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21:T491,Practices for Governing Agentic AI Systems

Agentic AI systems—AI systems that can pursue complex goals with limited direct supervision—are likely to be broadly useful if we can integrate them responsibly into our society. While such systems have substantial potential to help people more efficiently and effectively achieve their own goals, they also create risks of harm. In this white paper, we suggest a definition of agentic AI systems and the parties in the agentic AI system life-cycle, and highlight the importance of agreeing on a set of baseline responsibilities and safety best practices for each of these parties. As our primary contribution, we offer an initial set of practices for keeping agents’ operations safe and accountable, which we hope can serve as building blocks in the development of agreed baseline best practices. We enumerate the questions and uncertainties around operationalizing each of these practices that must be addressed before such practices can be codified. We then highlight categories of indirect impacts from the wide-scale adoption of agentic AI systems, which are likely to necessitate additional governance frameworks.22:T401,Point-E: A system for generating 3D point clouds from complex prompts

While recent work on text-conditional 3D object generation has shown promising results, the state-of-the-art methods typically require multiple GPU-hours to produce a single sample. This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes. In this paper, we explore an alternative method for 3D object generation which produces 3D models in only 1-2 minutes on a single GPU. Our method first generates a single synthetic view using a text-to-image diffusion model, and then produces a 3D point cloud using a second diffusion model which conditions on the generated image. While our method still falls short of the state-of-the-art in terms of sample quality, it is one to two orders of magnitude faster to sample from, offering a practical trade-off for some use cases. We release our pre-trained point cloud diffusion models, as well as evaluation code and models, at this https URL.11:["$","$L20",null,{"id":"1zjyzH5AX1DcC2WpPFQbTk","headline":"Our research","headlineSize":"Heading 4","description":"We research generative models and how to align them with human values.","ctaLinks":[{"label":"View research index","page":{"slug":"news/research","category":{"name":"Research","slug":"research","includeInNewsIndex":true,"id":"5wGbg5aYZ4v8Ms970UzsnM","__typename":"category","updatedAt":"2024-05-23T21:47:23.289Z","createdAt":"2024-03-25T14:00:08.172Z"},"title":"Research","pageType":"Index","showTableOfContents":false,"omitFromListings":false,"subnavTitle":"News","searchText":"Research","id":"380AI76cFTZraxb6QoRry9","__typename":"page","updatedAt":"2024-04-19T23:41:52.360Z","createdAt":"2024-03-27T04:24:52.946Z"},"external":false,"style":"Primary 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