Many people expect improvements in technology over the next few years, but fewer people are optimistic about improvements in the economy. Especially in Europe, there’s a narrative that productivity has stalled, that the welfare state is over-stretched, and that the regions of the world where innovation will be rewarded are the US and China – although there are lots of disagreements about which of these two countries will gain the upper hand.

To discuss these topics, our guest in this episode is Carl Benedikt Frey, the Dieter Schwarz Associate Professor of AI & Work at the Oxford Internet Institute. Carl is also a Fellow at Mansfield College, University of Oxford, and is Director of the Future of Work Programme and Oxford Martin Citi Fellow at the Oxford Martin School.

Carl’s new book has the ominous title, “How Progress Ends”. The subtitle is “Technology, Innovation, and the Fate of Nations”. A central premise of the book is that our ability to think clearly about the possibilities for progress and stagnation today is enhanced by looking backward at the rise and fall of nations around the globe over the past thousand years. The book contains fascinating analyses of how countries at various times made significant progress, and at other times stagnated. The book also considers what we might deduce about the possible futures of different economies worldwide.

Selected follow-ups:

• Professor Carl-Benedikt Frey - Oxford Martin School
• How Progress Ends: Technology, Innovation, and the Fate of Nations - Princeton University Press
• Stop Acting Like This Is Normal - Ezra Klein ("Stop Funding Trump’s Takeover")
• OpenAI o3 Breakthrough High Score on ARC-AGI-Pub
• A Human Amateur Beat a Top Go-Playing AI Using a Simple Trick - Vice
• The future of employment: How susceptible are jobs to computerisation? - Carl Benedikt Frey and Michael A. Osborne
• Europe's Choice: Policies for Growth and Resilience - Alfred Kammer, IMF
• MIT Radiation Laboratory ("Rad Lab")

Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration

Our guest in this episode is Noel Hurley. Noel is a highly experienced technology strategist with a long career at the cutting edge of computing. He spent two decade-long stints at Arm, the semiconductor company whose processor designs power hundreds of billions of devices worldwide.

Today, he’s a co-founder of Literal Labs, where he’s developing Tsetlin Machines. Named after Michael Tsetlin, a Soviet mathematician, these are a kind of machine learning model that are energy-efficient, flexible, and surprisingly effective at solving complex problems - without the opacity or computational overhead of large neural networks.

AI has long had two main camps, or tribes. One camp works with neural networks, including Large Language Models. Neural networks are brilliant at pattern matching, and can be compared to human instinct, or fast thinking, to use Daniel Kahneman´s terminology. Neural nets have been dominant since the first Big Bang in AI in 2012, when Geoff Hinton and others demonstrated the foundations for deep learning.

For decades before the 2012 Big Bang, the predominant form of AI was symbolic AI, also known as Good Old Fashioned AI. This can be compared to logical reasoning, or slow learning in Kahneman´s terminology.

Tsetlin Machines have characteristics of both neural networks and symbolic AI. They are rule-based learning systems built from simple automata, not from neurons or weights. But their learning mechanism is statistical and adaptive, more like machine learning than traditional symbolic AI.

Selected follow-ups:

• Noel Hurley - Literal Labs
• A New Generation of Artificial Intelligence - Literal Labs
• Michael Tsetlin - Wikipedia
• Thinking, Fast and Slow - book by Daniel Kahneman
• 54x faster, 52x less energy - MLPerf Inference metrics
• Introducing the Model Context Protocol (MCP) - Anthropic
• Pioneering Safe, Efficient AI - Conscium
• Smartphones and Beyond - a personal history of Psion and Symbian
• The Official History of Arm - Arm
• Interview with Sir Robin Saxby - IT Archive
• How Spotify came to be worth billions - BBC

Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration

Could the future see the emergence and adoption of a new field of engineering called nucleonics, in which the energy of nuclear fusion is accessed at relatively low temperatures, producing abundant clean safe energy? This kind of idea has been discussed since 1989, when the claims of cold fusion first received media attention. It is often assumed that the field quickly reached a dead-end, and that the only scientists who continue to study it are cranks. However, as we’ll hear in this episode, there may be good reasons to keep an open mind about a number of anomalous but promising results.

Our guest is Jonah Messinger, who is a Winton Scholar and Ph.D. student at the Cavendish Laboratory of Physics at the University of Cambridge. Jonah is also a Research Affiliate at MIT, a Senior Energy Analyst at the Breakthrough Institute, and previously he was a Visiting Scientist and ThinkSwiss Scholar at ETH Zürich. His work has appeared in research journals, on the John Oliver show, and in publications of Columbia University. He earned his Master’s in Energy and Bachelor’s in Physics from the University of Illinois at Urbana-Champaign, where he was named to its Senior 100 Honorary.

Selected follow-ups:

• Jonah Messinger (The Breakthrough Institute)
• nucleonics.org
• U.S. Department of Energy Announces $10 Million in Funding to Projects Studying Low-Energy Nuclear Reactions (ARPA-E)
• How Anomalous Science Breaks Through - by Jonah Messinger
• Wolfgang Pauli (Wikiquote)
• Cold fusion: A case study for scientific behavior (Understanding Science)
• Calculated fusion rates in isotopic hydrogen molecules - by SE Koonin & M Nauenberg
• Known mechanisms that increase nuclear fusion rates in the solid state - by Florian Metzler et al
• Introduction to superradiance (Cold Fusion Blog)
• Peter L. Hagelstein - Professor at MIT
• Models for nuclear fusion in the solid state - by Peter Hagelstein et al
• Risk and Scientific Reputation: Lessons from Cold Fusion - by Huw Price
• Katalin Karikó (Wikipedia)
• “Abundance” and Its Insights for Policymakers - by Hadley Brown
• Identifying intellectual dark matter - by Florian Metzler and Jonah Messinger

Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration