In this week’s episode of “Waking Up With AI,” Katherine Forrest and Anna Gressel delve into different types of AI architectures, from Transformer models to “Mamba” — a type of architecture that’s making significant headway in 2024.

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In this week’s episode, Katherine Forrest and Anna Gressel unravel interpretability, an evolving field of AI safety research, while exploring its potential significance for regulators as well as its relation to concepts like explainability and transparency.

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In this episode of “Waking Up With AI,” Katherine Forrest and Anna Gressel discuss the brief history of open source software, while also exploring the complexities and implications of open source AI models.

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In this episode of "Waking Up With AI," Katherine Forrest and Anna Gressel discuss the complexities and implications of the EU AI Act coming into effect, outlining its broad territorial scope, risk categories, governance expectations, and phased compliance timeline.

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Katherine and Anna unravel the relationship between robotics and generative AI while highlighting some recent demonstrations that integrate these two dynamic fields.

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Katherine Forrest and Anna Gressel break down the concept of “Mixture of Experts,” an innovative AI technique that enhances efficiency and performance and powers some of today’s most advanced large language models.

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Katherine Forrest and Anna Gressel explore the concept of human-centered AI, discussing how AI systems can be designed with the fundamental goal of prioritizing human needs and enhancing our daily lives.

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In this week’s episode, Katherine Forrest introduces the infamous accuracy versus fairness problem in AI, while pointing to some recent research developments that could show promise in identifying bias in models.

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In this week’s episode, Katherine Forrest and Anna Gressel delve into the pressing issues surrounding responsible AI and the potential for its misuse, including the darker side of AI.

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In this week’s episode of “Waking Up With AI,” Katherine Forrest and Anna Gressel describe what it means for an AI system to be transparent, and how the shift from narrow AI to generative AI has made transparency harder.

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