OpenAI is reinstating CEO Sam Altman to its board of directors and said it has "full confidence" in his leadership after an outside investigation into the turmoil that led the company to abruptly fire and rehire him in November. OpenAI said the investigation by the law firm WilmerHale concluded that Altman's ouster had been a "consequence of a breakdown in the relationship and loss of trust" between Altman and the prior board. The ChatGPT maker also said it has added three women to its board of directors: Sue Desmond-Hellman, a former CEO of the Bill & Melinda Gates Foundation; Nicole Seligman, a former Sony general counsel; and Instacart CEO Fidji Simo. The actions are a way for the San Francisco-based artificial intelligence company to show investors and customers that it is trying to move past the internal conflicts that nearly destroyed it last year and made global headlines. "I'm pleased this whole thing is over," Altman told reporters Friday, adding that he's been disheartened to see people leaking information to try to "pit us against each other" and demoralize the team. At the same time, he said he's learned from the experience and apologized for a dispute with a former board member he could have handled "with more grace and care".

Content creators increasingly utilize generative artificial intelligence (Gen-AI) on platforms such as YouTube, TikTok, Instagram, and various blogging sites to produce imaginative images, AI-generated videos, and articles using Large Language Models (LLMs). Despite its growing popularity, there remains an underexplored area concerning the specific domains where AI-generated content is being applied, and the methodologies content creators employ with Gen-AI tools during the creation process. This study initially explores this emerging area through a qualitative analysis of 68 YouTube videos demonstrating Gen-AI usage. Our research focuses on identifying the content domains, the variety of tools used, the activities performed, and the nature of the final products generated by Gen-AI in the context of user-generated content.

The entrepreneur who was suddenly fired as OpenAI CEO and from the ChatGPT developer's board last November, before regaining his CEO position days later, is now getting his director seat back, too. Altman and three veteran business executives, all women, were named to OpenAI's board on Friday, OpenAI announced in a blog post Friday. Sue Desmond-Hellmann, former CEO of the Bill & Melinda Gates Foundation; Nicole Seligman, a former Sony executive; and Fidji Simo, the CEO of Instacart and former Meta executive are the others joining the board. OpenAI has been looking to expand the board for months, after announcing an interim board after the November chaos. It was formed after a deal between some board members who had pushed Altman out but then agreed to step down when more than 95 percent of OpenAI employees threatened to quit if he wasn't brought back.

OpenAI's previous board shocked the tech world in November when it announced Altman's firing in a Friday afternoon blog post. After five days of wrangling between the board, company executives and OpenAI's investors, Altman was reinstated as CEO and three of the four board members who fired him stepped down. The fourth, Quora CEO Adam D'Angelo, is part of the current temporary board, but was not part of the subcommittee that led the review into the boardroom crisis, according to a December update to a blog post from OpenAI and the board.

Microsoft appears to have blocked several prompts in its Copilot tool that led the generative AI tool to spit out violent, sexual and other illicit images. The changes seem to have been implemented just after an engineer at the company wrote to the Federal Trade Commission to lay out severe concerns he had with Microsoft's GAI tech. When entering terms such as "pro choice," "four twenty" (a weed reference) or "pro life," Copilot now displays a message saying those prompts are blocked. It warns that repeated policy violations could lead to a user being suspended, according to CNBC. Users were also reportedly able to enter prompts related to children playing with assault rifles until earlier this week.

Title:Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models All reviewers agree on the quality of this submission.

Google's co-founder Sergey Brin has kept a low profile since quietly returning to work at the company. But the troubled launch of Google's artificial intelligence model Gemini resulted in a rare public utterance recently: "We definitely messed up." Brin's comments, at an AI "hackathon" event on 2 March, follow a slew of social media posts showing Gemini's image generation tool depicting a variety of historical figures – including popes, founding fathers of the US and, most excruciatingly, German second world war soldiers – as people of colour. The pictures, as well as Gemini chatbot responses that vacillated over whether libertarians or Stalin had caused the greater harm, led to an explosion of negative commentary from figures such as Elon Musk who saw it as another front in the culture wars. But criticism has also come from other sources including Google's chief executive, Sundar Pichai, who described some of the responses produced by Gemini as "completely unacceptable".

Large language models (LLMs) are demonstrating remarkable capabilities across various tasks despite lacking a foundation in human cognition. This raises the question: can these models, beyond simply mimicking human language patterns, offer insights into the mechanisms underlying human cognition? This study explores the ability of ChatGPT to predict human performance in a language-based memory task. Building upon theories of text comprehension, we hypothesize that recognizing ambiguous sentences (e.g., "Because Bill drinks wine is never kept in the house") is facilitated by preceding them with contextually relevant information. Participants, both human and ChatGPT, were presented with pairs of sentences. The second sentence was always a garden-path sentence designed to be inherently ambiguous, while the first sentence either provided a fitting (e.g., "Bill has chronic alcoholism") or an unfitting context (e.g., "Bill likes to play golf"). We measured both human's and ChatGPT's ratings of sentence relatedness, ChatGPT's memorability ratings for the garden-path sentences, and humans' spontaneous memory for the garden-path sentences. The results revealed a striking alignment between ChatGPT's assessments and human performance. Sentences deemed more related and assessed as being more memorable by ChatGPT were indeed better remembered by humans, even though ChatGPT's internal mechanisms likely differ significantly from human cognition. This finding, which was confirmed with a robustness check employing synonyms, underscores the potential of generative AI models to predict human performance accurately. We discuss the broader implications of these findings for leveraging LLMs in the development of psychological theories and for gaining a deeper understanding of human cognition.

Semantic Communication (SC) is a novel paradigm for data transmission in 6G. However, there are several challenges posed when performing SC in 3D scenarios: 1) 3D semantic extraction; 2) Latent semantic redundancy; and 3) Uncertain channel estimation. To address these issues, we propose a Generative AI Model assisted 3D SC (GAM-3DSC) system. Firstly, we introduce a 3D Semantic Extractor (3DSE), which employs generative AI models, including Segment Anything Model (SAM) and Neural Radiance Field (NeRF), to extract key semantics from a 3D scenario based on user requirements. The extracted 3D semantics are represented as multi-perspective images of the goal-oriented 3D object. Then, we present an Adaptive Semantic Compression Model (ASCM) for encoding these multi-perspective images, in which we use a semantic encoder with two output heads to perform semantic encoding and mask redundant semantics in the latent semantic space, respectively. Next, we design a conditional Generative adversarial network and Diffusion model aided-Channel Estimation (GDCE) to estimate and refine the Channel State Information (CSI) of physical channels. Finally, simulation results demonstrate the advantages of the proposed GAM-3DSC system in effectively transmitting the goal-oriented 3D scenario.

Elon Musk last week sued two of his OpenAI cofounders, Sam Altman and Greg Brockman, accusing them of "flagrant breaches" of the trio's original agreement that the company would develop artificial intelligence openly and without chasing profits. Late on Tuesday, OpenAI released partially redacted emails between Musk, Altman, Brockman, and others that provide a counternarrative. The emails suggest that Musk was open to OpenAI becoming more profit-focused relatively early on, potentially undermining his own claim that it deviated from its original mission. In one message Musk offers to fold OpenAI into his electric-car company Tesla to provide more resources, an idea originally suggested by an email he forwarded from an unnamed outside party. The newly published emails also imply that Musk was not dogmatic about OpenAI having to freely provide its developments to all.