-person AI team responsible for the company's AI technology and strategy. He was formerly Director of the Stanford AI Lab, home to 20+ faculty members and research groups. In 2023, he was named to the Time100 AI list of the most influential AI persons in the world. He has authored/co-authored over 200 papers in AI and related fields and holds a B.Sc. from CMU, M.Sc. from MIT, and Ph.D. from UC Berkeley. Dr. Ng now focuses his time primarily on providing AI training and on his entrepreneurial ventures, looking for the best ways to accelerate responsible AI adoption globally.

Talk abstract to be added

Speaker Bio: Azalia Mirhoseini is a co-founder of Ricursive Intelligence, a frontier lab dedicated to recursive self-improvement through AI that designs the chips that fuel it. She is also an Assistant Professor of Computer Science at Stanford University where she directs Scaling Intelligence, a lab focused on developing scalable and self-improving AI systems and methodologies toward the goal of artificial general intelligence. Previously, she spent several years in industry AI labs, including Google Brain, Anthropic, and Google DeepMind, working on the development of Claude and Gemini. Her past work includes Mixture-of-Experts (MoE) neural architectures, now predominantly used in leading generative AI models; AlphaChip, a pioneering work on deep reinforcement learning for layout optimization used in the design of advanced chips like Google AI accelerators (TPUs) and data center CPUs; as well as pioneering research on LLM Test-Time Scaling. Her work has been recognized through the Okawa Research Grant, the Google ML and Systems Junior Faculty Award, MIT Technology Review's 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and coverage by various media outlets, including WSJ, NYT, Forbes, MIT Technology Review, IEEE Spectrum, WIRED, and TechCrunch.

Software development iterates between implementation and verification until an evaluator accepts the result. Yet even a formal proof establishes correctness only relative to stated requirements and a modeled environment, not stakeholder intent or the real world. We introduce the two-gap framework to characterize these mismatches: the requirement gap and the model gap. Reward hacking exploits omissions in either; hallucination widens them by fabricating requirements or assumptions. Because open, changing systems cannot generally close these gaps, we propose an assurance-revision loop that uses deployment evidence to revise requirements, models, and evaluators. We frame assured agentic development as allocating scarce human judgment, agent capability, and compute: judgment narrows the requirement gap, while faithful evaluation narrows the model gap. Reality remains the final verifier.

Speaker Bio: Ion Stoica is a Professor in the EECS Department at the University of California at Berkeley where he holds the Xu Bao Chancellor's Chair and is the Director of SkyLab. He is currently doing research on cloud computing and AI systems. where he holds the Xu Bao Chancellor's Chair and is the Director of SkyLab. He is currently doing research on cloud computing and AI systems. Past work includes Ray, Apache Spark, Apache Mesos, Tachyon, Chord DHT, and Dynamic Packet State (DPS). He is an Honorary Member of the Romanian Academy, an ACM Fellow and has received numerous awards, including the Mark Weiser Award (2019), SIGOPS Hall of Fame Award (2015), and several Test of Time awards. He also co-founded three companies, Anyscale (2019), Databricks (2013) and Conviva (2006)

Moderator: Niloufar Salehi. Panelists: Jure Leskovec (Professor at Stanford University); Siddharth Mishra-Sharma (MTS at Anthropic); Zhenghao Chen (VP of AI/ML at Insitro)

Speaker Bio: Niloufar (Nilou) Salehi is an Associate Professor in the School of Information at UC, Berkeley. She studies human-centered AI including reasoning, evaluations environments, and reliability. Her work has been published and received awards in premier venues including ACM CHI, CSCW, and EMNLP and has been covered in Venture Beat, Wired, and the Guardian. She is a W. T. Grant Foundation scholar and a member of the advisory board on generative AI at NVIDIA. She received her PhD in computer science from Stanford University in 2018.