Babbage from The Economist

Geoffrey Hinton is one of the “godfathers” of artificial intelligence, critical in the development of deep learning, backpropagation and much more. In 2024 he was awarded the Nobel prize in physics in recognition of his immense contributions to the field of computer science. Not bad for someone who started his career with the aim of understanding the human brain. Despite his role in its creation, though, Professor Hinton has been surprised by the rapid development of the technology. He’s now convinced that artificial neural networks can think, reason and understand the world in a way that could eventually be superior to our own brains.Professor Hinton joins Alok Jha, The Economist’s science and technology editor, to discuss why he thinks artificial intelligence is much more human than it seems. For more on this topic, check out our series on the science that built the AI revolution. We’d also recommend the most recent episode of The Weekend Intelligence, which investigated the role of the human data-labellers who made deep learning possible.Transcripts of our podcasts are available via economist.com/podcasts.Listen to what matters most, from global politics and business to science and technology—subscribe to Economist Podcasts+.For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account.

In 2012 scientists at the Large Hadron Collider (LHC) at CERN in Geneva found the Higgs boson. Things have been quiet since then on the “epic discovery” front—but that doesn’t mean the thousands of physicists working there have been idle. The collider is undergoing a years-long upgrade to make it even more powerful, so that it can probe even deeper into the fabric of our reality. When the LHC is eventually reborn as the “High-Luminosity LHC” by the end of the decade, it will begin a new chapter of discovery. We speak to the incoming boss of CERN to find out if the machine will finally lift the veil on the “new physics” that scientists have been searching for for decades.Alok Jha, The Economist’s science and technology editor, travels to the LHC at CERN in Geneva, where he meets the next director-general Mark Thomson, plus many of the scientists and engineers who are working on the LHC’s big upgrade. Transcripts of our podcasts are available via economist.com/podcasts.Listen to what matters most, from global politics and business to science and technology—subscribe to Economist Podcasts+.For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account.

The world was shocked in 2018 when He Jiankui, a Chinese biophysicist, announced that he had helped to produce two girls whose genetic code he had edited when they had been embryos. His aim had been to tweak a gene that sometimes confers protection against HIV infections. No one had ever used the CRISPR gene-editing tool in reproduction before and it was completely untested in embryos. Scientists around the world condemned the work as wildly premature and possibly dangerous—the Chinese authorities agreed and Dr He was imprisoned for three years. Now, more than six years later, Dr He is back. And he still wants to prevent medical conditions by editing human embryos. But will the world ever be ready for this use of gene editing? Or will newer methods of editing human genes prove more promising?Host: Alok Jha, The Economist’s science and technology editor. Contributors: Emilie Steinmark, science correspondent at The Economist; Chinese researcher He Jiankui; Henry (Hank) Greely of the Stanford Centre for Biomedical Ethics; Panicos Shangaris of King's College Hospital.Transcripts of our podcasts are available via economist.com/podcasts.Listen to what matters most, from global politics and business to science and technology—subscribe to Economist Podcasts+.For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account.

Translation is tricky business—not least when your subject belongs to a different species. But as evidence mounts that many animals are capable of rich, complex communication, scientists are trying to bridge the inter-species gap. Already, artificial intelligence has proved a valuable tool. But one ambitious technologist is trying to take these models even further. Could his new initiative one day allow humans to speak to their fellow animals? And what else might people learn in the process?Host: Kenneth Cukier, The Economist’s deputy executive editor. Contributors: Denise Herzing, founder of the Wild Dolphin Project; Aza Raskin, co-founder of the Earth Species Project, and The Economist’s Abby Bertics.Transcripts of our podcasts are available via economist.com/podcasts.Listen to what matters most, from global politics and business to science and technology—subscribe to Economist Podcasts+.For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account.

Talking to animals has long been a human fantasy. But what is the nature of animal communication—and how does chirping and barking differ from human language? This is the first episode in a two-part series about animal communication and whether it could be translated in the age of AI. We meet a researcher who is leading the largest animal communication study ever attempted, and we ask whether language is a cognitive ability that’s unique to humans, or just one of many modes of communication dotted across the tree of life. Host: Kenneth Cukier, The Economist’s deputy executive editor. Contributors: Robert Berwick of the Massachusetts Institute of Technology; Federico Rossano of the University of California, San Diego; and The Economist’s Abby Bertics.Transcripts of our podcasts are available via economist.com/podcasts.Listen to what matters most, from global politics and business to science and technology—subscribe to Economist Podcasts+.For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account.

A sophisticated, predatory, multi-billion dollar industry is emerging from the shadows. It already rivals the size of the illicit drug trade. And it’s about to get bigger and much more powerful. The Economist’s Sue-Lin Wong follows a trail that starts with the collapse of a bank in rural Kansas to uncover a global, underground scam economy built around human trafficking, corruption and money laundering. Can it be stopped?Available now.To listen to the full series subscribe to Economist Podcasts+.

The launch of R1, an AI model by the Chinese startup DeepSeek, recently sent shockwaves through the technology world. R1 is a “reasoning” model—the most cutting-edge type of large language model (LLM)—and it performs about as well as the best-in-class Western models but for a fraction of the training cost. Like other LLMs, though, it still lacks many of the skills and types of intelligence that human brains achieve. For one, “reasoning” models still have a very limited understanding of the physical world in which they exist. Our guest today wants to get beyond these hurdles. Yann LeCun, chief AI scientist at Meta and a professor at New York University, thinks LLMs are not the answer if we want truly useful personal assistants, humanoid robots and driverless cars in the future. For machine intelligence to get more interactive with the real world, he is fundamentally rethinking how AI models are built and trained.This week, along with six other pioneers of machine learning, Professor LeCun was awarded the Queen Elizabeth Prize for Engineering. He joins Alok Jha, The Economist’s science and technology editor.For more on this topic, check out our series on the science that built the AI revolution, as well as our episodes on artificial general intelligence.Transcripts of our podcasts are available via economist.com/podcasts.Listen to what matters most, from global politics and business to science and technology—subscribe to Economist Podcasts+.For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account.

Data has transformed sport in recent decades—from identifying the best place to shoot from in basketball and football, to helping recruit the perfect baseball player. The new age of AI, which can utilise vast amounts of data on players, promises even deeper insights. Teams are experimenting with AI tools that can help pick the best players and prepare the best tactics for individual matches. Perhaps one day these models may even be able to predict injuries. AI models could transform sport—and the experiments with games could also inform the future of AI itself.Host: Alok Jha, The Economist’s science and technology editor. Contributors: The Economist’s Abby Bertics; James Tozer of Prospect Sporting Insights; Patrick Lucey of Stats Perform; Petar Veličković of Google DeepMind.Transcripts of our podcasts are available via economist.com/podcasts.Listen to what matters most, from global politics and business to science and technology—subscribe to Economist Podcasts+.For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account.

Since the 1980s, the average life expectancy of a person with Down’s syndrome has more than doubled, from less than 30 to well into their 60s. Living longer, though, has revealed a tragic twist—the vast majority of people with Down’s will go on to develop Alzheimer’s disease by the age of 65. That’s because people with Down's syndrome, who have an extra copy of chromosome 21, produce increased amounts of beta-amyloid, a protein that is implicated as a possible cause of Alzheimer’s. To make things worse, people with Down’s syndrome often cannot access the dementia drugs that might help them—doctors are reluctant to prescribe them because people with Down’s haven't been allowed to take part in the clinical trials for Alzheimer’s drugs. People with Down’s think that should change. In doing so, could scientists unlock the mystery of Alzheimer’s itself?Alok Jha, The Economist’s science and technology editor and Emilie Steinmark, our science correspondent, follow the story of Kate Olmstead, who has Down’s syndrome, and her mother, Amy. Emilie also interviews John Hardy, a neuroscientist at University College London who proposed the amyloid hypothesis for Alzheimer’s in the 1990s. Thanks also to Frank Stephens of the Global Down Syndrome Foundation.Transcripts of our podcasts are available via economist.com/podcasts.Listen to what matters most, from global politics and business to science and technology—subscribe to Economist Podcasts+.For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account.