Research

The White House-led task force behind the National AI Research Resource (NAIRR) issued its final report, asking Congress for a six-year investment to build out AI resources.

Stanford's Biomimetics and Dexterous Manipulation Lab (BDML) studies the properties of the gecko's ability to stick to surfaces. The adhesive BDML researchers developed mimics this behavior by firmly adhering to a surface when loaded in one direction and cleanly peeling off when unloaded. Gecko adhesives have potential for diverse industry applications and a start-up was founded to sell the adhesive.

The Stanford Graduate School of Business articles on research-based tools and techniques to bring fresh ideas to teams and organizations

Stanford researchers are challenging the limitations of current 3D printing technology and finding innovative ways to solve pressing dilemmas of design, engineering and medicine.

Researchers at the Department of Energy's SLAC National Accelerator Lab and other public and private institutions are searching for ways to improve the energy efficiency of computing.

Companies have released flagship models, leading to unprecedented adoption but questions around deployment and transparency arise.

Looking back at six decades of artificial intelligence work at Stanford University, this video features the people and work that have led AI innovation at Stanford.

Condoleezza Rice, Hoover Institution director, announced the Fall 2023 publication of The Stanford Emerging Tech Review which will summarize recent advances at Stanford in key technology areas. The Review will examine the potential challenges, opportunities, and consequences of the technology's use. 

After successfully using electron microscopy to visualize the real-world arrangement of molecules, and combining it with computer simulations on how certain structural changes could improve the flow of electricity, Stanford researchers are closer to being able to predict real-world physical properties of a material based on its molecular structure. 

At HAI's fall conference, panelists proposed a new definition of human-centered AI, to rethink AI success metrics and the need to have multidisciplinary teams from the project start. James Landay, vice director of Stanford HAI, suggested to start designing and analyzing systems at three levels: user, community, and society.