What's Your Problem?

Catherine Nakalembe is an assistant professor at the University of Maryland and the head of the Africa program at NASA Harvest, which uses satellite images to track agriculture around the world. Catherine’s problem is this: How do you predict when a famine is coming – and how do you convince leaders to act in time?
In this episode, Catherine explains:
How satellite data can predict crop failure months in advance
Why phots can convince decision-makers faster than maps or statistics
Why imaging systems developed for US farmland often don’t work in Africa
Why a half-page memo moves food trucks faster than a scientific paper
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When Dr. Lara Jehi began treating epilepsy patients in the 2000s, critical surgical decisions were driven more by clinician intuition and expertise than data. Today, she is a leader of IBM and Cleveland Clinic’s Discovery Accelerator, using advanced AI and quantum computing to transform how researchers analyze data, simulate molecules, accelerate drug discovery, and develop more precise treatments. Malcolm Gladwell talks with Dr. Jehi about how quantum computing is changing biomedical research, and what these breakthroughs could mean for the future of healthcare and life sciences.

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Nir Barzilai is a physician and a researcher at the Albert Einstein College of Medicine in New York, where he runs the Institute for Geroscience. Nir’s problem is this: How can we use lifestyle changes and drugs to slow the process of aging?
In this episode, Nir explains:
The search for biological markers of aging
The existing drugs that might reduce the risk of diseases associated with aging
The four key lifestyle factors that every researcher in the field agrees on
What he learned by studying the genes of people who lived to 100
What “geroscience” means, and why it doesn’t have a Wikipedia page
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Email us at problem@pushkin.fm
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Helen Pearson is the author of the book “Beyond Belief: How Evidence Shows What Really Works.” Helen's problem is this: How do you get people to make important decisions based on high-quality evidence, instead of on hunches or habits?
In this episode, Helen explains:
How a doctor's fatal mistake sparked the evidence-based medicine movement
Why management is so resistant to evidence-based practices
How systematic reviews changed the way she works as a science journalist
Why global trust in science remains high – outside the US
What a randomized trial of pint glasses revealed about preventing violent injuries.
Check out Helen’s book here.
Connect with us:
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In 2016, Geoffrey Hinton, one of the inventors of modern AI, predicted that AI would replace radiologists within years. Ten years later, we have a shortage of radiologists. AI has yet to revolutionize the field. What happened, and what does the story of AI and radiology tell us about AI and the future of work?
Saurabh Jha is a radiologist at the University of Pennsylvania. He's written about how he uses AI in his practice, and why radiologists have been slow to adopt AI. Saurabh has also traveled to India and Nepal to understand the very different ways AI is being adopted for radiology in the developing world.
In this episode, Saurabh explains:
Why AI still hasn't made radiologists faster or more productive
The risks of novice radiologists using AI versus the benefits to experts
Why a successful AI model would need to be nearly perfect
How AI is being used to spot tuberculosis in India
Why diagnosing a normal scan is harder than diagnosing disease
Connect with us:
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Listen to Jacob’s other show, Business History
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