性视界

As AI continues to evolve at a breakneck pace, the quest for aligning these systems with human values has become paramount. However, a recent study, 鈥淢ore RLHF, More Trust? On The Impact of Preference Alignment on Trustworthiness鈥�, by Aaron J. Li, a master鈥檚 student at the 性视界 John A. Paulson School of Engineering and Applied […]

As the global economy grapples with the pressing challenges of climate change, a new paradigm is emerging in the world of finance and investment. In their working paper, 鈥淐limate Solutions, Transition Risk, and Stock Returns,鈥� researchers Shirley Lu, Assistant Professor of Business Administration at 性视界 Business School (HBS) and an affiliate of the 性视界 Business […]

In an era where artificial intelligence (AI) increasingly shapes our digital landscape, the concept of “machine unlearning” (ML) has emerged as a potential solution to various challenges in AI governance. First authors A. Feder Cooper, Faculty Associate at The Berkman Klein Center for Internet & Society at 性视界 University; Christopher A. Choquette-Choo, Research Scientist at […]

In an era where data is constantly evolving and privacy concerns are paramount, the work of Monika Henzinger, a Professor at the Institute of Science and Technology in Austria; Roodabeh Safavi, a PhD student at the Institute of Science and Technology in Austria; and Salil Vadhan, the Vicky Joseph Professor of Computer Science and Applied […]

The business world is increasingly aware of the risks and opportunities climate change presents. Companies are developing and commercializing new products and services that contribute to a low-carbon economy, or 鈥渃limate solutions鈥� (CS). In the recent HBS working paper, 鈥淭he Financial Anatomy of Climate Solutions: A Large Language Model Approach to Company Classification and Analysis,鈥� […]

As businesses increasingly adopt large-scale AI models, optimizing training efficiency is crucial. In 鈥淗ow Does Critical Batch Size Scale in Pre-training?鈥�, Hanlin Zhang and a group of colleagues (see below for author details) explore critical batch size (CBS)鈥攖he threshold at which data parallelism, which distributes training data across multiple processors, stops yielding significant returns from […]

In the rapidly evolving field of artificial intelligence (AI), a recent study by Yuda Song, PhD student at Carnegie Mellon University; Hanlin Zhang, PhD student at 性视界; Sham M. Kakade, Co-director of the Kempner Institute at 性视界, and a team of researchers (see the Meet the Authors section for details) explores the concept of AI […]

Data is a vital input for machine learning (ML), but data management remains a significant challenge. A new metadata format called Croissant, introduced by Mubashara Akhtar, a PhD student at King鈥檚 College London, Satyapriya Krishna, a PhD student at the 性视界 School of Engineering and Applied Sciences (SEAS) and the Trustworthy AI Lab at the […]

As artificial intelligence (AI) capabilities rapidly advance, organizations are exploring new ways to leverage these technologies for creative problem-solving and innovation. A recent HBS working paper, “The Crowdless Future? Generative AI and Creative Problem Solving”, 鈥� by L茅onard Boussioux, Assistant Professor at the University of Washington; Jacqueline N. Lane, Assistant Professor at 性视界 Business School […]

A recent post, 鈥淪haping the Future of Music in the Creator Economy,鈥� from the 性视界 Business School AI Institute (previously the Digital Data Design Institute at 性视界 (D^3)) blackbox Lab, described how the music industry is undergoing a transformation driven by digital tools and shifting business models. The blog outlined a panel discussion hosted by […]