More details of Sam Altman's sudden ousting as CEO of OpenAI have emerged, with several senior researchers quitting the company, and executives and investors from across the industry expressing shock and confusion at what is increasingly being perceived as a board coup. Hours after Sam Altman was booted from the company by its board, Greg Brockman, another OpenAI cofounder and the company's chairman, quit in protest. Brockman later posted details of Altman's removal suggesting that the company's chief scientist, Ilya Sutskever, had orchestrated the effort to remove the CEO. Brockman's post claimed that Altman was told he was being fired by Sutskever, the company's chief scientist and a member of its board. Several accounts from inside the company suggest that a disagreement between Sutskever and Altman centered around the company's direction, and specifically its ability to build more capable AI technology safety.

This article is from Big Technology, a newsletter by Alex Kantrowitz. For years, Google dominated search with little opposition. The format faced little disruption; it was always just a bunch of blue links. And the company's multibillion-dollar deals with phone-makers to keep Google search as a default cemented its lead. But its comfortable perch is actually, really starting to fade.

Sam Altman, one of the biggest names in tech, was sacked from ChatGPT-maker OpenAI without warning.

OpenAI's firing of Sam Altman followed wide-ranging disagreements between the chief executive officer and his board -- in particular Ilya Sutskever, an OpenAI co-founder and the company's chief scientist -- according to a person familiar with the matter. The debates included differences of opinion on AI safety, the speed of development of the technology and the commercialization of the company, said the person, who asked not to be identified discussing private information. Altman's ambitions may have also played a role in the divorce. Altman has been looking to raise tens of billions of dollars from Middle Eastern sovereign wealth funds to create an AI chip startup to compete with processors made by Nvidia Corp., according to a person with knowledge of the investment proposal. Altman was also courting SoftBank Group chairman Masayoshi Son for a multibillion-dollar investment in a new company to make AI-oriented hardware in partnership with former Apple designer Jony Ive.

Sam Altman and Greg Brockman were "shocked and saddened by what the board did" and are still trying to figure out what exactly happened. The former CEO and the former President of OpenAI have published a post on X, sharing the details of what they do know and how they found out the former was being fired. Apparently, company co-founder Ilya Sutskever invited Altman for a meeting at noon on Friday, which was then attended by the whole board except for Brockman. It was at that meeting that Altman found out he was being fired and that OpenAI was going to announce it "very soon." Shortly after that, Sutskever reportedly invited Brockman to a separate Google Meet conference, where he was told that Altman had gotten fired and that he was being removed from the board.

Earlier this year, I asked Sam Altman whether decisions made by OpenAI's leaders might one day lead to unemployment among the masses. "Jobs are definitely going to go away, full stop," he told me. He couldn't have known then that his would be among the first. In a blog post released this afternoon, OpenAI--the artificial-intelligence juggernaut for which Altman was the CEO--announced that he would be leaving, effective immediately, because, according to the statement, "he was not consistently candid in his communications with the board." The statement did not specify the nature of Altman's alleged misrepresentations, but they must have concerned serious matters to merit such a dramatic and public rebuke.

Artificial Intelligence Impact Assessments ("AIIAs"), a family of tools that provide structured processes to imagine the possible impacts of a proposed AI system, have become an increasingly popular proposal to govern AI systems. Recent efforts from government or private-sector organizations have proposed many diverse instantiations of AIIAs, which take a variety of forms ranging from open-ended questionnaires to graded score-cards. However, to date that has been limited evaluation of existing AIIA instruments. We conduct a classroom study (N = 38) at a large research-intensive university (R1) in an elective course focused on the societal and ethical implications of AI. We assign students to different organizational roles (for example, an ML scientist or product manager) and ask participant teams to complete one of three existing AI impact assessments for one of two imagined generative AI systems. In our thematic analysis of participants' responses to pre- and post-activity questionnaires, we find preliminary evidence that impact assessments can influence participants' perceptions of the potential risks of generative AI systems, and the level of responsibility held by AI experts in addressing potential harm. We also discover a consistent set of limitations shared by several existing AIIA instruments, which we group into concerns about their format and content, as well as the feasibility and effectiveness of the activity in foreseeing and mitigating potential harms. Drawing on the findings of this study, we provide recommendations for future work on developing and validating AIIAs.

Generative Artificial Intelligence (AI), particularly tools like OpenAI's popular ChatGPT, is reshaping the landscape of computer science research. Used wisely, these tools can boost the productivity of a computer research scientist. This paper provides an exploration of the diverse applications of ChatGPT and other generative AI technologies in computer science academic research, making recommendations about the use of Generative AI to make more productive the role of the computer research scientist, with the focus of writing new research papers. We highlight innovative uses such as brainstorming research ideas, aiding in the drafting and styling of academic papers and assisting in the synthesis of state-of-the-art section. Further, we delve into using these technologies in understanding interdisciplinary approaches, making complex texts simpler, and recommending suitable academic journals for publication. Significant focus is placed on generative AI's contributions to synthetic data creation, research methodology, and mentorship, as well as in task organization and article quality assessment. The paper also addresses the utility of AI in article review, adapting texts to length constraints, constructing counterarguments, and survey development. Moreover, we explore the capabilities of these tools in disseminating ideas, generating images and audio, text transcription, and engaging with editors. We also describe some non-recommended uses of generative AI for computer science research, mainly because of the limitations of this technology.

Many environmental remediation and energy applications (conversion and storage) for sustainability need design and development of green novel materials. Discovery processes of such novel materials are time taking and cumbersome due to large number of possible combinations and permutations of materials structures. Often theoretical studies based on Density Functional Theory (DFT) and other theories, coupled with Simulations are conducted to narrow down sample space of candidate materials, before conducting laboratory-based synthesis and analytical process. With the emergence of artificial intelligence (AI), AI techniques are being tried in this process too to ease out simulation time and cost. However tremendous values of previously published research from various parts of the world are still left as labor-intensive manual effort and discretion of individual researcher and prone to human omissions. AIMS-EREA is our novel framework to blend best of breed of Material Science theory with power of Generative AI to give best impact and smooth and quickest discovery of material for sustainability. This also helps to eliminate the possibility of production of hazardous residues and bye-products of the reactions. AIMS-EREA uses all available resources -- Predictive and Analytical AI on large collection of chemical databases along with automated intelligent assimilation of deep materials knowledge from previously published research works through Generative AI. We demonstrate use of our own novel framework with an example, how this framework can be successfully applied to achieve desired success in development of thermoelectric material for waste heat conversion.

Score-based generative models (SGMs) is a recent class of deep generative models with state-of-the-art performance in many applications. In this paper, we establish convergence guarantees for a general class of SGMs in 2-Wasserstein distance, assuming accurate score estimates and smooth log-concave data distribution. We specialize our result to several concrete SGMs with specific choices of forward processes modelled by stochastic differential equations, and obtain an upper bound on the iteration complexity for each model, which demonstrates the impacts of different choices of the forward processes. We also provide a lower bound when the data distribution is Gaussian. Numerically, we experiment SGMs with different forward processes, some of which are newly proposed in this paper, for unconditional image generation on CIFAR-10. We find that the experimental results are in good agreement with our theoretical predictions on the iteration complexity, and the models with our newly proposed forward processes can outperform existing models.