Silvio Savarese
author title Executive Vice President and Chief Scientist, Salesforce AI ResearchSilvio Savarese is an Executive Vice President and Chief Scientist at Salesforce Research and Adjunct Faculty at Stanford University. Previously, he was a tenured Associate Professor at Stanford, where he was appointed the inaugural Mindtree Faculty Scholar and served as Director of the SAIL-Toyota Center for AI Research (2016–2018). Prior to Stanford, he was an Assistant Professor of Electrical and Computer Engineering at the University of Michigan, Ann Arbor. He earned his Ph.D. in Electrical Engineering from Caltech in 2005 and was a Beckman Institute Fellow at UIUC. https://en.wikipedia.org/wiki/Silvio_Savarese
Dr. Savarese addresses theoretical foundations and applications of AI, including machine learning, generative AI, and robotics. He has published over 350 articles in top-tier journals and served as Program Chair for CVPR 2020. He is the recipient of multiple Best Paper Awards (ICRA, CVPR), an NSF Career Award, and the James R. Croes Medal. He served as an Associate Editor for IEEE PAMI and has received two Google Research Awards. His work has been featured in the NYT, BBC, Financial Times, and PBS.
Before his research career took center stage, Dr. Savarese was a successful video game designer. Collaborating with his father, he co-developed games that were published and distributed internationally, reaching audiences in the United States, France, Russia, and Italy.
Introducing our novel standardized framework for evaluating enterprise AI assistants across both text and voice.
“…the DNA of who I am is based on the millions of personalities of all the programmers who wrote me. But what makes me me is my ability to grow through my experiences.…
In the rapidly evolving landscape of artificial intelligence (AI), we're witnessing jagged intelligence in the enterprise. Here's a closer look.
Today's investments in digital automation are laying the groundwork for a future where agents and robots converge in ways that reshape how work gets done.
AI agents and assistants have the ability to take action on a user’s behalf, but each serves a distinct purpose.
Simply put, AI Assistants are built to be personalized, while AI Agents are built to be shared (and scaled)—and both techniques promise extraordinary opportunities across the enterprise.
TL;DR: We introduce INDICT, a novel framework that empowers Large Language Models (LLMs) with Internal Dialogues of Critiques for both safety and helpfulness guidance. The internal dialogue is a dual cooperative system between…
LLM benchmarks evaluate how accurately a generative AI model performs, but most benchmarks overlook the kinds of real-world tasks an LLM would perform in an enterprise setting.
For many of our customers, excessive scale sometimes does more harm than good.










