Integrating generative AI (GAI) into higher education is crucial for preparing a future generation of GAI-literate students. Yet a thorough understanding of the global institutional adoption policy remains absent, with most of the prior studies focused on the Global North and the promises and challenges of GAI, lacking a theoretical lens. This study utilizes the Diffusion of Innovations Theory to examine GAI adoption strategies in higher education across 40 universities from six global regions. It explores the characteristics of GAI innovation, including compatibility, trialability, and observability, and analyses the communication channels and roles and responsibilities outlined in university policies and guidelines. The findings reveal a proactive approach by universities towards GAI integration, emphasizing academic integrity, teaching and learning enhancement, and equity. Despite a cautious yet optimistic stance, a comprehensive policy framework is needed to evaluate the impacts of GAI integration and establish effective communication strategies that foster broader stakeholder engagement. The study highlights the importance of clear roles and responsibilities among faculty, students, and administrators for successful GAI integration, supporting a collaborative model for navigating the complexities of GAI in education. This study contributes insights for policymakers in crafting detailed strategies for its integration.

These authors contributed equally to this work. Abstract Generative AI has the potential to revolutionize drug discovery. Yet, despite recent advances in machine learning, existing models cannot generate molecules that satisfy all desired physicochemical properties. Herein, we describe IDOLpro, a novel generative chemistry AI combining deep diffusion with multi-objective optimization for structure-based drug design. The latent variables of the diffusion model are guided by differentiable scoring functions to explore uncharted chemical space and generate novel ligands in silico, optimizing a plurality of target physicochemical properties. We demonstrate its effectiveness by generating ligands with optimized binding affinity and synthetic accessibility on two benchmark sets. IDOLpro produces ligands with binding affinities over 10% higher than the next best state-of-the-art on each test set. On a test set of experimental complexes, IDOLpro is the first to surpass the performance of experimentally observed ligands. IDOLpro can accommodate other scoring functions (e.g. ADME-Tox) to accelerate hit-finding, hit-to-lead, and lead optimization for drug discovery.

Out-of-distribution (OOD) detection is a critical task in machine learning that seeks to identify abnormal samples. Traditionally, unsupervised methods utilize a deep generative model for OOD detection. However, such approaches necessitate a different model when evaluating abnormality against a new distribution. With the emergence of foundational generative models, this paper explores whether a single generalist model can also perform OOD detection across diverse tasks. To that end, we introduce our method, Diffusion Paths, (DiffPath) in this work. DiffPath proposes to utilize a single diffusion model originally trained to perform unconditional generation for OOD detection. Specifically, we introduce a novel technique of measuring the rate-of-change and curvature of the diffusion paths connecting samples to the standard normal. Extensive experiments show that with a single model, DiffPath outperforms prior work on a variety of OOD tasks involving different distributions. Our code is publicly available at https://github.com/clear-nus/diffpath.

ZeroEyes CEO Mike Lahiff joins'Fox & Friends' to explain how the technology works to help keep students safe in schools. Google's flagship developer conference called I/O just wrapped up with interesting leaps in how the Big Tech giant is planning to change the world. Here are the seven biggest things we learned from Google at I/O 2024. Google's I/O event was largely an opportunity for it to make its case to developers -- and, to a lesser extent, consumers -- as to why its artificial intelligence is ahead of rivals Microsoft and OpenAI. Here's a rundown of the seven highlights to keep an eye on.

OpenAI reportedly made exiting employees choose between keeping their vested equity and being able to speak out against the company. According to Vox, which viewed the document in question, employees could "lose all vested equity they earned during their time at the company, which is likely worth millions of dollars" if they didn't sign a nondisclosure and non-disparagement agreement, thanks to a provision in the off-boarding papers. OpenAI CEO Sam Altman confirmed in a tweet on Saturday evening that such a provision did exist, but said "we have never clawed back anyone's vested equity, nor will we do that if people do not sign a separation agreement (or don't agree to a non-disparagement agreement)." An OpenAI spokesperson echoed this in a statement to Vox, and Altman said the company "was already in the process of fixing the standard exit paperwork over the past month or so." But as Vox notes in its report, at least one former OpenAI employee has spoken publicly about sacrificing equity by declining to sign an NDA upon leaving.

In the era of generative artificial intelligence (AI), the fusion of large language models (LLMs) offers unprecedented opportunities for innovation in the field of modern education. We embark on an exploration of prompted LLMs within the context of educational and assessment applications to uncover their potential. Through a series of carefully crafted research questions, we investigate the effectiveness of prompt-based techniques in generating open-ended questions from school-level textbooks, assess their efficiency in generating open-ended questions from undergraduate-level technical textbooks, and explore the feasibility of employing a chain-of-thought inspired multi-stage prompting approach for language-agnostic multiple-choice question (MCQ) generation. Additionally, we evaluate the ability of prompted LLMs for language learning, exemplified through a case study in the low-resource Indian language Bengali, to explain Bengali grammatical errors. We also evaluate the potential of prompted LLMs to assess human resource (HR) spoken interview transcripts. By juxtaposing the capabilities of LLMs with those of human experts across various educational tasks and domains, our aim is to shed light on the potential and limitations of LLMs in reshaping educational practices.

A former senior employee at OpenAI has said the company behind ChatGPT is prioritising "shiny products" over safety, revealing that he quit after a disagreement over key aims reached "breaking point". Jan Leike was a key safety researcher at OpenAI as its co-head of superalignment, ensuring that powerful artificial intelligence systems adhere to human values and aims. His intervention comes before a global artificial intelligence summit in Seoul next week, where politicians, experts and tech executives will discuss oversight of the technology. Leike resigned days after the San Francisco-based company launched its latest AI model, GPT-4o. His departure means two senior safety figures at OpenAI have left this week following the resignation of Ilya Sutskever, OpenAI's co-founder and fellow co-head of superalignment.

This week, artificial intelligence caught up with the future – or at least Hollywood's idea of it from a decade ago. "It feels like AI from the movies," wrote the OpenAI chief executive, Sam Altman, of his latest system, an impressive virtual assistant. To underline his point he posted a single word on X – "her" – referring to the 2013 film starring Joaquin Phoenix as a man who falls in love with a futuristic version of Siri or Alexa, voiced by Scarlett Johansson. For some experts, that new AI, GPT-4o, will be an unsettling reminder of their concerns about the technology's rapid advances, with a key OpenAI safety researcher leaving this week following a disagreement over the company's direction. For others the GPT-4o release will be confirmation that innovation continues in a field promising benefits for all. Next week's global AI summit in Seoul, attended by ministers, experts and tech executives, will hear both perspectives, as underlined by a safety report released before the meeting that referred to potential positives as well as numerous risks.

This research investigates distinct human-generative AI collaboration types and students' interaction experiences when collaborating with generative AI (i.e., ChatGPT) for problem-solving tasks and how these factors relate to students' sense of agency and perceived collaborative problem solving. By analyzing the surveys and reflections of 79 undergraduate students, we identified three human-generative AI collaboration types: even contribution, human leads, and AI leads. Notably, our study shows that 77.21% of students perceived they led or had even contributed to collaborative problem-solving when collaborating with ChatGPT. On the other hand, 15.19% of the human participants indicated that the collaborations were led by ChatGPT, indicating a potential tendency for students to rely on ChatGPT. Furthermore, 67.09% of students perceived their interaction experiences with ChatGPT to be positive or mixed. We also found a positive correlation between positive interaction experience and a sense of positive agency. The results of this study contribute to our understanding of the collaboration between students and generative AI and highlight the need to study further why some students let ChatGPT lead collaborative problem-solving and how to enhance their interaction experience through curriculum and technology design.

In the summer of 2023, OpenAI created a "Superalignment" team whose goal was to steer and control future AI systems that could be so powerful they could lead to human extinction. Less than a year later, that team is dead. OpenAI told Bloomberg that the company was "integrating the group more deeply across its research efforts to help the company achieve its safety goals." But a series of tweets from Jan Leike, one of the team's leaders who recently quit revealed internal tensions between the safety team and the larger company. In a statement posted on X on Friday, Leike said that the Superalignment team had been fighting for resources to get research done.