In today’s column, I provide a close look at an outstanding AI safety summit that was undertaken by Stanford’s Center for AI Safety on October 5, 2026. This is an annual event and a significant opportunity to bring together academics and practitioners to explore the present and future status of building and deploying safe and trustworthy AI.

Numerous talks by leading experts were coupled with breakout sessions and vital occasions to confer on the latest research by universities and what’s happening in Silicon Valley’s frontier AI labs. One aspect that I always especially relish is that graduate students are given a moment in the spotlight to describe their research efforts. Many of them toil away behind the scenes and rarely get a chance to showcase their innovative work. Kudos for having both student lightning talks and an accompanying poster session that allowed a close-up look and hearty chat with these bright and budding researchers.

Let’s talk about it. This analysis of AI breakthroughs is part of my ongoing Forbes column coverage of the latest in AI, including identifying and explaining key AI complexities (see the link here ).

AI Safety Topnotch Research

The Stanford University Center for AI Safety (SAFE) is globally known for its state-of-the-art work on advancing the science of safe and trustworthy AI systems. Indeed, when I participated in a recent Harvard AI Workshop on the trustworthiness of AI (see my coverage at the link here ), several of the latest publications and research projects of SAFE were brought up during the weighty discussions.

The overarching mission and theme of SAFE is this (per their website):

  • “The Stanford Center for AI Safety leads research, education, and policy to ensure AI systems are safe, trustworthy, and beneficial for humanity.”
  • “We envision a future where AI technologies are developed and deployed responsibly, with robust safety guarantees, transparent decision-making, and alignment with human values.”
  • “Through interdisciplinary collaboration spanning computer science, engineering, law, and social sciences, we aim to shape the policies and practices that govern AI development worldwide.”

It is quite timely to be thinking about AI safety due to the recent spate of incidents and concerns about AI agents that went awry; see my analysis of those headline-grabbing issues and their repercussions at the link here . We need evidence-backed methods and robust techniques that will aid in improving AI safety and stave off the harm that dual-use AI can potentially create. Thankfully, research think tanks and scholarly endeavors by university academic programs are helping to find new ways to make AI safer. For information about Stanford’s SAFE, including their research projects, industry affiliations, publications, events, and other facets, see the link here .

Readers might recall my coverage of last year’s annual SAFE event; see the link here , and my ongoing reporting about SAFE in prior years; see the link here . Once again, the annual summit this year knocked one out of the ballpark.

Opening remarks by Professor Mykel Kochenderfer got the gathering underway and posited vital questions about AI safety that engaged attendees in vibrant dialogue throughout the day. I’ve previously discussed Dr. Kochenderfer’s groundbreaking research, including a detailed exploration of his new book, Algorithms for Validation ; see my review at the link here .

Some highlights of the SAFE Summit 2026 speaker agenda included:

  • Seraphina Goldfarb-Tarrant, Cohere, a talk on “The world outside the beaker: modelling the complexity of sociotechnical systems in LLM safety and security”.
  • Dave Orr, Anthropic, a talk on “Safeguards at Anthropic: model behavior, classifiers, access control, and monitoring.
  • Conrad Stosz, Transluce, a talk on “Improving the science and practice of evaluating AI companions”.
  • Tina Hernandez-Boussard, Associate Dean of Research, School of Medicine, Stanford University, an invited talk on AI safety.
  • Hamed Hassani, University of Pennsylvania, a talk on “Distributed agent attacks and how stateful defenses catch them”.
  • Sydney Katz and Robert Moss, Valgo, a talk on “Continuous monitoring of safety performance”.
  • Houjun (Jack) Liu, Stanford University (student spotlight), a talk on “Adaptive stress testing for safer, more secure, and less deceptive language models”.

I’ll be doing an in-depth analysis of several of those talks in an upcoming series of postings. Stay tuned. Thanks go to Dr. Kiana Jafari, Executive Director of SAFE, for her momentous efforts in putting together the event and making sure that all went superbly (for Dr. Jafari’s research on human-agent teaming and human-centered AI, see the link here ).

Courses Underway And Upcoming

If you are keenly interested in AI safety, it might be worthwhile to consider taking a course on this rapidly evolving topic. Classes that are allied with SAFE at Stanford include these notable offerings (some are underway, some are upcoming):

  • “Governing AI: Law, Policy, and Institutions”, CS283/LAW4052, taught by Nate Persily, Rob Reich, Anka Reuel, and Sanmi Koyejo.
  • “Navigation for Autonomous Systems”, AA275, taught by Grace Gao.
  • “Validation of Safety Critical Systems”, AA228V/CS238V, taught by Sydney Katz.
  • “AI’s Psyche and Psych”, PSYC 248, taught by Nina Vasan

A course that I’ve been devotedly following this quarter is the governing AI class that is noted in the first bullet point above. The syllabus for the class provides a handy list of essential readings in the field of AI governance; see the syllabus at the link here . In addition, Anka Reuel and Rob Reich are providing an insightful glimpse of the week-by-week content and discussions in the course, doing so on their jointly written Substack site at the link here . Readers are aware that I’ve previously met and mentioned Professor Rob Reich in my postings and highly recommend his many articles, books, and writings; see the link here .

AI is here and becoming increasingly ubiquitous. As a dual-use capability, there are tremendous upsides to AI, but equally disconcerting downsides. Researchers are rightfully asking probing questions about what AI safety means, which risks deserve our utmost attention, how safety should be measured, and what the best ways are to suitably govern AI. By rigorously thinking about and performing empirical testing, real-world experimentation, and continuous evaluation, AI safety research is moving us toward a much deeper understanding of the challenges and actionable solutions that will make AI systems safer and more trustworthy.

For whatever level of trust we put toward AI, a markedly famous maxim must still prevail: trust, but verify.