The CEO of OpenAI, the company responsible for creating artificial intelligence chatbot ChatGPT and image generator Dall-E 2, said "regulation of AI is essential" on Tuesday as he testified in front of a Senate judiciary committee panel. In his first appearance in front of Congress, Sam Altman said he supported regulatory guardrails for the technology that would enable the benefits of artificial intelligence while minimizing the harms. "We think that regulatory intervention by governments will be critical to mitigate the risks of increasingly powerful models," Altman said in his prepared remarks. "For example, the US government might consider licensing and testing requirements for development and release of AI models above a threshold of capabilities." Altman and Gary Marcus, emeritus professor of psychology and neural science at New York University, both called for a new regulatory agency for the technology.

OpenAI CEO Sam Altman discussed the risks and benefits of AI at a Senate Judiciary subcommittee hearing on May 16, 2023. OpenAI CEO Sam Altman told a panel of senators Tuesday that his greatest fear as his company develops artificial intelligence capabilities is that is causes major harmful disruption for people, and acknowledged that AI has this potential downside if it isn't properly regulated. "My worst fears are that we cause significant – we, the field, the technology industry – cause significant harm to the world," Altman told a Senate Judiciary subcommittee. "I think that could happen in a lot of different ways. It's why we started the company."

The Biden administration is increasingly calling AI an important priority, and there are growing efforts on Capitol Hill to draft legislation addressing the technology. Senate Majority Leader Charles E. Schumer (D-N.Y.) has been developing a new AI framework, which would "deliver transparent, responsible AI while not stifling critical and cutting edge innovation."

Now, Altman will answer questions from a Senate Judiciary subcommittee, acting as a quasi-industry spokesman for companies racing ahead to deploy chatbots and other tools to the public, including Google and Microsoft. Topics are likely to include the risks of AI, competition in the industry and how government should handle it -- and maybe even whether robots are going to kill us all.

Increasingly, we're surrounded by fake people. Sometimes we know it and sometimes we don't. They offer us customer service on Web sites, target us in video games, and fill our social-media feeds; they trade stocks and, with the help of systems such as OpenAI's ChatGPT, can write essays, articles, and e-mails. By no means are these A.I. systems up to all the tasks expected of a full-fledged person. But they excel in certain domains, and they're branching out. Many researchers involved in A.I. believe that today's fake people are just the beginning.

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"Blurb writing is a mini art form," Iris Murdoch once wrote in a letter to former Penguin blurb writer Elizabeth Buchan. And like many other art forms, companies have been experimenting with the idea that it could be created without an artist. A German company that provides digital book distribution and marketing services to publishers has announced it will integrate ChatGPT, a chatbot that answers questions by drawing on publicly available internet data, into its software. "During the beta phase, publishers can test the benefits of the artificial intelligence tool for their digital book marketing," states Bookwire, adding that it will only use ChatGPT if a publisher agrees and the disclaimer that the company "does not assume any responsibility for the content created by ChatGPT". But there is also another question that needs to be asked.

Two companies are coming together to develop humanoid robots with AI that will be able to perform jobs from manufacturing to health care professions. Senators on Tuesday will grill OpenAI CEO Sam Altman about the "perils and promise" of artificial intelligence as part of a push to better understand this quickly emerging technology and impose some kind of regulatory regime around it. Altman will testify before the Senate Judiciary Subcommittee on Privacy, Technology, and the Law, which will mark his first time as a witness at a public congressional hearing. His testimony comes several weeks after Senate Majority Leader Chuck Schumer, D-N.Y., said he is working on a regulatory blueprint and as several members of the House and Senate have talked about the need for rules of the road for AI. Members of the subcommittee have made it clear over the last week that they want to learn more about AI to make sure it's used safely and responsibly.

In-context learning is a new learning paradigm where a language model conditions on a few input-output pairs (demonstrations) and a test input, and directly outputs the prediction. It has been shown highly dependent on the provided demonstrations and thus promotes the research of demonstration retrieval: given a test input, relevant examples are retrieved from the training set to serve as informative demonstrations for in-context learning. While previous works focus on training task-specific retrievers for several tasks separately, these methods are often hard to transfer and scale on various tasks, and separately trained retrievers incur a lot of parameter storage and deployment cost. In this paper, we propose Unified Demonstration Retriever (\textbf{UDR}), a single model to retrieve demonstrations for a wide range of tasks. To train UDR, we cast various tasks' training signals into a unified list-wise ranking formulation by language model's feedback. Then we propose a multi-task list-wise ranking training framework, with an iterative mining strategy to find high-quality candidates, which can help UDR fully incorporate various tasks' signals. Experiments on 30+ tasks across 13 task families and multiple data domains show that UDR significantly outperforms baselines. Further analyses show the effectiveness of each proposed component and UDR's strong ability in various scenarios including different LMs (1.3B - 175B), unseen datasets, varying demonstration quantities, etc.

We are currently unable to specify human goals and societal values in a way that reliably directs AI behavior. Law-making and legal interpretation form a computational engine that converts opaque human values into legible directives. "Law Informs Code" is the research agenda embedding legal knowledge and reasoning in AI. Similar to how parties to a legal contract cannot foresee every potential contingency of their future relationship, and legislators cannot predict all the circumstances under which their proposed bills will be applied, we cannot ex ante specify rules that provably direct good AI behavior. Legal theory and practice have developed arrays of tools to address these specification problems. For instance, legal standards allow humans to develop shared understandings and adapt them to novel situations. In contrast to more prosaic uses of the law (e.g., as a deterrent of bad behavior through the threat of sanction), leveraged as an expression of how humans communicate their goals, and what society values, Law Informs Code. We describe how data generated by legal processes (methods of law-making, statutory interpretation, contract drafting, applications of legal standards, legal reasoning, etc.) can facilitate the robust specification of inherently vague human goals. This increases human-AI alignment and the local usefulness of AI. Toward society-AI alignment, we present a framework for understanding law as the applied philosophy of multi-agent alignment. Although law is partly a reflection of historically contingent political power - and thus not a perfect aggregation of citizen preferences - if properly parsed, its distillation offers the most legitimate computational comprehension of societal values available. If law eventually informs powerful AI, engaging in the deliberative political process to improve law takes on even more meaning.