Thinking Machines

Part of the Frontiers in Data Science and AI initiative

Thinking Machines investigates what it would take for AI systems to move beyond pattern recognition toward genuine reasoning, exploring the mathematical, structural, and conceptual foundations that enable abstraction, generalization, and understanding. 

It spans research that uses generative models to probe social and psychological processes as well as work that examines the deep interplay between AI and mathematics, from hypothesis generation to the testing and evaluation of scientific claims. 

Across these directions, the series asks how uncertainty, causality, and narrative shape scientific inquiry, and how we can rigorously assess the reliability and reproducibility of knowledge produced with AI.

Upcoming Events

AWARDED PROPOSALS

Leads: Andrew Blumberg, Herbert and Florence Irving Professor of Cancer Data Research, Mathematics, and Computer Science; Kriste Krstovski, Data Science Institute; Ivan Corwin, Mathematics; and Daniel Hsu, Computer Science

Awarded: Fall 2025
Events & Activities: Spring 2026 and Fall 2026

This program examines the deep, two-way relationship between artificial intelligence and mathematics, highlighting how each field informs and strengthens the other. The first phase focuses on the foundational uses of AI in mathematics, beginning with a research-oriented symposium that includes a hands-on training workshop on large language models, followed by a dedicated symposium on pedagogy. The second phase turns to the mathematical foundations of AI, featuring symposia that surface core theoretical challenges and emerging research questions arising from modern AI applications. Together, these sessions create a structured pathway for scholars to engage with both sides of this rapidly evolving intersection, which spans disciplines ranging from engineering and business to the social sciences and the biomedical and physical sciences.

Program Information:

Lead: Yamil Velez, Political Science

Awarded: Fall 2025
Events & Activities: Spring 2026 and Fall 2026

Social scientists are increasingly leveraging Generative AI models to learn about political and psychological processes. In political science, economics, and psychology, large language models are being used to classify content, generate experimental stimuli such as persuasive messages, simulate responses to surveys and incentivized experiments, and devise adaptive surveys that respond to participant input. While excitement about these tools is increasing, there is less guidance on how to rigorously incorporate them into research. This program addresses this gap by bringing together scholars developing both novel applications and methodological approaches needed to advance the field.

Program Information:

Lead: Michael Morris, Management Division, Columbia Business School
Co-Sponsor: AI in Business Initiative, Columbia Business School

Awarded: Spring 2026
Events & Activities: Fall 2026

Professionals increasingly use Large Language Model (“LLM”) chatbots as partners—both in preparation and as copilots during live negotiations. Firms also routinely deploy AI systems to monitor market shifts, digest complex information, support decision-making, and even autonomously negotiate routine agreements. As these tools advance, research is beginning to identify their broader effects on employees, organizations, markets, and legal systems.

This multi-disciplinary conference will convene expertise from a range of fields: computer scientists developing new LLMs and data systems; business leaders developing and adopting AI negotiation tools; behavioral, organizational, and legal researchers; and negotiation educators and trainers. Through interdisciplinary inquiry, the conference will ask: where is AI really changing negotiation, where is it not, and what should we do about it? The conversation will extend beyond bargaining efficiency to questions of trust, organizational coordination, market dynamics, and how responsibility should be shared among humans, machines, and institutions. 

Program Information: