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Artificial Intelligence Arts Arts, Technology and society Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Data Electrical engineering and computer science (EECS) Ethics Law machine learning MIT Schwarzman College of Computing Research School of Engineering Technology and policy Technology and society

When AI art has no author: Study finds generated images often can’t be traced to training data

A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.

Artificial Intelligence Arts Arts, Technology and society Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Data Electrical engineering and computer science (EECS) Ethics Law machine learning MIT Schwarzman College of Computing Research School of Engineering Technology and policy Technology and society

When AI art has no author: Study finds generated images often can’t be traced to training data

A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.

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Toward a future that preserves benefits of neurotechnology for all

PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.

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Improving understanding with language

MIT senior Olivia Honeycutt investigates how the ways we communicate can shape our views of the world.

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Study: Transparency is often lacking in datasets used to train large language models

Researchers developed an easy-to-use tool that enables an AI practitioner to find data that suits the purpose of their model, which could improve accuracy and reduce bias.