Ameet Talwalkar (CMU and Datadog)

June 17, 2025
Soda 510

Title and Abstract

Why AI Needs Specialization

While modern AI holds great promise, the gap between its hype and practical impact remains substantial. This talk advocates for the importance of specialization to help bridge that gap—urging researchers to tailor problem formulations, modeling approaches, data collection, and evaluation methods to concrete downstream tasks. We begin by briefly examining the limitations of existing domain-specific foundation models–for genomics, satellite imaging, and time series–that apply techniques from core AI domains such as vision and NLP with minimal specialization. We then present recent work from CMU and Datadog AI Research that advances specialized approaches on two distinct tasks: autonomously executing complex web tasks and proactively detecting or predicting disruptions in production software systems. These efforts highlight the critical role of domain-aware design in moving beyond shiny demos and toward meaningful AI impact.

Bio

Ameet Talwalkar is an associate professor in the Machine Learning Department at CMU and Chief Scientist at Datadog. His current research interests include AI for science, human-AI interaction, and developing specialized foundation models and agents. He co-founded Determined AI (acquired by HPE), helped create MLlib in Apache Spark, co-authored the textbook 'Foundations of Machine Learning,’ and spearheaded the creation of the MLSys conference