Azeem Azhar's Exponential View

Azeem Azhar
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9 snips
Jan 10, 2024 • 34min

Managing AI’s Carbon Footprint

Sasha Luccioni, an AI researcher and climate lead at Hugging Face, joins Azeem Azhar to discuss the environmental impact of AI, including energy consumption and carbon emissions. They explore the challenges of categorizing AI's climate impact and the importance of setting standards for generative AI models. They also touch on the challenges of AI infrastructure, existential risks, and the distracted focus on AI risks.
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Dec 27, 2023 • 33min

AI Takes the Wheel: New Advances in Autonomous Driving

The podcast explores the shift towards AI integration in the automotive industry and subscription-based services. They discuss the role of 'embodied AI' in shaping everyday assistance beyond digital interactions. The challenges and breakthroughs of applying AI in complex environments like road traffic are also discussed.
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5 snips
Dec 20, 2023 • 28min

AI Is Transforming Businesses (with Andrew Ng)

AI pioneer and entrepreneur Andrew Ng joins Azeem Azhar to discuss the lessons organizations have learned about AI implementation, the potential of generative AI, and the need for regulation to support AI development.
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5 snips
Dec 13, 2023 • 33min

A.I. Is Driving an Information Revolution

The podcast discusses the challenges in AI research and product development, democratizing information access, the 'innovator's dilemma' for Google Search, conversational interfaces, and interests shaping AI regulation debate.
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Dec 6, 2023 • 46min

Understanding the Schism at OpenAI

The podcast delves into the ideological schism within OpenAI and its impact on AI safety and development. It explores the power dynamics in the tech industry and the potential future implications. The guest discusses real-world examples and tension surrounding the development of GPT-4. It also highlights the influence of cultural norms and the need for transparency and accountability in technology development.
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8 snips
Nov 29, 2023 • 36min

Azeem’s Picks: The Promise of AI with Fei-Fei Li

Fei-Fei Li, pioneering AI scientist and professor at Stanford, discusses the transformation of AI development from computer vision, rethinking human and machine value systems, and how the road to AGI can help us learn about human cognition.
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Nov 22, 2023 • 48min

Azeem’s Picks: Grading AI’s Hits and Misses

Murray Shanahan, veteran AI scientist at Imperial College London and principal scientist at DeepMind, discusses the importance of embodied interaction in AI progress, potential breakthroughs in the field, and how salary inflation for commercial AI engineers may hinder research.
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16 snips
Nov 15, 2023 • 32min

Azeem’s Picks: Creating AI Responsibly with Joanna Bryson

Leading expert on AI governance, Joanna Bryson, discusses the concept of intelligence and AI, the benefits and dangers of developing AI, and the challenge of explainability in AI systems.
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Nov 8, 2023 • 31min

Azeem’s Picks: AI, Accountability, and Power with Meredith Whittaker

Artificial Intelligence (AI) is on every business leader’s agenda. How do you ensure the AI systems you deploy are harmless and trustworthy? This month, Azeem picks some of his favorite conversations with leading AI safety experts to help you break through the noise. Today’s pick is Azeem’s conversation with Meredith Whittaker, president of the Signal Foundation. Meredith is a co-founder and chief advisor of the AI Now Institute, an independent research group looking at the social impact of artificial intelligence. They discuss: How discriminatory culture influences the values embedded in AI. Whether ethics officers can actually influence firms and their products. Why responsible AI design must include recognition of the shadow workforce helping to create it. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
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Nov 1, 2023 • 49min

Azeem’s Picks: How to Practice Responsible AI with Dr. Rumman Chowdhury

Dr. Rumman Chowdhury, a pioneer in applied algorithmic ethics, discusses assessing and diagnosing bias in 'black box' algorithms, top-down organizational change necessary for responsible AI, and the emerging field of 'Responsible Machine Learning Operations'.

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