Harvard University has been a main target of the Trump administration’s attempt to leverage federal funding in order to crack down on antisemitism and “woke" ideology.

This extended abstract introduces Self-Explaining Contrastive Evidence Re-Ranking (CER), a novel method that restructures retrieval around factual evidence by fine-tuning embeddings with contrastive learning and generating token-level attribution rationales for each retrieved passage. Hard negatives are automatically selected using a subjectivity-based criterion, forcing the model to pull factual rationales closer while pushing subjective or misleading explanations apart. As a result, the method creates an embedding space explicitly aligned with evidential reasoning. We evaluated our method on clinical trial reports, and initial experimental results show that CER improves retrieval accuracy, mitigates the potential for hallucinations in RAG systems, and provides transparent, evidence-based retrieval that enhances reliability, especially in safety-critical domains.

Small- and medium-sized manufacturers need innovative data tools but, because of competition and privacy concerns, often do not want to share their proprietary data with researchers who might be interested in helping. This paper introduces a privacy-preserving platform by which manufacturers may safely share their data with researchers through secure methods, so that those researchers then create innovative tools to solve the manufacturers' real-world problems, and then provide tools that execute solutions back onto the platform for others to use with privacy and confidentiality guarantees. We illustrate this problem through a particular use case which addresses an important problem in the large-scale manufacturing of food crystals, which is that quality control relies on image analysis tools. Previous to our research, food crystals in the images were manually counted, which required substantial and time-consuming human efforts, but we have developed and deployed a crystal analysis tool which makes this process both more rapid and accurate. The tool enables automatic characterization of the crystal size distribution and numbers from microscope images while the natural imperfections from the sample preparation are automatically removed; a machine learning model to count high resolution translucent crystals and agglomeration of crystals was also developed to aid in these efforts. The resulting algorithm was then packaged for real-world use on the factory floor via a web-based app secured through the originating privacy-preserving platform, allowing manufacturers to use it while keeping their proprietary data secure. After demonstrating this full process, future directions are also explored.

"I teach computer science, and that is all," wrote Boaz Barak, of Harvard University, in a recent op-ed in The New York Times.a The main point of the op-ed was to protest the growing politicization of U.S. higher education, especially at elite universities, where we have seen many faculty members proceed from scholarship to advocacy. But in spite of the provocative title, the content of Barak's op-ed is quite more nuanced. "We should not normalize bringing one's ideology to the classroom," wrote Barak, and I could not agree more. But he also wrote that "The interaction of computer science and policy sometimes arises in my classes, and I make sure to present multiple perspectives." Here, Barak is advocating fairness and balance, rather than neutrality and avoidance of non-technical topics.

Advanced artificial intelligence models score well on professional medical exams but still flunk one of the most crucial physician tasks: talking with patients to gather relevant medical information and deliver an accurate diagnosis. "While large language models show impressive results on multiple-choice tests, their accuracy drops significantly in dynamic conversations," says Pranav Rajpurkar at Harvard University. That became evident when researchers developed a method for evaluating a clinical AI model's reasoning capabilities based on simulated doctor-patient conversations. The "patients" were based on 2000 medical cases primarily drawn from professional US medical board exams. "Simulating patient interactions enables the evaluation of medical history-taking skills, a critical component of clinical practice that cannot be assessed using case vignettes," says Shreya Johri, also at Harvard University.

A panel joins'Fox News @ Night' to weigh in on a voter sentiment poll about the incoming Trump administration, Chinese President Xi Jinping's invitation to the presidential inauguration, and efforts by Trump Cabinet nominees to court senators. President-elect Donald Trump unleashed a slew of nominations on Sunday night, naming several new people to serve in his forthcoming administration. In several Truth Social posts on Sunday, Trump introduced various experts to work in the White House on issues ranging from defense to technology to budgeting. The Republican leader began by naming Stephen Alexander Vaden as his nominee for deputy secretary of the Department of Agriculture. "In my First Term, Stephen was the General Counsel of the Department of Agriculture, and a Member of the Board of the Commodity Credit Corporation, where he won two cases before the United States Supreme Court, relocated and reorganized the Agencies that comprise the Department to better serve Rural America, and engaged in substantial regulatory reform," Trump wrote in a post.

Artificial Intelligence is rapidly embedding itself within militaries, economies, and societies, reshaping their very foundations. Given the depth and breadth of its consequences, it has never been more pressing to understand how to ensure that AI systems are safe, ethical, and have a positive societal impact. This book aims to provide a comprehensive approach to understanding AI risk. Our primary goals include consolidating fragmented knowledge on AI risk, increasing the precision of core ideas, and reducing barriers to entry by making content simpler and more comprehensible. The book has been designed to be accessible to readers from diverse backgrounds. You do not need to have studied AI, philosophy, or other such topics. The content is skimmable and somewhat modular, so that you can choose which chapters to read. We introduce mathematical formulas in a few places to specify claims more precisely, but readers should be able to understand the main points without these.

We investigate the behavior of the Nadaraya-Watson kernel smoothing estimator in high dimensions using its relationship to the random energy model and to dense associative memories.

ChatGPT's release a year ago triggered a wave of panic among educators. Now, universities are in the midst of college application season, concerned that students might use the artificial intelligence tool to forge admissions essays. But is a chatbot-created essay good enough to fool college admissions counselors? To find out, The Washington Post asked a prompt engineer -- an expert at directing AI chatbots -- to create college essays using ChatGPT. The chatbot produced two essays: one responding to a question from the Common Application, which thousands of colleges use for admissions, and one answering a prompt used solely for applicants to Harvard University.