Berkeley experts in artificial intelligence are studying how things like ChatGPT will transform everything from admissions screening and research to writing college essays. It passed the bar exam, first with a mediocre score and then with a ranking among the top tier of newly minted lawyers. It scored better than 90% of SAT takers. It nearly aced the verbal section of the GRE -- though it has room for improvement with AP Composition. In the months since the machine-learning interface ChatGPT debuted, hundreds of headlines and hot-takes have whirled about how artificial intelligence will overhaul everything from health care and business to legal affairs and shopping.

This article explores the ethical problems arising from the use of ChatGPT as a kind of generative AI and suggests responses based on the Human-Centered Artificial Intelligence (HCAI) framework. The HCAI framework is appropriate because it understands technology above all as a tool to empower, augment, and enhance human agency while referring to human wellbeing as a grand challenge, thus perfectly aligning itself with ethics, the science of human flourishing. Further, HCAI provides objectives, principles, procedures, and structures for reliable, safe, and trustworthy AI which we apply to our ChatGPT assessments. The main danger ChatGPT presents is the propensity to be used as a weapon of mass deception (WMD) and an enabler of criminal activities involving deceit. We review technical specifications to better comprehend its potentials and limitations. We then suggest both technical (watermarking, styleme, detectors, and fact-checkers) and non-technical measures (terms of use, transparency, educator considerations, HITL) to mitigate ChatGPT misuse or abuse and recommend best uses (creative writing, non-creative writing, teaching and learning). We conclude with considerations regarding the role of humans in ensuring the proper use of ChatGPT for individual and social wellbeing.

Days of flurrying speculation gave way on Tuesday to the official reveal of the 34 felony charges against former US President and 2024 Republican hopeful Donald Trump. In an arraignment in Manhattan's criminal court that was watched the world over, in an unsealed indictment and statement of facts, and in a news conference by New York District Attorney Alvin Bragg, the clearest picture yet of the case against Trump has emerged. But the new details also raised several questions, leaving some legal experts divided on the strength of the case based on what is currently known. Speaking to Al Jazeera, former federal prosecutor Alan Baron said, based on what is known, it appears to be a "very strong case" against Trump. "People who are very familiar with this kind of prosecution in New York are saying that it's very solid," he said. John Malcolm, a former federal prosecutor and legal expert at the Heritage Foundation, a conservative think tank said the former president could prevail.

Metaphysic CEO Tom Graham runs a company that creates authorized "deep fakes" of people using AI. As the industry leader in creating hyperreal content powered by generative AI, Metaphysic champions individual ownership and control of their AI likenesses and biometric data. By leveraging legal institutions and existing law and regulation, Graham, through this submission, demonstrates the increasingly fine line between reality and computer-generated media as he and Metaphysic seek to create, for the first time, a new bundle of intellectual property rights that must be available to any individual in the future. "Generative AI can create content that looks and feels real, and regular people's avatars can be inserted into content by third parties without their consent. This is not right, and we should never lose control over our identity, privacy or biometric data," said Graham, in a statement.

A group called Future of Life Institute has circulated a petition, signed by nearly 3,000 people in and around the technology industry, calling for a six-month moratorium on large scale experiments with artificial intelligence (AI). The petition has triggered a huge debate. Those who have signed the petition note that developers of GPT-4 and other large language model AIs promise that their technology will change the course of civilization, but claims they have not taken appropriate steps to protect civilization from harm. Those who oppose the petition fall into two large buckets: those who are comfortable with the status quo of rapidly developing AI models and those who believe the petition sponsors are so focused on the future that they ignore widespread harms from existing applications of AI. The latter argument is particularly interesting, as the group includes leading technologists and scholars in the AI field, including Timnit Gebru, Emily Bender, and Margaret Mitchell.

FOX Business correspondent Lydia Hu has the latest on jobs at risk as AI further develops on "America's Newsroom." The Federal Trade Commission (FTC) is making a play to be a key regulator of artificial intelligence (AI) systems, just as technology heavyweights and policymakers are clamoring for federal government oversight of AI applications. Last week's call for a moratorium on new AI development from tech giants like Elon Musk and Steve Wozniak kick-started a discussion about whether and how the government should step in and put guardrails up around potentially dangerous AI systems. Several lawmakers responded by saying a moratorium would be difficult to impose, leaving a huge gap between calls for action and the realities of how quickly Congress can act. However, the FTC has made it clear over the last week that it is prepared to bridge that gap and take a stab at regulating emerging AI systems. The federal agency tasked with policing "deceptive or unfair business practices" says it has a dog in this fight and is building up a capacity to take on the threats that AI poses to wary consumers.

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We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at https://segment-anything.com to foster research into foundation models for computer vision.

Intelligent or generative writing tools rely on large language models that recognize, summarize, translate, and predict content. This position paper probes the copyright interests of open data sets used to train large language models (LLMs). Our paper asks, how do LLMs trained on open data sets circumvent the copyright interests of the used data? We start by defining software copyright and tracing its history. We rely on GitHub Copilot as a modern case study challenging software copyright. Our conclusion outlines obstacles that generative writing assistants create for copyright, and offers a practical road map for copyright analysis for developers, software law experts, and general users to consider in the context of intelligent LLM-powered writing tools.

A Smart Legal Contract (SLC) is a specialized digital agreement comprising natural language and computable components. The Accord Project provides an open-source SLC framework containing three main modules: Cicero, Concerto, and Ergo. Currently, we need lawyers, programmers, and clients to work together with great effort to create a usable SLC using the Accord Project. This paper proposes a pipeline to automate the SLC creation process with several Natural Language Processing (NLP) models to convert law contracts to the Accord Project's Concerto model. After evaluating the proposed pipeline, we discovered that our NER pipeline accurately detects CiceroMark from Accord Project template text with an accuracy of 0.8. Additionally, our Question Answering method can extract one-third of the Concerto variables from the template text. We also delve into some limitations and possible future research for the proposed pipeline. Finally, we describe a web interface enabling users to build SLCs. This interface leverages the proposed pipeline to convert text documents to Smart Legal Contracts by using NLP models.