As politicians weigh proposals that call for steep cuts to the National Science Foundation (NSF) and the National Institutes of Health, we spoke with someone who can answer the question more precisely than most. The latest in a long-running National Academies series, it explores the many ways that research innovations become impactful commercial activities. The so-called "tire track report series" dates back to 1995, when the National Research Council's Computer Science and Telecommunications Board produced the report Evolving the High Performance Computing and Communications Initiative to Support the Nation's Information Infrastructure. Can you describe how that effort got started and how it has continued to evolve? Current times are fairly unique, but there has always been this myth in Congress: "Look at Silicon Valley, look at all these tech companies that got started in a garage. Why do we need to fund computer science research? This is a self-sufficient thing."

Nearly three months into President Donald Trump's term, the future of American AI leadership is in jeopardy. Basically any generative-AI product you have used or heard of--ChatGPT, Claude, AlphaFold, Sora--depends on academic work or was built by university-trained researchers in the industry, and frequently both. Today's AI boom is fueled by the use of specialized computer-graphics chips to run AI models--a technique pioneered by researchers at Stanford who received funding from the Department of Defense. They rely on a training method called "reinforcement learning," the foundations of which were developed with National Science Foundation (NSF) grants. "I don't think anybody would seriously claim that these [AI breakthroughs] could have been done if the research universities in the U.S. didn't exist at the same scale," Rayid Ghani, a machine-learning researcher at Carnegie Mellon University, told me.

Hardly a week passes without another billionaire endorsing Donald Trump. With Joe Biden proposing a 25% tax on those with assets over 100m ( 80m), this is no shock. The pro-Trump multimillionaire club now includes a growing number of venture capitalists. Unlike hedge funders or private equity barons, venture capitalists have traditionally held progressive credentials. They've styled themselves as the heroes of innovation, and the Democrats have done more to polish their progressive image than anyone else.

Advances in large language models (LLMs) have encouraged their adoption in the healthcare domain where vital clinical information is often contained in unstructured notes. Cancer staging status is available in clinical reports, but it requires natural language processing to extract the status from the unstructured text. With the advance in clinical-oriented LLMs, it is promising to extract such status without extensive efforts in training the algorithms. Prompting approaches of the pre-trained LLMs that elicit a model's reasoning process, such as chain-of-thought, may help to improve the trustworthiness of the generated responses. Using self-consistency further improves model performance, but often results in inconsistent generations across the multiple reasoning paths. In this study, we propose an ensemble reasoning approach with the aim of improving the consistency of the model generations. Using an open access clinical large language model to determine the pathologic cancer stage from real-world pathology reports, we show that the ensemble reasoning approach is able to improve both the consistency and performance of the LLM in determining cancer stage, thereby demonstrating the potential to use these models in clinical or other domains where reliability and trustworthiness are critical.

We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelX is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at https://github.com/pyt-team.

We explore the AI2050 "hard problems" that block the promise of AI and cause AI risks: (1) developing general capabilities of the systems; (2) assuring the performance of AI systems and their training processes; (3) aligning system goals with human goals; (4) enabling great applications of AI in real life; (5) addressing economic disruptions; (6) ensuring the participation of all; (7) at the same time ensuring socially responsible deployment; (8) addressing any geopolitical disruptions that AI causes; (9) promoting sound governance of the technology; and (10) managing the philosophical disruptions for humans living in the age of AI. For each problem, we outline the area, identify significant recent work, and suggest ways forward. [Note: this paper reviews literature through January 2023.]

Yiwen Lin, University of California, Irvine Lauren Snow, University of California, Irvine Acknowledgments: This work was partially supported by the National Science Foundation (Grant Number 1535300), and National Institutes of Health (Grant Number 5UC2NS128361-02). Abstract Collaboration is key to STEM, where multidisciplinary team research can solve complex problems. However, inequality in STEM fields hinders their full potential, due to persistent psychological barriers in underrepresented students' experience. This paper documents teamwork in STEM and explores the transformative potential of computational modeling and generative AI in promoting STEM-team diversity and inclusion. Leveraging generative AI, this paper outlines two primary areas for advancing diversity, equity, and inclusion. First, formalizing collaboration assessment with inclusive analytics can capture fine-grained learner behavior. Second, adaptive, personalized AI systems can support diversity and inclusion in STEM teams. Four policy recommendations highlight AI's capacity: formalized collaborative skill assessment, inclusive analytics, funding for socio-cognitive research, human-AI teaming for inclusion training.

My wife is a physician, so we often find ourselves in social engagements with other physicians. When asked what I do, many are intrigued by what it means to be a computer science professor. My graduate students do that now. Do you teach people how to program? No, though some of my colleagues do.

Climate change, clean energy and sustainability, building low emissions technologies and developing ethical artificial intelligence are some of the challenges being tackled by CSIRO, Australia's national science agency, and the United States National Science Foundation (NSF) under a multi-million-dollar partnership. The recently established partnership between the two leading science organisations is aiming to accelerate joint research and initiatives in areas of mutual priority between Australia and the United States. CSIRO Chief Executive Larry Marshall said the two leading science organisations have already enabled a number of opportunities across the two countries in only a year, launching this month an AUD$100 million Global Centers initiative, partnering in the areas of responsible and ethical Artificial Intelligence (AI) and developing sustainable materials for global challenges. "As national science agencies, CSIRO and the NSF are working together to build international bridges for national benefit, strengthening our science and innovation to improve lives around the world," Dr Marshall said. "As the world races towards new applications for technologies like AI, it will take global collaboration to champion responsible and ethical applications that embrace the full potential of technological advances and drive healthy competitive advantages.

Rewards play a crucial role in reinforcement learning (RL). A good choice of reward function motivates an agent to explore and learn which actions are valuable. The feedback that an agent receives via rewards allows them to update their behavior and learn useful policies. However, designing reward functions is complicated and cumbersome, even for domain experts. Automatically inferring a reward function is more desirable for end-users interacting with a system.