BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Tom Goldstein (University of Maryland) DTSTART:20221117T170000Z DTEND:20221117T180000Z DTSTAMP:20260423T003253Z UID:MPML/92 DESCRIPTION:Title: Bu ilding (and breaking) neural networks that think fast and slow\nby Tom Goldstein (University of Maryland) as part of Mathematics\, Physics and M achine Learning (IST\, Lisbon)\n\n\nAbstract\nMost neural networks are bui lt to solve simple patternmatching tasks\, a process that is often known a s “fast” thinking. In this talk\, I’ll use adversarial methods to ex plore the robustness of neural networks. I’ll also discuss whether vulne rabilities of AI systems that have been observed in academic labs can pose real security threats to industrial systems. Then\, I’ll present method s for constructing neural networks that exhibit “slow” thinking abilit ies akin to human logical reasoning. Rather than learning simple pattern m atching rules\, these networks have the ability to synthesize algorithmic reasoning processes and solve difficult discrete search and planning probl ems that cannot be solved by conventional AI systems. Interestingly\, thes e reasoning systems naturally exhibit error correction and robustness prop erties that make them more difficult to break than their fast thinking cou nterparts.\n LOCATION:https://researchseminars.org/talk/MPML/92/ END:VEVENT END:VCALENDAR