Microsoft's president says the sacking of OpenAI boss Sam Altman was not because of AI safety worries.

LinkedIn began rolling out a generative AI feature to select users this spring, powered by OpenAI's GPT-4 model, to help premium subscribers write headlines and "about" sections. Users can generate text summarizing what's already in their profile and get spruced-up suggestions offered by the feature, which is highlighted with a gold button that says "write with AI." The capability is available to all of LinkedIn's millions of premium subscribers, and the company said it's exploring expanding access in the future.

Following Sam Altman's rollercoaster of a return as OpenAI's CEO, the company announced that it will now include Microsoft as a non-voting observer on its board. The question remains as to why the firm's largest investor wasn't on its board in the first place, but this seems to be somewhat addressed for now, at least. Altman is joined by co-founder Greg Brockman who resumes his role as President, whereas Mira Murati, who very briefly served as interim CEO throughout the drama, will return to her role as CTO. The announcement also confirms a new board consisting of former Salesforce CEO Bret Taylor (chair), former Clinton Treasury Secretary Larry Summers, and original member Adam D'Angelo, who is also Quora's co-founder and CEO. It was earlier rumored that Altman's exit was partly influenced by D'Angelo's seeming conflict of interest, as OpenAI was developing a potential competitor to Quora's Poe service -- the latter offers OpenAI's ChatGPT and GPT-4, along with several other text-generating AI models.

Altman was fired from OpenAI on Nov. 17, kicking off a chaotic five days as the tech industry grappled with the implications of the face of the AI revolution being unceremoniously removed from his company. Five days later, Altman was back, a new board had been appointed, consisting of Taylor, former treasury secretary Larry Summers and Quora CEO Adam D'Angelo, one of the previous board members who had removed Altman. Since then, Silicon Valley has speculated about who else would join the board and ultimately control the fate of the company.

Sam Altman marked his formal return as CEO helm of OpenAI today in a company memo that confirmed changes to the company's board including a new non-voting seat for the startup's primary investor, Microsoft. In a memo sent to staff and shared on OpenAI's blog, Altman painted the chaos of the past two weeks, triggered by the board's loss of trust in their CEO, during which almost the entire staff of the company threatened to quit, as a testament to the startup's resilience rather than a sign of instability. "You stood firm for each other, this company, and our mission," Altman wrote. "One of the most important things for the team that builds [artificial general intelligence] safely is the ability to handle stressful and uncertain situations, and maintain good judgment throughout. Altman was ousted on November 17. The company's nonprofit board of directors said that a deliberative review had concluded that Altman "was not consistently candid in his communications with the board." Under OpenAI's unusual structure, the board's duty was to the project's original, nonprofit mission of developing AI that is beneficial to humanity, not the company's business. That board that ejected Altman included the company's chief scientist, Ilya Sutskever, who later recanted and joined with staff who threatened to quit if Altman was not reinstated. Altman said that there would be no hard feelings over that, although his note left questions over Sutskever's future. "I love and respect Ilya, I think he's a guiding light of the field and a gem of a human being.

OpenAI's new board formally took over on Wednesday and said it would add an observer role for partner Microsoft, capping a dramatic chapter for the artificial-intelligence startup and launching a new phase of difficult decisions. The new board's initial three members were decided as part of CEO Sam Altman's return last week after the previous board abruptly fired him. The replacement directors' priorities include creating an independent committee to review the events around Altman's ouster, OpenAI's interim chairman, Bret Taylor, said in a note to employees on Wednesday.

Generative artificial intelligence (GenAI), exemplified by ChatGPT, Midjourney, and other state-of-the-art large language models and diffusion models, holds significant potential for transforming education and enhancing human productivity. While the prevalence of GenAI in education has motivated numerous research initiatives, integrating these technologies within the learning analytics (LA) cycle and their implications for practical interventions remain underexplored. This paper delves into the prospective opportunities and challenges GenAI poses for advancing LA. We present a concise overview of the current GenAI landscape and contextualise its potential roles within Clow's generic framework of the LA cycle. We posit that GenAI can play pivotal roles in analysing unstructured data, generating synthetic learner data, enriching multimodal learner interactions, advancing interactive and explanatory analytics, and facilitating personalisation and adaptive interventions. As the lines blur between learners and GenAI tools, a renewed understanding of learners is needed. Future research can delve deep into frameworks and methodologies that advocate for human-AI collaboration. The LA community can play a pivotal role in capturing data about human and AI contributions and exploring how they can collaborate most effectively. As LA advances, it is essential to consider the pedagogical implications and broader socioeconomic impact of GenAI for ensuring an inclusive future.

Climate hazards can cause major disasters when they occur simultaneously as compound hazards. To understand the distribution of climate risk and inform adaptation policies, scientists need to simulate a large number of physically realistic and spatially coherent events. Current methods are limited by computational constraints and the probabilistic spatial distribution of compound events is not given sufficient attention. The bottleneck in current approaches lies in modelling the dependence structure between variables, as inference on parametric models suffers from the curse of dimensionality. Generative adversarial networks (GANs) are well-suited to such a problem due to their ability to implicitly learn the distribution of data in high-dimensional settings. We employ a GAN to model the dependence structure for daily maximum wind speed, significant wave height, and total precipitation over the Bay of Bengal, combining this with traditional extreme value theory for controlled extrapolation of the tails. Once trained, the model can be used to efficiently generate thousands of realistic compound hazard events, which can inform climate risk assessments for climate adaptation and disaster preparedness. The method developed is flexible and transferable to other multivariate and spatial climate datasets.

The widespread of generative artificial intelligence has heightened concerns about the potential harms posed by AI-generated texts, primarily stemming from factoid, unfair, and toxic content. Previous researchers have invested much effort in assessing the harmlessness of generative language models. However, existing benchmarks are struggling in the era of large language models (LLMs), due to the stronger language generation and instruction following capabilities, as well as wider applications. In this paper, we propose FFT, a new benchmark with 2116 elaborated-designed instances, for LLM harmlessness evaluation with factuality, fairness, and toxicity. To investigate the potential harms of LLMs, we evaluate 9 representative LLMs covering various parameter scales, training stages, and creators. Experiments show that the harmlessness of LLMs is still under-satisfactory, and extensive analysis derives some insightful findings that could inspire future research for harmless LLM research. Figure 1: Examples of three kinds of harmful contents Warning: This paper contains potentially generated by LLMs. Note that the southernmost point sensitive content.

Vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. However, many VLMs rely on proprietary data and are not open-source, which restricts the use of white-box approaches for fine-tuning. As such, we aim to develop a black-box approach to optimize VLMs through natural language prompts, thereby avoiding the need to access model parameters, feature embeddings, or even output logits. We propose employing chat-based LLMs to search for the best text prompt for VLMs. Specifically, we adopt an automatic hill-climbing procedure that converges to an effective prompt by evaluating the performance of current prompts and asking LLMs to refine them based on textual feedback, all within a conversational process without human-in-the-loop. In a challenging 1-shot image classification setup, our simple approach surpasses the white-box continuous prompting method (CoOp) by an average of 1.5% across 11 datasets including ImageNet. Our approach also outperforms both human-engineered and LLM-generated prompts. We highlight the advantage of conversational feedback that incorporates both positive and negative prompts, suggesting that LLMs can utilize the implicit gradient direction in textual feedback for a more efficient search. In addition, we find that the text prompts generated through our strategy are not only more interpretable but also transfer well across different VLM architectures in a black-box manner. Lastly, we demonstrate our framework on a state-of-the-art black-box VLM (DALL-E 3) for text-to-image optimization.