S1 E2: Science, Superstar Scientists, and Innovation with Pierre Azoulay from MIT
Oct 16, 2023
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MIT professor Pierre Azoulay discusses the relationship between science and technology, the impact of superstar scientists, and policies for encouraging innovation. Topics include interdisciplinary vs. pluridisciplinary research, the production function of science, and whether science progresses one funeral at a time.
Science and technology have dynamic feedback loops, influencing each other's progress.
Superstar scientists impact both collaborators' productivity decline and non-collaborators' output increase.
Specialized training programs can effectively guide individuals towards research-focused pathways, fostering innovation.
Deep dives
Investigating the Relationship Between Science and Technology
Science and technology are distinct realms that should not be treated interchangeably, as highlighted in the podcast episode. The linear model often assumed where scientific progress leads directly to technological advancements is challenged. There are dynamic feedback loops between science and technology, with instances where technology inspires scientific developments. The interplay between scientific knowledge and technology can often be intertwined, leading to significant advancements, particularly evident in sectors like the biopharmaceutical industry.
Impact of Superstar Scientists on Collaborators and Non-Collaborators
The effects of superstar scientists on collaborators and non-collaborators were explored in the podcast. Collaborators of superstar scientists experienced a significant decline in productivity following the scientist's unexpected death. Conversely, non-collaborators thrived and increased their output. This nuanced evidence showcases the complex dynamics in scientific communities, where superstars not only elevate those around them but also impact the field's agenda.
Training Innovators for the Future
The podcast delved into the role of specialized training programs in shaping future innovators. The discussion focused on a program that provided intense research training to young physicians, leading to a notable shift towards research careers. The effectiveness of such programs in fostering innovation and guiding individuals towards research-focused pathways was highlighted, showcasing the long-term impact of targeted training on scientific progress.
Challenges and Insights in AI Research
The episode also touched upon current challenges and potential areas of exploration in AI research. The dominance of the deep learning paradigm, particularly in large language models, raised questions about the trade-offs associated with focused research efforts. Exploring alternative paradigms and understanding the implications of concentrated talent and resources in specific directions were key points of interest for future research endeavors in AI.
Identifying Research Questions and Effective Data Collection
The podcast discussed the value of identifying significant research questions and effective data collection strategies. The speakers shared insights on failures and lessons learned, emphasizing the importance of derisking projects early on and avoiding excessive data collection without clear variations to study. The focus on combining large administrative data sets with detailed individual data for in-depth analysis highlighted the role of meticulous data collection in impactful research endeavors.
In this episode, we dive into the production of science with Pierre Azoulay from MIT Sloan School of Management. We dig deeper the relationship between science and technology, how they feed into each other, the impact of superstar scientists, and policies to encourage innovation, and more! Email us at lsepoidcast@gmail.com OR X it out here: @POID_cast
Hosts: Ruveyda Gozen (@ruveyda_gozen) & John Van Reenen (@johnvanreenen)
03:45 Interdisciplinary vs Pluri-disciplinary Academic Research
06:18 How does science feed into technology and vice versa?
10:24 The production function of science
14:50 Does science progress one funeral at a time?
37:20 What is Pierre's production function of research?
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