In the rapidly evolving landscape of artificial intelligence, significant advancements are being made, impacting a broad spectrum of fields ranging from Education [Becker et al.(2018)] to road transit [Banks and Stanton(2019)]. Looking ahead, these advancements are poised to significantly influence the dynamics of team environments. While research on teams only a few years ago highlighted the potential usefulness of AI integration in both research and practical settings, it also acknowledged the limitations of AI technologies in fully mimicking and comprehending the complex aspects of human-team interactions at the time [Seeber et al.(2020)]. However, with recent developments in generative AI and Large Language Models i.e., (OpenAI's GPT-4 [OpenAI(2023)], Google's Bard [Manyika and Hsiao(2023)] and Gemini [Team et al.(2023)]), we are approaching a level where AI-human teams can collaborate more effectively e.g., [Lakhnati et al.(2023)]. This progression prompts a critical question: How can we harness the evolving capabilities of AI to effectively enhance and integrate it into human-AI team dynamics, particularly in settings where traditional automation tools face limitations?

In recent years, novel communication strategies have emerged to face the challenges that the increased number of connected devices and the higher quality of transmitted information are posing. Among them, semantic communication obtained promising results especially when combined with state-of-the-art deep generative models, such as large language or diffusion models, able to regenerate content from extremely compressed semantic information. However, most of these approaches focus on single-user scenarios processing the received content at the receiver on top of conventional communication systems. In this paper, we propose to go beyond these methods by developing a novel generative semantic communication framework tailored for multi-user scenarios. This system assigns the channel to users knowing that the lost information can be filled in with a diffusion model at the receivers. Under this innovative perspective, OFDMA systems should not aim to transmit the largest part of information, but solely the bits necessary to the generative model to semantically regenerate the missing ones. The thorough experimental evaluation shows the capabilities of the novel diffusion model and the effectiveness of the proposed framework, leading towards a GenAI-based next generation of communications.

It's nothing new for computers to mimic human social etiquette, emotion, or humor. We just aren't used to them doing it very well. OpenAI's presentation of an all-new version of ChatGPT on Monday suggests that's about to change. It's built around an updated AI model called GPT-4o, which OpenAI says is better able to make sense of visual and auditory input, describing it as "multimodal." You can point your phone at something, like a broken coffee cup or differential equation, and ask ChatGPT to suggest what to do.

Sutskever was one of the OpenAI board members who voted to remove co-founder Sam Altman as chief executive in November. Altman was reinstated days later, and Sutskever and the other directors who voted against Altman resigned from the board.

On Tuesday, Google announced its own new tools, including a conversational assistant called Gemini Live, which can do many of the same things. It also revealed that it's building a sort of "do-everything" AI agent, which is currently in development but will not be released until later this year. Soon you'll be able to explore for yourself to gauge whether you'll turn to these tools in your daily routine as much as their makers hope, or whether they're more like a sci-fi party trick that eventually loses its charm. Here's what you should know about how to access these new tools, what you might use them for, and how much it will cost. What it's capable of: The model can talk with you in real time, with a response delay of about 320 milliseconds, which OpenAI says is on par with natural human conversation.

When OpenAI released its ChatGPT tool in November 2022, it was a shot across the bows of Google, with generative artificial intelligence promising a new way to access the world's information beyond search engines. Since then, the rivalry between these firms has only grown, with both announcing new services this week. While there are signs that OpenAI is winning this race, is either company aiming for a future anyone actually wants?

OpenAI's co-founder and chief scientist, Ilya Sutskever, is leaving the startup at the center of today's artificial intelligence boom. "After almost a decade, I have made the decision to leave OpenAI," Sutskever said in a post on X. Sutskever played a key role in the dramatic firing and rehiring in November last year of OpenAI's CEO, Sam Altman. At the time, Sutskever was on the board of OpenAI and helped to orchestrate Altman's firing. Days later, he reversed course, signing on to an employee letter demanding Altman's return and expressing regret for his "participation in the board's actions". After Altman returned, Sutskever was removed from the board, and his position at the company became unclear.

Ilya Sutskever has announced on X, formerly known as Twitter, that he's leaving OpenAI almost a decade after he co-founded the company. He's confident that OpenAI "will build [artificial general intelligence] that is both safe and beneficial" under the leadership of CEO Sam Altman, President Greg Brockman and CTO Mira Murati, he continued. In his own post about Sutskever's departure, Altman called him "one of the greatest minds of our generation" and credited him for his work with the company. Jakub Pachocki, OpenAI's previous Director of Research who headed the development of GPT-4 and OpenAI Five, has taken Sutskever's role as Chief Scientist. After almost a decade, I have made the decision to leave OpenAI.

OpenAI Chief Scientist and co-founder Ilya Sutskever is leaving the artificial intelligence company, a departure that ends months of speculation in Silicon Valley about the future of a top AI researcher who played a key role in the brief ouster of Sam Altman last year. Sutskever will be replaced by Research Director Jakub Pachocki, OpenAI said on its blog Tuesday. In a post on X, Sutskever called trajectory of OpenAI "miraculous" and said that he was confident the company will build AI that is "both safe and beneficial" under its current leadership. The exit removes an executive and renowed researcher who has played a pivotal role in the company since its earliest days, helping guide discussions over the safety of AI technology and at times differing with Altman over strategy. When OpenAI was founded in 2015, he served as its research director after being recruited to join the company by Elon Musk.

The rapid advancement of AI technologies yields numerous future impacts on individuals and society. Policy-makers are therefore tasked to react quickly and establish policies that mitigate those impacts. However, anticipating the effectiveness of policies is a difficult task, as some impacts might only be observable in the future and respective policies might not be applicable to the future development of AI. In this work we develop a method for using large language models (LLMs) to evaluate the efficacy of a given piece of policy at mitigating specified negative impacts. We do so by using GPT-4 to generate scenarios both pre- and post-introduction of policy and translating these vivid stories into metrics based on human perceptions of impacts. We leverage an already established taxonomy of impacts of generative AI in the media environment to generate a set of scenario pairs both mitigated and non-mitigated by the transparency legislation of Article 50 of the EU AI Act. We then run a user study (n=234) to evaluate these scenarios across four risk-assessment dimensions: severity, plausibility, magnitude, and specificity to vulnerable populations. We find that this transparency legislation is perceived to be effective at mitigating harms in areas such as labor and well-being, but largely ineffective in areas such as social cohesion and security. Through this case study on generative AI harms we demonstrate the efficacy of our method as a tool to iterate on the effectiveness of policy on mitigating various negative impacts. We expect this method to be useful to researchers or other stakeholders who want to brainstorm the potential utility of different pieces of policy or other mitigation strategies.