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Copyright Versus AI Part 1: The Legal Battle w/Pamela Samuelson

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Conteúdo fornecido por Max Bodach and Foundation for American Innovation. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por Max Bodach and Foundation for American Innovation ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.

Copyright law and artificial intelligence are on a collision course, with major implications for the future of AI development, research, and innovation. In this first episode of The Dynamist's four-part series exploring AI and copyright, we're joined by Professor Pamela Samuelson of Berkeley Law, a pioneering scholar in intellectual property law and a leading voice on copyright in the digital age. FAI Senior Fellow Tim Hwang guest hosts.

The conversation covers the wave of recent lawsuits against AI companies, including The New York Times suit against OpenAI and litigation facing Anthropic, NVIDIA, Microsoft, and others. These cases center on two key issues: the legality of using copyrighted materials as training data and the potential for AI models to reproduce copyrighted content. Professor Samuelson breaks down the complex legal landscape, explaining how different types of media (books, music, software) might fare differently under copyright law due to industry structure and existing precedent.

Drawing on historical parallels from photocopying to the Betamax case, Professor Samuelson provides crucial context for understanding today's AI copyright battles. She discusses how courts have historically balanced innovation with copyright protection, and what that might mean for AI's future. With several major decisions expected in the coming months, including potential summary judgments, these cases could reshape the AI landscape - particularly for startups and research institutions that lack the resources of major tech companies.

  continue reading

103 episódios

Artwork
iconCompartilhar
 
Manage episode 460068609 series 3530279
Conteúdo fornecido por Max Bodach and Foundation for American Innovation. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por Max Bodach and Foundation for American Innovation ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.

Copyright law and artificial intelligence are on a collision course, with major implications for the future of AI development, research, and innovation. In this first episode of The Dynamist's four-part series exploring AI and copyright, we're joined by Professor Pamela Samuelson of Berkeley Law, a pioneering scholar in intellectual property law and a leading voice on copyright in the digital age. FAI Senior Fellow Tim Hwang guest hosts.

The conversation covers the wave of recent lawsuits against AI companies, including The New York Times suit against OpenAI and litigation facing Anthropic, NVIDIA, Microsoft, and others. These cases center on two key issues: the legality of using copyrighted materials as training data and the potential for AI models to reproduce copyrighted content. Professor Samuelson breaks down the complex legal landscape, explaining how different types of media (books, music, software) might fare differently under copyright law due to industry structure and existing precedent.

Drawing on historical parallels from photocopying to the Betamax case, Professor Samuelson provides crucial context for understanding today's AI copyright battles. She discusses how courts have historically balanced innovation with copyright protection, and what that might mean for AI's future. With several major decisions expected in the coming months, including potential summary judgments, these cases could reshape the AI landscape - particularly for startups and research institutions that lack the resources of major tech companies.

  continue reading

103 episódios

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