A user who is frustrated with changes a social media company made to their favorite messaging platform, like the introduction of an annoying AI chatbot, can’t do much to address the situation, short of leaving the platform.
As a theoretical cryptographer, Kalai deals with questions like: how do you prove you know something without revealing what it is? What does it mean to know something at all? How can we verify AI answers are good, not just correct? And, in a world increasingly run by systems we don't fully understand, what do we owe each other in the name of safety?
American Family Insurance, CSAIL Alliances Affiliate, exists to protect what matters most to households and businesses across the country, using data science and applied AI.
Add to calendarAmerica/New_YorkAI, Data Science & Careers from American Family Insurance10/15/2026
American Family Insurance, CSAIL Alliances Affiliate, exists to protect what matters most to households and businesses across the country, using data science and applied AI.
This talk introduces MIT graduate students to insurance as a serious technology, data, and AI problem, not just a financial product. Insurance AI rarely means one model: many specialized systems — for risk, fraud, compliance, and claims — each with its own evaluation, quality gates, and human review. The speaker leads several applied AI initiatives at American Family and has worked hands-on across this ecosystem. The talk draws on that complexity, and on pressures like catastrophe risk, climate, cybersecurity, and responsible AI deployment, to explain why insurance sits at the center of some of today's hardest technology problems. It highlights AmFam's academic research partnerships — on severe weather and wildfire risk — as one model for how graduate research translates into real-world impact, and closes with internship and career opportunities for graduate students.
Tesla, CSAIL Alliances members, develops and deploys autonomy at scale in vehicles, robots and more.
Tesla believes that an approach based on advanced AI for vision and planning, supported by efficient use of inference hardware, is the only way to achieve a general solution for full self-driving, bi-pedal robotics and beyond.
Add to calendarAmerica/New_YorkTraining Robust Optimus Policies: Tesla AI Tech Talk10/14/2026
Tesla, CSAIL Alliances members, develops and deploys autonomy at scale in vehicles, robots and more.
Tesla believes that an approach based on advanced AI for vision and planning, supported by efficient use of inference hardware, is the only way to achieve a general solution for full self-driving, bi-pedal robotics and beyond.
Tesla Full Self Driving (FSD) drives millions of miles autonomously every day across hundreds of cities. This feat is only possible through training extremely robust policies. When the goal is to similarly have millions of robots doing thousands of useful tasks in a wide variety of environments, we need to ensure that Optimus has similarly robust policies. In this talk, we share some of our strategies in training such robust policies and showcase some of our latest world models that make this possible.
Modern processors are fast in part because they guess. Rather than waiting to find out which way a program will branch, a chip predicts the likely path and races ahead. When the guess is right, time is saved. When it's wrong, the work is discarded, but traces of it linger, and since the Spectre vulnerability was disclosed in 2018, attackers have known how to read those traces to pull secrets out of memory they should never see.
Agentic workflows are artificial intelligence-powered software systems that chain together multiple models and external tools to tackle complicated tasks, like analyzing a video and answering questions about it.
When security researchers want to understand what a modern processor is really doing with the kind of detail that determines whether attacks like Spectre and Meltdown are possible, they usually run their experiments on top of an operating system that was never built for the job. They open up macOS or Linux, patch the kernel by hand, and hope the modifications hold. The approach is unstable, hard to reproduce, and on Apple's platforms, slated for deprecation.
Researchers from MIT and elsewhere have developed a more user-friendly and efficient method to help networking engineers identify potential system failures before they cause major problems, like a cloud service outage that leaves millions of users unable to access applications.
A new method developed by MIT researchers can accelerate a privacy-preserving artificial intelligence training method by about 81 percent. This advance could enable a wider array of resource-constrained edge devices, like sensors and smartwatches, to deploy more accurate AI models while keeping user data secure.
