AI has advanced fast by scaling models, data, and compute. As foundation models get more capable, an old question is coming back...
What should machines still learn from human intelligence?
That question sat at the center of ReLearn, the CVPR 2026 workshop "Rediscovering Intelligence: Can AI Still Learn from Humans?" that Lambda co-organized and sponsored, with compute credits for every accepted paper. Researchers from computer vision, cognitive science, and embodied AI came together to ask what human intelligence can still teach machines, and where machines should go past the human blueprint.
The invited speakers were Alexei (Alyosha) Efros of UC Berkeley, Manling Li of Northwestern University, Dima Damen of the University of Bristol, Alan Yuille of Johns Hopkins University, Saining Xie of New York University, and William T. Freeman of Massachusetts Institute of Technology. Their talks didn't point to a single recipe for human-inspired AI. Together, they traced a shift in how researchers think about intelligence: from what machines should learn from humans to how machines should represent and experience the physical world.
Three themes stood out.
1. When should AI learn from humans?
Efros named the tension in his talk, "When Is It Good to Learn from Humans?" His starting point was provocative: today's AI may already learn too much from us.
At a high level, foundation models distill a huge amount of accumulated human knowledge. Efros called them cultural technologies, systems built on knowledge people have already discovered, organized, and written down.
Human influence shows up lower in the stack, too. Text arrives with a symbolic structure people created, which makes tokenization fairly simple. Vision has no natural vocabulary like that. Even self-supervised visual learning leans on human-designed augmentations that quietly decide which changes should preserve meaning.