Microsoft on Monday announced it has hired ousted OpenAI CEO and co-founder Sam Altman to head a new advanced AI research team after OpenAI's board decided against reinstating Altman following negotiations over the weekend. The appointment caps off a chaotic weekend for the tech world, which kicked off Friday after OpenAI's board of directors suddenly fired Altman, a high-profile figure and wunderkind of the artificial intelligence boom. It appeared there was a chance Altman could return to OpenAI but the board instead decided to appoint its second interim CEO, seemingly ending Altman's hopes of returning to the helm after his shocking ouster. Microsoft Chairman and CEO Satya Nadella said Monday the tech giant -- who is a lead investor in OpenAI -- is also bringing in OpenAI co-founder Greg Brockman, who resigned as president of the company in protest over the firing of Altman. "We're extremely excited to share the news that Sam Altman and Greg Brockman, together with colleagues, will be joining Microsoft to lead a new advanced AI research team," Nadella wrote in a statement posted on X, the platform formerly known as Twitter.

Efforts by a group of OpenAI executives and investors to reinstate Sam Altman to his role as chief executive officer reached an impasse over the makeup and role of the board, according to people familiar with the negotiations. Resolution could come quickly, though talks are fluid and ongoing. Altman, who was fired Friday, is open to returning but wants to see governance changes, including the removal of existing board members, said the people, who asked not to be identified because the negotiations are private. He's also seeking a statement absolving him of wrongdoing, they said. After facing intense outrage over the ouster, the board initially agreed in principle to step down, but have so far refused to officially do so.

To truly understand the events of this past weekend--the shocking, sudden ousting of OpenAI's CEO, Sam Altman, arguably the avatar of the generative-AI revolution, followed by reports that the company was in talks to bring him back, and then yet another shocking revelation that he would start a new AI team at Microsoft instead--one must understand that OpenAI is not a technology company. It was founded in 2015 as a nonprofit dedicated to the creation of artificial general intelligence, or AGI, that should benefit "humanity as a whole." In this conception, OpenAI would operate more like a research facility or a think tank. The company's charter bluntly states that OpenAI's "primary fiduciary duty is to humanity," not to investors or even employees. In 2019, OpenAI launched a subsidiary with a "capped profit" model that could raise money, attract top talent, and inevitably build commercial products.

Healthy companies led by competent, commercially successful and globally beloved founders generally don't tend to fire them. And, as Sam Altman walked on stage in San Francisco on Nov. 6, all those things could have described his role at OpenAI. The co-founder and chief executive officer had kicked off a global race for artificial intelligence supremacy, helped OpenAI surpass much larger competitors, and was, by this point, regularly compared to Bill Gates and Steve Jobs. Eleven days later he would be fired -- kicking off a chaotic weekend during which executives and investors loyal to Altman were agitating for his return. The board ignored them, and hired Emmett Shear, the former Twitch CEO, instead.

With the rise of Large Language Models (LLMs), notably characterized by GPT frameworks, there emerges a catalyst for novel healthcare applications. Earlier iterations of chatbot caregivers, though existent, have yet to achieve a dimension of human-like authenticity. This paper unveils `MemoryCompanion' a pioneering digital health solution explicitly tailored for Alzheimer's disease (AD) patients and their caregivers. Drawing upon the nuances of GPT technology and prompt engineering, MemoryCompanion manifests a personalized caregiving paradigm, fostering interactions via voice-cloning and talking-face mechanisms that resonate with the familiarity of known companions. Using advanced prompt-engineering, the system intricately adapts to each patient's distinct profile, curating its content and communication style accordingly. This approach strives to counteract prevalent issues of social isolation and loneliness frequently observed in AD demographics. Our methodology, grounded in its innovative design, addresses both the caregiving and technological challenges intrinsic to this domain.

From text to audio and image, today's generative-AI systems are trained on large quantities of human-generated content. Most of this content is obtained by scraping a variety of online sources. As generative AI becomes more common, it is reasonable to expect that future data scraping will invariably catch generative AI's own creations. We ask what happens when these generative systems are trained on varying combinations of human-generated and AI-generated content. Although it is early in the evolution of generative AI, there is already some evidence that retraining a generative AI model on its own creation - what we call model poisoning - leads to a range of artifacts in the output of the newly trained model. It has been shown, for example, that when retrained on their own output, large language models (LLMs) contain irreversible defects that cause the model to produce gibberish - so-called model collapse [22].

Chatbots and AI-agents have become widespread in customer service and in applications like knowledge management, recommender systems, and help desks. Businesses increasingly want to benefit from the capabilities of large language models like OpenAI's GPT-4 and applications powered by such models. Nevertheless, the use of generative AI by companies has been seriously slowed down by concerns about data protection and by the fact that generative AI is known to sometimes make things up - create "hallucinations" as it is often called. Even if an answer does not contain hallucinated information, it may still suffer from incompleteness or misleadingly connected pieces of information. However, companies that want to use AI-agents in non-trivial circumstances need to be able to control them, in particular in customer-facing applications. It would be very unfortunate if it misinforms customers about the company's products or prices. It should also stick very closely to the intended marketing messages. While there is a lot of discussion about "safe AI", "reliable AI", "trustworthy AI", "explainable AI" (XAI) etc., the question of "controllable AI" is rarely discussed. However, as stated above, it is very often crucial that enterprises cannot just rely on, but are in fact able to control an AI system (more precisely, exercise control at design time how the system will behave at runtime).

AI-based image generation has continued to rapidly improve, producing increasingly more realistic images with fewer obvious visual flaws. AI-generated images are being used to create fake online profiles which in turn are being used for spam, fraud, and disinformation campaigns. As the general problem of detecting any type of manipulated or synthesized content is receiving increasing attention, here we focus on a more narrow task of distinguishing a real face from an AI-generated face. This is particularly applicable when tackling inauthentic online accounts with a fake user profile photo. We show that by focusing on only faces, a more resilient and general-purpose artifact can be detected that allows for the detection of AI-generated faces from a variety of GAN- and diffusion-based synthesis engines, and across image resolutions (as low as 128 x 128 pixels) and qualities.

According to tech news site the Information, Mr Altman and Greg Brockman - another co-founder who quit on Friday as the company's president - were invited to the firm's San Francisco headquarters for talks on Sunday.

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