Renewable Energy and Energy Transition Investor at Schroders Greencoat. Husband, father, former physicist and sports addict.

London
Joined December 2008
James Samworth retweeted
Here is the full talk:
Had fun writing this talk for our MPhil students today: "How to avoid AI Brain Rot" Discussing how to avoid intelligence atrophy from AI use in work and study. And how to stand out on an ever-narrowing bell curve
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James Samworth retweeted
Working with AI models, I've arrived at what I consider the most significant pure-theory breakthrough of my research (in theoretical neuroscience) so far: arxiv.org/abs/2610.12426 Chaotic systems show what is colloquially called the butterfly effect, namely, a tiny nudge to the initial state grows exponentially in time. How fast the nudge grows depends on its direction. Each direction has its own growth rate, called a Lyapunov exponent, and the full set of rates is the Lyapunov spectrum. The Lyapunov spectrum is a rich (and diffeomorphism-invariant) characterization of chaos, telling us how many directions are chaotic, how many dimensions the attractor occupies, and how fast the dynamics generate information. In 1988, Sompolinsky, Crisanti, and Sommers (Physical Review Letters) introduced a nonlinear recurrent-network model of N randomly coupled neurons that becomes chaotic when the coupling strength exceeds a critical value. This network has since become a cornerstone of theoretical neuroscience, both as a model of the spontaneous activity of cortex and as the starting point for training recurrent networks on tasks. I have worked on this model for much of my research. In the same paper, the authors obtained the largest Lyapunov exponent from the ground-state energy of a Schrödinger equation and named the full Lyapunov spectrum as an open problem. In 2023, beautiful work by Engelken et al. (Physical Review Research) computed the full Lyapunov spectrum numerically. However, an analytical calculation of the full Lyapunov spectrum has remained out of reach. Four decades after the problem was posed, this paper derives the full Lyapunov spectrum at large N from a simple self-consistent single-site problem. This calculation has three steps. First, we lower every exponent by a constant s, which turns the question "how many exponents lie below s?" into the question "how many tangent directions decay?" We show that, if we drive the shifted tangent dynamics with a source and take the minimum-norm solution, the one-step response is the projector onto the decaying directions. Thus, the trace of this projector counts the decaying directions. This step is exact at finite N. Second, to work with the minimum-norm solution, we cast it as the vanishing-regularization limit of the solution of a least-squares problem. This regularized solution in turn solves a dynamical system with one field running forward in time and another running backward. Third, we apply the cavity method, that is, we add one neuron to the network, along with its forward and backward fields. The effect of the rest of the network on this neuron then reduces to the classical dynamical mean-field description of the activity of this neuron, plus two kernels that couple the forward and backward fields of this neuron to each other across time. Thus, the full Lyapunov spectrum follows from a single-site problem, which boils down to the self-consistent calculation of these kernels. The resulting theory agrees closely with simulations for the Lyapunov spectrum, the attractor dimension, and the entropy rate, and recovers known results, including the Schrödinger equation of Sompolinsky, Crisanti, and Sommers for the largest Lyapunov exponent. As a byproduct, the theory establishes analytically that the chaos is extensive, i.e., the attractor dimension and the entropy rate grow in proportion to N. I had assumed that this problem was analytically intractable. After working independently for 100 minutes with Max thinking, GPT-6 Astra produced the initial derivation, yielding a self-consistent problem whose solution matched simulations of large networks. I found this derivation intriguing, albeit complicated and somewhat mysterious (see AI Methodology in the paper). After investigating the structure of the proposed solution more deeply, together with AI models, I arrived at a physically transparent derivation. I am excited to apply this approach to other network models, trained networks, and, more broadly, to high-dimensional systems to which Lyapunov analysis is relevant, including AI models themselves. Of course, as with any paper, I took care to write up the paper clearly. A few broader thoughts... Neural circuits are high-dimensional nonlinear dynamical systems, and such systems are notoriously hard to understand. I would frame the central problem of theoretical neuroscience as understanding how such systems function, process information, learn, and generate behavior. Many people in the theoretical sciences are anxious about AI. However, in my field at least, we are nowhere close to fully understanding the high-dimensional computations unfolding in our brains. With AI, we could reach a level of understanding that so far we have only gestured toward. So, I am excited.
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If confirmed, this is telling: Russian data centres get hit, and online activity around AfD suddenly plummets. 📉 So much talk about German patriotism… then the connection from Russia goes down. 🤔🇷🇺
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Nothing says" I've got my life together" like 1,000 lbs of salmon-fueled confidence.
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James Samworth retweeted
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
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James Samworth retweeted
We are focusing on understanding this model, and using what we learn to help us guide and pace how we pursue further advances in capability. Our goal is to build AI systems which are steerable, accountable, and connected to people, which may require more deliberate choices about the pace of progress, as we continue our mission to ensure AGI benefits all of humanity. openai.com/index/navier-stok…
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I wanted to speak - for the first time in detail - about NARCISSISTIC CONTRARIANISM. This is a trait Trump has shown with greater frequency in recent days. It's led some analysts to surmise that Trump is acting as if he wants to lose the midterms. Here's what it REALLY is: A narcissist contrarian is someone who takes the opposite side of obvious or even popular issues, opposing prevailing views. They do this reflexively, because they are feeling enfeebled or attacked. When the story of the excessive time at sea by military personnel aboard the USS Lincoln was driving public ridicule, Trump characterized their time at sea as "not long enough." Why would he possibly say that? When a bad inflation report broke, Trump was quoted as saying "I love inflation." Really? He's said the same thing recently about "data centers," a sensitive topic across the political spectrum. And any time he is accused of too much focus on the WH ballroom, a broadly unpopular issue, his response is to speak glowingly and lavishly about it even more. The key thing to realize here is that a narcissistic contrarian doesn't oppose prevailing views because they've thought something through. On the "contrary." They do it because disagreement serves their battered ego, signals grandiosity through them knowing better, and attracts attention, which is a narcissist's nutrition. Psychologists speak to 8 core traits leading a narcissist to become excessively contrarian which are: 1. A grandiose self-image that has taken a battering. 2. An innate resistance to compromise. 3. Binary thinking - if one is right, the other is wrong. 4. Deep insecurity masked by professing superiority. 5. The need for self-preservation - to appear smarter - now, to reverse the trend where they appear old or ill. 6. Deep feelings of innate distrust - assuming bad intent even where none exists; paranoia. 7. The need to re-exert autonomy - show they can and will do what they want, to dominate a news cycle. 8. The need to prevail in a big current power struggle. Let me help you go through that list: CHECK, CHECK, CHECK, CHECK, CHECK, CHECK, CHECK, and CHECK. So - to summarize - when Trump takes a position or makes a statement that goes against conventional wisdom, it's not only because he's ignorant, stubborn, or an idiot - it's because he's losing, desperate, and narcissistically damaged - and suggesting or adopting a contrary viewpoint is his way of trying to cope, reverse bad trends, and nurse his narcissistic injury.
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James Samworth retweeted
VO2 max drops 46% from age 20 to 70, but cardiac output only falls 31%. The gap between those numbers reveals something unexpected about where aging really happens. For decades, the dominant explanation for declining aerobic capacity with age centered on the cardiovascular system. Your heart beats slower, pumps less blood per beat, and delivers less oxygen to working muscles. This narrative made intuitive sense and aligned with the most visible changes of aging. But new research quantifies a different story. The limitation isn't just about oxygen delivery. It's increasingly about what happens after the blood arrives at muscle tissue. VO2 max represents the maximum rate your body can consume oxygen during all-out exercise. It's one of the strongest predictors of longevity and functional independence as we age. Beginning around age 30, it drops roughly 10% per decade in sedentary people. The Fick equation breaks down what determines VO2 max: VO2 max = Cardiac Output × Arteriovenous Oxygen Difference Cardiac output is how much blood your heart pumps per minute. The arteriovenous oxygen difference, or a-vO2 difference, measures how much oxygen your muscles extract from that blood. This equation splits the aging story into two parts: central factors (heart and lungs delivering oxygen) and peripheral factors (muscles extracting and using oxygen). Central limitation comes from declining maximal heart rate, which drops about one beat per minute per year after age 20, and reduced stroke volume as the heart stiffens and large arteries lose elasticity. Peripheral limitation comes from changes in skeletal muscle itself: mitochondrial density and function, capillary networks that deliver blood to muscle fibers, muscle mass, and the enzymes that drive oxidative metabolism. The new analysis reveals that by late middle age, nearly half of the limitation on VO2 max originates in the periphery, not the cardiovascular system. In younger adults, approximately 77% of the total limitation is central and 23% peripheral. Your heart and oxygen delivery system are the primary bottleneck. In older adults, that shifts to roughly 56% central and 44% peripheral. The muscles are catching up to the heart as a source of failure. The most striking finding: oxygen extraction capacity falls dramatically with age. Skeletal muscle extracts roughly 80% of delivered oxygen at maximal effort in young adults. By ages 75 to 80, that figure drops to approximately 60%. That's a 20 percentage point decline in the muscle's ability to use the oxygen the cardiovascular system is still delivering. Four converging biological processes drive this peripheral decline: 1. Sarcopenia preferentially strips away type II muscle fibers, which are rich in mitochondria and oxidative capacity. You lose not just muscle mass, but specifically the muscle tissue most capable of using oxygen. 2. Mitochondrial density and efficiency decline. Mitochondria become fewer in number, smaller in size, and less effective at converting oxygen into usable energy through ATP production. 3. Capillary rarefaction thins the networks of small blood vessels threading through muscle tissue. Fewer capillaries mean longer distances for oxygen to diffuse from blood to muscle cells, creating a bottleneck at the delivery endpoint. 4. Interstitial changes in the space between capillaries and muscle cells further impair oxygen movement. Changes in tissue structure and composition slow the final step of oxygen's journey into working muscle. None of these is catastrophic alone. Together, they compound into something substantial that previous research underemphasized by focusing primarily on cardiac decline. Here's what matters for intervention: • The peripheral factors driving VO2 max decline (mitochondria, capillaries, oxidative enzymes) remain responsive to training well into later decades of life • Endurance training produces the most reliable capillary growth: 13.3% increase in capillary density and 15% increase in capillary-to-fiber ratio over 8-10 weeks • High-intensity interval training (HIIT) delivers comparable mitochondrial gains to endurance training but requires substantially less total time, estimated at 1.7x more time-efficient • Sprint interval training (SIT) generates the fastest mitochondrial signal per minute of any modality, producing 3-5x greater VO2 max improvement per hour of exercise, though it doesn't significantly increase capillary density • Meaningful adaptation begins within two weeks. Roughly 13.7% of total mitochondrial gains from an endurance block occur in the first 14 days • The biological machinery that aging erodes is the same machinery that training can partially rebuild, even in the seventh and eighth decades of life The practical implication: maintaining VO2 max with age isn't just about keeping your cardiovascular system fit. You need training that specifically targets peripheral adaptations, the cellular machinery inside muscle tissue that extracts and uses oxygen. That means prioritizing exercise intensity that creates metabolic stress sufficient to trigger mitochondrial remodeling and capillary growth. Moderate-intensity steady-state exercise improves cardiovascular fitness but generates weaker peripheral adaptation signals than high-intensity work. The framework also explains why training becomes more important, not less, as you age. When you're 25 and 77% of your VO2 max limitation is central, peripheral decline happens quietly in the background. By 65, when peripheral factors account for nearly half the limitation, those quiet changes have accumulated into the primary driver of functional decline. The decisions made in the fourth and fifth decades of life shape the physiological ceiling of the seventh and eighth. Aerobic aging is a slow, cumulative process affecting multiple systems simultaneously. So is the adaptive response to training. The most important variable isn't the perfect training modality. It's consistency across the decades during which the oxygen cascade is quietly remodeling in one direction or the other.
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Top 10 Spreaders of Disinformation in the US according to ChatGPT: 1. Donald Trump 2. Fox News 3. Elon Musk 4. Robert F. Kennedy 5. Alex Jones 6. Tucker Carlson 7. Sean Hannity 8. Newsmax 9. The Gateway Pundit 10. Breitbart
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A selection of articles published by The Spectator between August 01 and August 14 2026:
BREAKING: Jason Arday, the former University of Cambridge professor, has been found dead at an address in Battersea, according to reports. A spokesperson for the Metropolitcan Police said the 41-year-old's death is not being treated as suspicious. “Sadly, a 41-year-old man was pronounced dead at the scene. His next-of-kin have been informed and are being supported by officers," he said. “At this time his death is being treated as unexpected, but is not believed to be suspicious. "The case is being investigated by officers from the Met’s Central South Command Unit. A file will be prepared for the coroner.” More follows...
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Amazing sight. An All Black legend returning the challenge. xitter.cf/bencoles_/status/20872…
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James Samworth retweeted
We asked an unreleased research version of Claude to take a stab at the Riemann hypothesis. It didn’t solve it, but it did make strides on a related problem: it increased the lower bound for the fraction of zeros of the Riemann zeta function that satisfy the hypothesis from 41.6% to 67.2%. anthropic.com/research/riema…
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James Samworth retweeted
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That’s false: the scientific community has investigated every single fake assertion from Kennedy and the antivaccine activists: 1) starting in 1998 with their false assertion about MMR, 2) then Kennedy wrote that ridiculous article in Rolling Stone in 2005 also retracted, 3) then Jenny McCarthy and Jim Carey paraded around in green t shirts saying we have to “green” our vaccine ecosystem and separate vaccines, all BS, 4) then it was aluminum also debunked. Meanwhile DHHS refuses to acknowledge the role of several hundred autism genes and also refuses to investigate actual known environmental toxins that interact with autism genes like valproate. This is all to benefit the corrupt wellness influencer industry, it’s all about their filthy lucre
.@SecKennedy to @DanaBashCNN: “None of the eight vaccines that are administered during the first six months of life have ever been studied for a relationship to autism."
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James Samworth retweeted
Pro-Trump states and counties had far higher COVID death rates. Misinformation and disinformation were to blame. Trump and Musk were the biggest propagators of misinformation in terms of reach.
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James Samworth retweeted
Under a Binface government, anyone who doesn’t accept human-made climate change as fact will be sent back to school, from Year 1 right the way to A Levels, to see if anything goes in the second time round. #VoteBinface
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James Samworth retweeted
Cette photo mérite absolument tous les prix. Elle a même sa place au Louvre. Elle relate précisément l’inertie de l’individu face à l’effondrement. Regardez la composition. Un chef-d’œuvre involontaire. Au premier plan, le sable doré, les tongs abandonnées, la glacière. Le décor ordinaire du bonheur estival. Deux personnages assis, de dos, comme dans un tableau de Caspar David Friedrich. Sauf que Friedrich peignait des hommes contemplant le sublime. Ici, ils contemplent la catastrophe. Et ils ne bougent pas. Au centre, le parasol. Publicitaire, criard, dérisoire. Un pastis face à l’apocalypse. C’est presque trop parfait : la France qui prend l’apéro pendant que le monde brûle. Aucun scénariste n’aurait osé. Et puis le ciel. Les deux tiers de l’image. Cette muraille de fumée qui avale la lumière, ce dégradé d’ocre et de noir digne d’un Turner. La nature qui reprend la palette des maîtres pour peindre notre propre fin. La femme photographie l’incendie avec son téléphone. Mise en abyme totale : nous documentons notre disparition au lieu de l’empêcher. Cette image dit tout. Le déni, le confort, l’habitude. Nous sommes tous sur cette plage. Nous voyons tous le nuage. Et nous restons assis, parce que l’eau est bonne et qu’on a posé nos congés. Les historiens de l’art parleront un jour de cette photo comme on parle du Radeau de la Méduse. Sauf que cette fois, le naufrage, c’est nous qui le regardons depuis la plage. 💔🌍
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Great to see this finally coming out! Well done Richard.
近刊 Richard J. Samworth & Rajen D. Shah Modern Statistical Methods and Theory --An Introduction to Nonparametric and High-Dimensional Statistics-- 電子版はOpen Accessらしい cambridge.org/core/books/mod…
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