#284 How Do We Measure Innovation? The Economic Analysis of the Impact of M&A on Innovation in the Pharmaceutical Industry

Our Curious Amalgam

1:14:43

Artificial intelligence is evolving at an unprecedented pace—what does that mean for the future of technology, venture capital, business, and even our understanding of ourselves? Award-winning journalist and writer Anil Ananthaswamy joins us for our latest episode to discuss his latest book Why Machines Learn: The Elegant Math Behind Modern AI . Anil helps us explore the journey and many breakthroughs that have propelled machine learning from simple perceptrons to the sophisticated algorithms shaping today’s AI revolution, powering GPT and other models. The discussion aims to demystify some of the underlying mathematical concepts that power modern machine learning, to help everyone grasp this technology impacting our lives–even if your last math class was in high school. Anil walks us through the power of scaling laws, the shift from training to inference optimization, and the debate among AI’s pioneers about the road to AGI—should we be concerned, or are we still missing key pieces of the puzzle? The conversation also delves into AI’s philosophical implications—could understanding how machines learn help us better understand ourselves? And what challenges remain before AI systems can truly operate with agency? If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform. Sign up for our newsletter at techsurgepodcast.com for exclusive insights and updates on upcoming TechSurge Live Summits. Links: Read Why Machines Learn, Anil’s latest book on the math behind AI https://www.amazon.com/Why-Machines-Learn-Elegant-Behind/dp/0593185749 Learn more about Anil Ananthaswamy’s work and writing https://anilananthaswamy.com/ Watch Anil Ananthaswamy’s TED Talk on AI and intelligence https://www.ted.com/speakers/anil_ananthaswamy Discover the MIT Knight Science Journalism Fellowship that shaped Anil’s AI research https://ksj.mit.edu/ Understand the Perceptron, the foundation of neural networks https://en.wikipedia.org/wiki/Perceptron Read about the Perceptron Convergence Theorem and its significance https://www.nature.com/articles/323533a0…

год назад 23:27

MP3•Главная эпизода

When analyzing the competitive effects of a proposed transaction, the U.S. antitrust agencies may attempt to define and assess the potential loss of innovation in a relevant market. But what exactly is "innovation," and how can potential losses (or gains) in innovation be measured? In this episode Alicia Downey and Lijun Zhang speak with Cornerstone Research economist Penka Kovacheva about how economists are attempting to measure the impact of mergers on innovation, with a focus on the pharmaceutical industry. Listen to this episode to learn about whether any of these attempts have been successful and why there is much more work to be done.

With special guest:

Penka Kovacheva, Vice President, Cornerstone Research

#284 How Do We Measure Innovation? The Economic Analysis of the Impact of M&A on Innovation in the Pharmaceutical Industry

Our Curious Amalgam

0-10 subscribers

published год назад

MP3•Главная эпизода

With special guest:

Penka Kovacheva, Vice President, Cornerstone Research

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