Harvard Research
The Myth of Machine Unlearning: The Complexities of AI Data Removal
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 Harvard University; Christopher A. Choquette-Choo, Research Scientist at […]
Mastering Efficiency in AI Training: Insights from Critical Batch Size Research
As businesses increasingly adopt large-scale AI models, optimizing training efficiency is crucial. In “How Does Critical Batch Size Scale in Pre-training?”, Hanlin Zhang and a group of colleagues (see below for author details) explore critical batch size (CBS)—the threshold at which data parallelism, which distributes training data across multiple processors, stops yielding significant returns from […]
Curated Insights | Mark Nunnelly
Introduction Explore the latest insights from Harvard Business School’s research emphasizing lucrative long-term investment potentials in disruptive AI, digitization and innovative scientific pursuits, underpinning opportunities like Moderna’s triumphant COVID-19 vaccine development. Insights The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications By: Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott […]
