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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 […]

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