Hugin & Munin Journal

The 2026 Nobel Prize in Physiology or Medicine was awarded to Karl Deisseroth, Peter Hegemann and Georg Nagel “for their discoveries concerning light-gated ion channels and optogenetics.” The technique rests on a remarkable proposition: give selected neurons a light-sensitive protein borrowed from microorganisms, illuminate them with the right wavelength, and their electrical activity can be controlled with millisecond precision. This is optogenetics . Its mechanism is ingenious, but the reason it changed neuroscience is more interesting than the mechanism itself. A neuron may become active whenever an animal feels fear, remembers a place or seeks food, but observation alone cannot tell us exactly what that activity means. The neurons might generate the behaviour, respond to it, or participate in some larger process that causes both. Optogenetics gave systems neuroscience a much more precise way to test those possibilities by selectively activating or inhibiting candidate cells and ...

Editorial image generated by the author. On 11 September 2026, twenty-five Fields Medallists were the initial signatories to an unusually blunt warning about artificial intelligence and mathematics. In A Severe Misalignment of AI in Mathematics , they argued that using famous mathematical problems as AI benchmarks risks confusing what mathematics can easily measure with what mathematics actually values. The concern is not that AI systems cannot solve difficult problems. It is almost the opposite. Historically, solving a famous problem usually indicated that somebody had developed new ideas, techniques or understanding along the way. The solution was valuable partly because of the mathematical structure created around it. If machines can mass-produce solutions without those results being absorbed into the intellectual structure of the field, that old relationship breaks down. We can accumulate proofs without necessarily accumulating much understanding. Much of this c...

Recent AI systems have begun producing results that look less like polished imitation and more like genuine discovery. In mathematics, specialised systems have found new constructions or solved difficult problems by combining learned search, synthetic examples and formal evaluation. Generative models can also produce images, programs and arguments that did not occur in their training data in precisely the same form. This makes concern about limited human-generated training data sound almost contradictory. If a model can create novelty, why can it not manufacture the examples it needs and become increasingly self-sustaining? The answer is not that synthetic data is inherently inferior, or that only humans can produce useful information. A learning system needs some source of correction that is independent of its current approximation. That correction may come from people, but it may also come from a proof checker, a compiler, a game, an experiment, a sensor or a physical process tha...

Much of the public conversation about artificial intelligence has settled into a familiar set of disputes: jobs disappearing, artists losing income, contested training data and the possibility that machines may eventually surpass human intelligence. Those concerns are real. Scientific usefulness does not settle questions about consent, ownership, employment or who controls the technology. It does, however, leave out an important part of what is already happening in laboratories and research institutes. AI is increasingly being used not merely to automate known procedures, but to search spaces of possibility too large for human intuition to inspect unaided. It can rank molecules, predict structures, generate mathematical constructions and identify patterns across datasets that no individual researcher could examine directly. This makes AI a new kind of scientific instrument. Microscopes and telescopes extend perception; AI more often extends search. It can show researchers where t...