AI: Foundations for Academia (and Startups) 
 
A New Seminar Series (MIBR, MIT) 
  
“Is it still worthwhile to be a researcher in AI?” 
  
The landscape of AI research is shifting. Many problems that once defined academia are now dominated by scale, data, and incumbent advantage. Yet the most important questions remain open. What are the fundamental principles of intelligence, and how can they guide systems that learn efficiently, generalize robustly, and create real societal and economic value? 
 
This seminar argues that academia remains the place to identify and test principles of intelligence. Startups remain the place to turn those principles into systems that matter.

We convene the MIT-area community to identify foundational principles that are shared by biological and artificial intelligence, and translate them into deployable, venture-scale technologies. 
 
We will connect empirical evidence from cognitive development, systems neuroscience, and modern AI systems with formal structure in learning and reasoning, including sample efficiency, sparse compositionality, invariances, memory, optimization dynamics, and generalization.

By the end of the series, we will have identified and tested key conjectures about intelligence and built new collaborations between labs and startups. 
 
Who should attend: Theorists and experimentalists in ML/AI, neuroscience, cognitive science, and applied math; founders, investors, and industry researchers exploring principle-driven AI products. Graduate students and postdocs are strongly encouraged to participate. 
 
Format & cadence: Roughly biweekly, hybrid (MIT campus + Zoom). Each session features a 35–45 minute talk, a 15–20 minute cross-disciplinary discussion. 
 
Join us: If you’re interested in the foundational principles of AI or how to use them to solve important problems, this seminar is for you. To propose a talk, volunteer as a discussant, or suggest a startup case study, please get in touch withtp@ai.mit.edu, pierb@mit.edu or jbuchet@mit.edu.