The advent of ChatGPT by OpenAI has prompted extensive discourse on its potential implications for science and higher education. While the impact on education has been a primary focus, there is limited empirical research on the effects of large language models (LLMs) and LLM-based chatbots on science and scientific practice. To investigate this further, we conducted a Delphi study involving 72 experts specialising in research and AI. The study focused on applications and limitations of LLMs, their effects on the science system, ethical and legal considerations, and the required competencies for their effective use. Our findings highlight the transformative potential of LLMs in science, particularly in administrative, creative, and analytical tasks. However, risks related to bias, misinformation, and quality assurance need to be addressed through proactive regulation and science education. This research contributes to informed discussions on the impact of generative AI in science and helps identify areas for future action.

In recent years, discussions of responsible AI practices have seen growing support for "participatory AI" approaches, intended to involve members of the public in the design and development of AI systems. Prior research has identified a lack of standardised methods or approaches for how to use participatory approaches in the AI development process. At present, there is a dearth of evidence on attitudes to and approaches for participation in the sites driving major AI developments: commercial AI labs. Through 12 semi-structured interviews with industry practitioners and subject-matter experts, this paper explores how commercial AI labs understand participatory AI approaches and the obstacles they have faced implementing these practices in the development of AI systems and research. We find that while interviewees view participation as a normative project that helps achieve "societally beneficial" AI systems, practitioners face numerous barriers to embedding participatory approaches in their companies: participation is expensive and resource intensive, it is "atomised" within companies, there is concern about exploitation, there is no incentive to be transparent about its adoption, and it is complicated by a lack of clear context. These barriers result in a piecemeal approach to participation that confers no decision-making power to participants and has little ongoing impact for AI labs. This papers contribution is to provide novel empirical research on the implementation of public participation in commercial AI labs, and shed light on the current challenges of using participatory approaches in this context.

This research delves into the intersection of illustration art and artificial intelligence (AI), focusing on how illustrators engage with AI agents that embody their original characters (OCs). We introduce 'ORIBA', a customizable AI chatbot that enables illustrators to converse with their OCs. This approach allows artists to not only receive responses from their OCs but also to observe their inner monologues and behavior. Despite the existing tension between artists and AI, our study explores innovative collaboration methods that are inspiring to illustrators. By examining the impact of AI on the creative process and the boundaries of authorship, we aim to enhance human-AI interactions in creative fields, with potential applications extending beyond illustration to interactive storytelling and more.

Intelligence is a difficult concept to define, despite many attempts at doing so. Rather than trying to settle on a single definition, this article introduces a broad perspective on what intelligence is, by laying out a cascade of definitions that induces both a nested hierarchy of three levels of intelligence and a wider-ranging space that is built around them and approximations to them. Within this intelligence space, regions are identified that correspond to both natural - most particularly, human - intelligence and artificial intelligence (AI), along with the crossover notion of humanlike intelligence. These definitions are then exploited in early explorations of four more advanced, and likely more controversial, topics: the singularity, generative AI, ethics, and intellectual property. Consider the space of all possible forms of intelligence, what can be called the intelligence space. We know very little about the nature of this space other than how it is currently populated with various, often only partially understood, instances of natural and artificial intelligence, although some earlier thoughts on this space - typically under different names - can be found in [2-5]. Rosenbloom [6] takes a different tack at understanding this space, in attempting to define a set of dichotomies whose cross product spans technologies underlying artificial and human intelligence, with the possibility of it spanning a much larger swath of the intelligence space. Here, another tack is taken, of starting with a generic, trilevel definition of intelligence that anchors the space and hypothesizing that a range of approximations to this definition could flesh out much of the full space (Section 1). This space is then exploited by exploring the relationship between natural and artificial intelligence via the mapping of human intelligence, humanlike intelligence, artificial intelligence, and cognitive science onto regions of this space (Section 2) and then beginning an exploration into the implications of this all for four more advanced, and likely more controversial topics: the singularity, as it relates to intelligence; ethics, with a particular focus on its relationship to artificial intelligence (AI); the current white-hot topic of generative AI, with a particular focus on large language models (LLMs); and intellectual property, with a particular focus on whether or not its creation might be ascribed to AI systems (Section 3).

We introduce a framework for automatically defining and learning deep generative models with problem-specific structure. We tackle problem domains that are more traditionally solved by algorithms such as sorting, constraint satisfaction for Sudoku, and matrix factorization. Concretely, we train diffusion models with an architecture tailored to the problem specification. This problem specification should contain a graphical model describing relationships between variables, and often benefits from explicit representation of subcomputations. Permutation invariances can also be exploited. Across a diverse set of experiments we improve the scaling relationship between problem dimension and our model's performance, in terms of both training time and final accuracy. Our code can be found at https://github.com/plai-group/gsdm.

Mercedes-Benz vehicles are known for their quiet cabins, but things are going to get a little louder in them soon. The luxury automaker has announced that it is launching a software update that will bring ChatGPT into its vehicles through a collaboration with the Microsoft Azure OpenAI Service., starting on June 16. The feature will be integrated into the MBUX infotainment system, which already offers a wide array of voice commands through the "Hey, Mercedes" voice assistant feature. ChatGPT will allow occupants to have "conversations with natural dialogues and follow-up questions" with the generative artificial intelligence platform. A beta version of Mercedes-Benz's ChatGPT voice assistant is launching on June 16.

Europeans wanting to try Google Bard will have to wait. The Irish Data Protection Commission (IDPC), the main overseer of data in the European Union, has forced Google to delay the rollout of its Bard chatbot in the region. The generative AI was supposed to launch in the EU this week, but IDPC Deputy Commissioner Graham Doyle says his agency hasn't received a "detailed" privacy briefing, a data impact assessment or supporting info. The Commission is still in the midst of an "ongoing examination" of Bard, according to Doyle. It isn't estimating when it might wrap up that investigation, but it plans to share info with other EU data regulators as quickly as possible.

Thank you for joining us on "The cloud hub: From cloud chaos to clarity." Generative AI can enable chatbots to provide meaningful and relevant responses to users, but there are risks and challenges that must be considered when adopting it.

FOX Business correspondent Lydia Hu has the latest on jobs at risk as AI further develops on "America's Newsroom." Lightning Motorcycles is speeding up the design of its bikes with the help of artificial intelligence. The San Jose, California-based, electric motorcycle builder has started using new computer-aided design software from AutoCAD that leverages generative AI to develop complex components in a fraction of the time it would take engineers to do it using standard methods. "It really allows our engineers to draw on a much greater database of ideas," Lightning Motors Corp. CEO JoJo Hatfield told Fox News Digital. "Where we would typically be limited by the engineers' experience, we can now draw on the generative design software's database of options."

Politicians think they have a way to hold companies accountable for troublesome generative AI: take away their legal protection. Senators Richard Blumenthal and Josh Hawley have introduced a No Section 230 Immunity for AI Act that, as the name suggests, would prevent OpenAI, Google and similar firms from using the Communications Decency Act's Section 230 to waive liability for harmful content and avoid lawsuits. If someone created a deepfake image or sound bite to ruin a reputation, for instance, the tool developer could be held responsible alongside the person who used it. Hawley characterizes the bill as forcing AI creators to "take responsibility for business decisions" as they're developing products. He also casts the legislation as a "first step" toward creating rules for AI and establishing safety measures.