The exciting and impressive use of new, powerful foundation models could cause harm to individuals and society but HAI and CRFM are pushing for the creation of an expert review board to set community norms, share best practices and agree to standardized access rules before foundation models are released. We are, as HAI Associate Director Rob Reich puts it, in a race between “disruption and democracy.”

Erik Brynjolfsson and a team of researchers from Stanford, MIT, Harvard, and LinkedIn recently conducted the largest experimental study to date on the impact of digital job sites on the labor market and found that weaker social connections have a greater beneficial effect on job mobility than stronger ties.

HAI Co-Director Fei-Fei Li is highlighted in this article for her significant contributions to AI including the creation of HAI and how it is furthering understanding of the field.

The rapid advancement of foundation models, as well as the ethical concerns around them, is why one year ago, Stanford HAI founded the Center for Research on Foundation Models (CRFM). Despite progress, there is much work left to do.

HAI Privacy and Data Policy Fellow Jennifer King provides insight into the data used to train large language models, and the risks these foundation models pose to everyone’s privacy.

It’s time for a shared, value-neutral way of talking about AI’s risks and opportunities, with policymaker education like HAI’s recent AI Boot Camp being a good start.

HAI’s “Advancing Technology for a Sustainable Planet" conference highlighted the concerns about the environmental impact of AI along with proposed solutions for how regulation might help.

Percy Liang, Director of HAI’s Center for Research on Foundation Models, says what’s needed are broader community standards to guide research and decisions.

“Whether it’s an AI tool or whether it’s a person making that suggestion, there’s an art to that data communication,” says HAI Faculty Affiliate Danton Char.

CRFM Director Percy Liang explains the urgent need to develop community norms that guarantee research access and help guide the future of AI responsibly.

The work of HAI Co-Director Fei-Fei Li is highlighted in this article on how embodied AI is pushing the field of AI forward.

The efficacy of legislation aimed at curbing harmful practices remains to be seen, explains HAI Privacy and Data Policy Fellow Jennifer King.

Erik Brynjolfsson, Senior Fellow at HAI and Director of the Stanford Digital Economy Lab, says that “having an AI pretend to be sentient is going to happen way before an AI is actually sentient.”

Following HAI's 2021 launch of the Center for Research on Foundation Models and the term "foundation models" itself, this Economist special series on foundation models explores the opportunities and risks, and what it means for the future of AI.

Erik Brynjolfsson, director of the Stanford Digital Economy Lab within HAI, remains optimistic that the tidal wave of transformation from AI will increase productivity.

Percy Liang, director of Stanford HAI’s Center for Research on Foundation Models, comments on the power and risks of large-scale AI models such as Google’s LaMDA.

The international policy director at Stanford’s Cyber Policy Center discusses the EU’s latest rule for the internet.

HAI seed grant recipients Sadasivan Shankar and Richard N. Zare discuss how machine learning is being used to find new pharmaceutical compounds, holding great promise for the future, but also great peril.