Books are the original medium for communicating science to the masses. In a holiday special, producer Kunal Patel asks Babbage’s family of correspondents about the books that have inspired them in their careers as science journalists.
Host: Alok Jha, The Economist’s science and technology editor. Contributors: Rachel Dobbs, The Economist’s climate correspondent; Kenneth Cukier, our deputy executive editor; The Economist’s Emilie Steinmark; Geoff Carr, our senior editor for science and technology; and Abby Bertics, The Economist’s science correspondent. 
Reading list: “The Periodic Table” by Primo Levi; “When We Cease to Understand the World” by Benjamín Labatut; “A Theory of Everyone” by Michael Muthukrishna; “Madame Curie” by Ève Curie; “Sociobiology” by E. O. Wilson; “The Selfish Gene” by Richard Dawkins; “Why Fish Don't Exist” by Lulu Miller; and “How Far the Light Reaches” by Sabrina Imbler.
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Babbage: Science book club

Babbage is our weekly podcast on science and technology, named after Charles Babbage—a 19th-century polymath and grandfather of computing. Host Alok Jha talks to our correspondents about the innovations, discoveries and gadgetry shaping the world. Published every Wednesday.
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What is intelligence? In the middle of the 20th century, the inner workings of the human brain inspired computer scientists to build the first “thinking machines”. But how does human intelligence actually relate to the artificial kind?
This is the first episode in a four-part series on the evolution of modern generative AI. What were the scientific and technological developments that took the very first, clunky artificial neurons and ended up with the astonishingly powerful large language models that power apps such as ChatGPT?
Host: Alok Jha, The Economist’s science and technology editor. Contributors: Ainslie Johnstone, The Economist’s data journalist and science correspondent; Dawood Dassu and Steve Garratt of UK Biobank; Daniel Glaser, a neuroscientist at London’s Institute of Philosophy; Daniela Rus, director of MIT’s Computer Science and Artificial Intelligence Laboratory; Yoshua Bengio of the University of Montréal, who is known as one of the “godfathers” of modern AI.
On Thursday April 4th, we’re hosting a live event where we’ll answer as many of your questions on AI as possible, following this Babbage series. If you’re a subscriber, you can submit your question and find out more at economist.com/aievent. 
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Babbage: The science that built the AI revolution—part one

Dark matter is thought to make up around a quarter of the universe, but so far it has eluded detection by all scientific instruments. Scientists know it must exist because of the ways galaxies move and it also explains the large-scale structure of the modern universe. But no-one knows what dark matter actually is.
Scientists have been hunting for dark matter particles for decades, but have so far had no luck. At the annual meeting of the American Association for the Advancement of Science, held recently in Denver, a new generation of researchers presented their latest tools, techniques and ideas to step up the search for this mysterious substance. Will they finally detect the undetectable? 
Host: Alok Jha, The Economist’s science and technology editor. Contributors: Don Lincoln, senior scientist at Fermi National Accelerator Laboratory; Christopher Karwin, a fellow at NASA’s Goddard Space Flight Center; Josef Aschbacher, boss of the European Space Agency; Michael Murra of Columbia University; Jodi Cooley, executive director of SNOLAB; Deborah Pinna of University of Wisconsin and CERN.
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Babbage: The hunt for dark matter

OpenAI and Microsoft are leaders in generative artificial intelligence (AI). OpenAI has built GPT-4, one of the world’s most sophisticated large language models (LLMs) and Microsoft is injecting those algorithms into its products, from Word to Windows. 
At the World Economic Forum in Davos last week, Zanny Minton Beddoes, The Economist’s editor-in-chief, interviewed Sam Altman and Satya Nadella, who run OpenAI and Microsoft respectively. They explained their vision for humanity’s future with AI and addressed some thorny questions looming over the field, such as how AI that is better than humans at doing tasks might affect productivity and how to ensure that the technology doesn’t pose existential risks to society.
Host: Alok Jha, The Economist's science and technology editor. Contributors: Zanny Minton Beddoes, editor-in-chief of The Economist; Ludwig Siegele, The Economist’s senior editor, AI initiatives; Sam Altman, chief executive of OpenAI; Satya Nadella, chief executive of Microsoft. 
If you subscribe to The Economist, you can watch the full interview on our website or app. 
Essential listening, from our archive:
“Daniel Dennett on intelligence, both human and artificial”, December 27th 2023
“Fei-Fei Li on how to really think about the future of AI”, November 22nd 2023
“Mustafa Suleyman on how to prepare for the age of AI”, September 13th 2023
“Vint Cerf on how to wisely regulate AI”, July 5th 2023
“Is GPT-4 the dawn of true artificial intelligence?”, with Gary Marcus, March 22nd 2023
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Babbage: Sam Altman and Satya Nadella on their vision for AI

A year ago, the public launch of ChatGPT took the world by storm and it was followed by many more generative artificial intelligence tools, all with remarkable, human-like abilities. Fears over the existential risks posed by AI have dominated the global conversation around the technology ever since. 
Fei-Fei Li, a pioneer that helped lay the groundwork that underpins modern generative AI models, takes a more nuanced approach. She’s pushing for a human-centred way of dealing with AI—treating it as a tool to help enhance—and not replace—humanity, while focussing on the pressing challenges of disinformation, bias and job disruption.
Fei-Fei Li is the founding co-director of Stanford University’s Institute for Human-Centred Artificial Intelligence. Fei-Fei and her research group created ImageNet, a huge database of images that enabled computers scientists to build algorithms that were able to see and recognise objects in the real world. That endeavour also introduced the world to deep learning, a type of machine learning that is fundamental part of how large-language and image-creation models work.
Host: Alok Jha, The Economist’s science and technology editor. 
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Babbage: Science book club

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