Drawing on 1,178 safety and reliability papers from 9,439 generative AI papers (January 2020 - March 2025), we compare research outputs of leading AI companies (Anthropic, Google DeepMind, Meta, Microsoft, and OpenAI) and AI universities (CMU, MIT, NYU, Stanford, UC Berkeley, and University of Washington). We find that corporate AI research increasingly concentrates on pre-deployment areas -- model alignment and testing & evaluation -- while attention to deployment-stage issues such as model bias has waned. Significant research gaps exist in high-risk deployment domains, including healthcare, finance, misinformation, persuasive and addictive features, hallucinations, and copyright. Without improved observability into deployed AI, growing corporate concentration could deepen knowledge deficits. We recommend expanding external researcher access to deployment data and systematic observability of in-market AI behaviors.

Nvidia continues to push boundaries of generative AI in machine learning, self-driving cars, robotics, graphics, simulation and more.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. The hardware giant continues to push the boundaries of technology in machine learning (ML), self-driving cars, robotics, graphics, simulation and more. The three categories of awards at NeurIPS 2022 were these: outstanding main track papers, outstanding datasets and benchmark track papers, and the test of time paper. Nvidia also presented a series of AI advancements it had worked on for the past year. It has released two papers, on providing unique lighting approaches and on 3D model creation, following up on its work in 3D and generative AI. "NeurIPS is a major conference in machine learning, and we see high value in participating in the show among other leaders in the field. We showcased 60 research projects at the conference and were proud to have two papers honored with NeurIPS 2022 Awards for their contributions to machine learning," Sanja Fidler, VP of AI research at Nvidia and a writer on both the 3D MoMa and GET3D papers, told VentureBeat.