Ai Education Summit at Standford

This HAI seminar featured Common Crawl Foundation’s work on preserving humanity's knowledge and making it accessible through its free public web dataset, a vital resource since 2008. The Common Crawl team presented insights from a new data product that utilizes Common Crawl's metadata to explore concerns around robots.txt exclusions, legal demands, and "bot defenses," advocating for greater transparency and informed solutions for the future of public web data. This seminar was recorded on October 22, 2025 at Stanford University. 00:00:00 Introduction 00:01:01 Lecture 00:48:53 Q&A

In this HAI seminar, Brad Myers, director of the Human-Computer Interaction Institute at Carnegie Mellon University, discussed the design and importance of interaction techniques — the fundamental building blocks of user interfaces. He explored their history, challenges, and future, highlighting examples from buttons and touch gestures to voice assistants and virtual reality. The talk drew from his university courses and his new book on the topic, titled "Pick, Click, Flick! The Story of Interaction Techniques." This seminar was recorded on October 21, 2025 at Stanford University. 00:00:00 Introduction 00:09:01 Lecture 01:13:32 Q&A

Police body-worn cameras have been at the center of police reform efforts over the past decade. Yet the vast majority of the footage generated by those cameras is never examined, undermining the camera’s utility as a tool for accountability and improving interactions between the police and community members. In this talk, researchers explain how they are harnessing artificial intelligence (AI) and large language models (LLMs) to unlock the research potential of body-worn camera footage to better understand the nature of law enforcement’s encounters with the public. In turn, leveraging the resulting insights could fuel both the development and systematic evaluation of officer trainings and other institutional interventions designed to improve policing. Learn more about other research supported by the Hoffman-Yee Research Grant program here: https://hai.stanford.edu/research/g... 00:00:00 Introduction 00:00:37 Lecture 00:32:55 Q&A

The promise of modern generative AI tools is that they will assist users in creating production-quality visual content from a simple text prompt describing what the user wants. But current black-box AI are difficult to work with; the AI often misinterprets the intent of the user and users lack a predictive conceptual model for what the AI will produce given an input prompt. This mutual lack of a theory of mind leads to a collaboration by trial-and-error, where the user repeatedly tries different prompts hoping to find one that will produce the desired output. In this talk, researchers discuss how they take major steps towards allowing both entities (humans and AI) to develop a shared conceptual grounding that allows each to simulate how the other might operate given an input task. Learn more about other research supported by the Hoffman-Yee Research Grant program here: https://hai.stanford.edu/research/g... 00:00:00 Introduction 00:00:30 Lecture 00:35:49 Q&A

DNA encodes the fundamental language for all living organisms. Recently, large language models have been used to learn this mysterious biological language to unlock a better understanding of this blueprint of life. Yet, learning from DNA has its distinct challenges over natural language - it’s extremely long, with the human genome over 3 billion nucleotides in length. It’s also highly sensitive to small changes, where a single point mutation can mean the difference between having a disease or not. Overcoming these technical challenges of modeling long sequences in DNA can lead to a deeper understanding of human disease, the creation of novel therapeutics, and the possibility to engineer life itself. This talk describes a research project aimed at developing a new line of long sequence language models that can reproduce the organization of DNA sequences from the molecular to the whole genome scale. The researchers seek to lead the ethical development of DNA sequence modeling and design, and to bring the innovation of AI systems for the betterment of human health. Learn more about other research supported by the Hoffman-Yee Research Grant program here: https://hai.stanford.edu/research/g... 00:00:00 Introduction 00:00:29 Lecture 00:30:26 Q&A

Massive datasets are the cornerstone for developing large language models (LLMs) and other generative AI. However, these datasets have also sparked debates regarding generative AI, highlighted by several copyright disputes involving OpenAI. This talk explores critical aspects of data creation and attribution for generative AI. Throughout the project, the researchers aim to ground their research with real-world legal and policy considerations and high-impact applications in law and medicine. Learn more about other research supported by the Hoffman-Yee Research Grant program here: https://hai.stanford.edu/research/g... 00:00:00 Lecture 00:29:06 Q&A

Imagine if we could build digital twins of your cells and simulate how they would respond to a drug treatment before exposing you to the drug while considering your sex, age, and comorbidities. The key to such a medical future requires developing end-to-end frameworks for cell modeling. Recent scientific and technological advances present a historic opportunity to make unprecedented progress in our fight against human disease. Specifically, new sources of biomedical data are rapidly becoming available, and new AI techniques make it possible to understand these massive datasets. In this talk, researchers discuss how they seek to (1) create a multimodal foundation model for cells capable of capturing function and state across human tissues and individuals, (2) develop an intuitive chat model interface to augment the ability of biologists to use and understand it, and (3) showcase its capacities by modeling cells affected by the menstrual cycle to answer critical questions in women’s health, with an initial focus on cardiovascular disease management. Learn more about other research supported by the Hoffman-Yee Research Grant program here: https://hai.stanford.edu/research/g... 00:00:00 Lecture 00:34:20 Q&A