Science (Audio)

This series on artificial intelligence explores recent breakthroughs of AI, its broader societal implications and its future potential. In this presentation, Pamela Samuelson, professor of Law and Information at UC Berkeley, discusses whether computer-generated texts and images fall under the copyright law. She says that early on, the consensus was that AI was just a tool, like a camera, so humans could claim copyright in machine-generated outputs to which they made contributions. Now the consensus is that AI-generated texts and images are not copyrightable for the lack of a human author. The urgent questions today focus on whether ingesting in-copyright works as training data is copyright infringement and whether the outputs of AI programs are infringing derivative works of the ingested images. Four recent lawsuits, one involving GitHub’s Copilot and three involving Stable Diffusion, will address these issues.

Samuelson has been a member of the UC Berkeley School of Law faculty since 1996. She has written and spoken extensively about the challenges that new information technologies pose for traditional legal regimes, especially for intellectual property law. She is a member of the American Academy of Arts & Sciences, a fellow of the Association for Computing Machinery (ACM), a contributing editor of Communications of the ACM, a past fellow of the John D. & Catherine T. MacArthur Foundation, a member of the American Law Institute, and an honorary professor of the University of Amsterdam.

Series: "Data Science Channel" [Science] [Business] [Show ID: 38859]

ChatGPT is a remarkable language model, it does have certain technical limitations. Sometimes, it may give incorrect or nonsensical answers because it doesn't truly understand the meaning behind the words. It can also struggle with remembering information from earlier in the conversation, so you may need to repeat things. Another thing to be aware of is that ChatGPT learns from lots of text, which means it can sometimes reflect biases or prejudices present in that text. A panel of experts discusses the limitations when using ChatGPT for reliable information or advice. Series: "Data Science Channel" [Science] [Show ID: 38934]

While exercise helps us stay healthy, what is happening on the molecular level? Matthew A. Romero, Ph.D., shares his work to understand how muscle in general and other cells specifically are transcriptionally regulated by exercise and how this affects their general behavior and how this is impacted by diseases such as obesity.

Series: "Stem Cell Channel" [Health and Medicine] [Science] [Show ID: 39033]

This series on artificial intelligence explores recent breakthroughs of AI, its broader societal implications and its future potential. In this presentation, Sergey Levine, associate professor of Electrical Engineering and Computer Science at UC Berkeley, discusses AI reinforcement learning methods. Levine asks what it would take to create machine learning systems that can make decisions when faced with the full complexity and diversity of the real world, while still retaining the ability of reinforcement learning to come up with new solutions? He discusses how advances in offline reinforcement learning can enable machine learning systems to learn to make more optimal decisions from data, combining the best of data-driven machine learning with the capacity for emergent behavior and optimization provided by reinforcement learning.

Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as applications in other decision-making domains. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more.

Series: "Data Science Channel" [Science] [Show ID: 38857]

The COVID-19 pandemic ushered in a new era of how we interact and function in society. Our brains and behaviors experienced unprecedented upheavals, forcing us to adapt to new ways of living. In recent years, science has made bold advances in understanding how the brain and its wiring manage new situations and stress. Leading experts in cognitive science, neurobiology and psychology present perspectives on the brain and the fascinating ways it adapts to change and stress. Hear about what happens inside the brain during times of stress; how to self-regulate your brain and bodily states; as well as ideas on mindfulness, radical honesty and how to build emotional resilience. Series: "A Deep Look into the Future of Biology" [Health and Medicine] [Science] [Show ID: 38938]

The COVID-19 pandemic forced researchers and scientists to find ways to predict how the virus was spreading across the United States and around the World. Using computer models that take into account factors like population size, interactions, and disease characteristics. Duke University Statistical Science professor Jason Xu explains how data is now helping to predict epidemics. Series: "Data Science Channel" [Science] [Show ID: 38735]

This series on artificial intelligence explores recent breakthroughs of AI, its broader societal implications and its future potential. In this presentation, Stuart Russell, professor of computer science at the UC, Berkeley, discusses what AI is and how it could be beneficial to civilization. Russell is a leading researcher in artificial intelligence and the author, with Peter Norvig, of “Artificial Intelligence: A Modern Approach,” the standard text in the field. His latest book, “Human Compatible,” addresses the long-term impact of AI on humanity. He is also an honorary fellow of Wadham College at the University of Oxford. Series: "Data Science Channel" [Science] [Show ID: 38856]

As humans, we benefit immensely from the ecosystems around us — including the ocean — in obvious and not-so-obvious ways. As climate change continues to affect these ecosystems, we must ask ourselves — what can we gain by safeguarding them? Join Bernie Bastien and Raiza Pilatowsky in an interactive talk that explores the need to recognize what we value about nature in order to find new and inspiring ways to protect our planet, and ensure a sustainable future for generations to come. Series: "Jeffrey B. Graham Perspectives on Ocean Science Lecture Series" [Science] [Show ID: 38691]

This series on artificial intelligence explores recent breakthroughs of AI, its broader societal implications and its future potential. In this presentation, Michael Jordan, professor of Electrical Engineering and Computer Science and Statistics at UC Berkeley, discusses the how to connect research in economics with computer science and statistics, with a long-term goal of providing a broader conceptual foundation for emerging real-world AI systems, and to upend received wisdom in the computational, economic and inferential disciplines.

Jordan argues that AI has focused on a paradigm in which intelligence inheres in a single agent, and in which agents should be autonomous so they can exhibit intelligence independent of human intelligence. Thus, when AI systems are deployed in social contexts, the overall design is often naive. Such a paradigm need not be dominant. In a broader framing, agents are active and cooperative, and they wish to obtain value from participation in learning-based systems. Agents may supply data and resources to the system, only if it is in their interest. Critically, intelligence inheres as much in the system as it does in individual agents.

Jordan's research interests bridge the computational, statistical, cognitive, biological and social sciences. He is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences, and a foreign member of the Royal Society. He was a plenary lecturer at the International Congress of Mathematicians in 2018. He received the Ulf Grenander Prize from the American Mathematical Society in 2021, the IEEE John von Neumann Medal in 2020, the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize from the Cognitive Science Society in 2015 and the ACM/AAAI Allen Newell Award in 2009. Series: "Data Science Channel" [Science] [Business] [Show ID: 38858]

Responses are generated based on the patterns and information it has acquired during training. While ChatGPT lacks genuine understanding and operates based on statistical patterns rather than true comprehension, it has the ability to talk like a human. But, how does ChatGPT actually work? Halıcıoğlu Data Science Institute professor Jingbo Shang breaks down how the large language model and artificial intelligence actually works. Series: "Data Science Channel" [Science] [Show ID: 38931]