"Nate Silver's The Signal and the Noise is The Soul of …
Ultra-Processed Information: AI and the Coming Deluge of Noise | Frankly 128
On the edge: the art of risking everything
"From the New York Times bestselling author of The Signal and the Noise, the definitive guide to our era of risk-and the players raising the stakes In the bestselling The Signal and the Noise, Nate Silver showed how forecasting would define the age of Big Data. Now, in this timely and riveting new book, Silver investigates "The River," or those whose mastery of risk allows them to shape-and dominate-so much of modern life. These professional risk takers-poker players and hedge fund managers, crypto true-believers and blue-chip art collectors-can teach us much about navigating the uncertainty of the 21st century. By embedding within the worlds of Doyle Brunson, Peter Thiel, Sam Bankman-Fried, Sam Altman, and many others, Silver offers insight into a range of issues that affect us all, from the frontiers of finance to the future of AI. The River has increasing amounts of wealth and power in our society, and understanding their mindset-including the flaws in their thinking-is key to understanding what drives technology and the global economy today. There are certain commonalities in this otherwise diverse group: high tolerance for risk; appreciation of uncertainty; affinity for numbers; skill at de-coupling; self-reliance and a distrust of the conventional wisdom. For the River, complexity is baked in, and the work is how to navigate it, without going beyond the pale. Taking us behind-the-scenes from casinos to venture capital firms to the FTX inner sanctum to meetings of the effective altruism movement, On the Edge is a deeply-reported, all-access journey into a hidden world of powerbrokers and risk takers"--
Asking the wrong questions — Benedict Evans
With fundamental technology change, we don't so much get our predictions wrong as make predictions about the wrong things.
Dr. Émile P. Torres (they/them) on Twitter / X
What's amazing about this is every time there's a new grand prediction about AI, it's like people forget the 500 previous predictions from the same group of people that were completely wrong. AI by 2025 (Musk). AI doing most coding by 2025 (Amodei). Etc. Why is that?? https://t.co/t9CcMQO4EN— Dr. Émile P. Torres (they/them) (@xriskology) January 28, 2026
How accurate have Ed Zitron's AI skeptic predictions been?
Model Collapse Ends AI Hype
The Cosmos and the Model
Humboldt, the Romantics, and What AI Loses by Averaging
The Expert Trap and the Next War
what forecasting failure tells us about the future
Critical Signals
Critical Signals explores practical and visionary responses to an era of collapses.
Want signal? We need more noise (looking at the quiet bottleneck)
We need more signal, which means we want more noise. A lot of current scientific infrastructure is designed to minimize messiness: define a narrow question, collect the minimum data required to answer it, standardize the dataset, exclude complicating variables, finish the analysis, publish the result. That approach is understandable. It is also one reason we…
Noise in Cognition: Bug or Feature?
Noise in behavior is often considered a nuisance: Although the mind aims for the best possible action, it is let down by unreliability in the sensory and response systems. Researchers often represent noise as additive, Gaussian, and independent. Yet a careful look at behavioral noise reveals a rich structure that defies easy explanation. First, in both perceptual and preferential judgments sensory and response noise may potentially play only minor roles, with most noise arising in the cognitive computations. Second, the functional form of the noise is both non-Gaussian and nonindependent, with the distribution of noise being better characterized as heavy-tailed and as having substantial long-range autocorrelations. It is possible that this structure results from brains that are, for some reason, bedeviled by a fundamental design flaw, albeit one with intriguingly distinctive characteristics. Alternatively, noise might not be a bug but a feature. Specifically, we propose that the brain approximates probabilistic inference with a local sampling algorithm, one using randomness to drive its exploration of alternative hypotheses. Reframing cognition in this way explains the rich structure of noise and leads to the surprising conclusion that noise is not a symptom of cognitive malfunction but plays a central role in underpinning human intelligence.
Wisdom in a World in Crisis: The Counterintuitive Need to Slow Down and Find Spaciousness - The Great Simplification
In this episode, Nate is rejoined by philosopher and neuroscientist Iain McGilchrist for discussion on how our left-brain dominance obscures our sense of value, especially for abstract qualities such as truth, goodness, and beauty.
Illusions of Understanding in the Sciences
Scientists seek to understand the causes of observed phenomena. Beliefs that they have succeeded are based on understanding that is rarely or possibly never complete, and varies in depth and quality. Most often scientists believe they understand more than they do, making their belief an illusion. This illusion then persists in explanations scientists provide in print, in talks, or in discussions. The illusion that a scientist has a valid and complete explanation tends to be magnified when the data are well described by mathematical and computer simulation models due to the precision of such models and their ability to predict well; prediction does not imply causality, but gives the illusion that it does. The first part of this essay supports the case for the universality of partial and incomplete levels of understanding by showing the difficulty of reaching a deep level of understanding for even a simple analysis and model that most scientists use and believe they understand: linear regression. The second part highlights some implications of the existence of many levels of understanding and explanation, and their use by scientists for design, testing, analysis, and theory development. It discusses the way that deduction and induction depend on the levels of understanding and the implications of the illusion that a scientist’s understanding is deep. It makes a case that the many incomplete levels of understanding affect, often unwittingly, the ways scientists design experiments, test theories, comprehend, communicate, and teach.
Episode 5: Metaphors for AI, with Shannon Vallor
This week, Adam talks with Shannon Vallor, a philosopher of AI, about the metaphors we use to talk about AI and ourselves. They also dunk on libertarians and San Francisco billboards, and consider what kind of hope we can have for the future of technology.Messages: • Support the show at patreon.com/DreamingAgainstTheMachine • Follow us on Bluesky and Instagram • Subscribe to the show wherever you get your podcasts! • Check out other great podcasts from Multitude like Wow if TrueAbout the show:Dreaming Against the Machine is a podcast about envisioning a realistic and hopeful future. Each week, the show’s host, journalist and astrophysicist Dr. Adam Becker, will have an earnest (and entertaining!) conversation with a guest about possible futures, seen through the lenses of history, science, and culture. In a world where tech oligarchs and their power fantasies are driving visions of the future, Dreaming Against the Machine aims to take back the terms of the public conversation about what our world can and should be.
The Inversion Problem: Why Algorithms Should Infer Mental State and Not Just Predict Behavior
More and more machine learning is applied to human behavior. Increasingly these algorithms suffer from a hidden—but serious—problem. It arises because they often predict one thing while hoping for another. Take a recommender system: It predicts clicks but hopes to identify preferences. Or take an algorithm that automates a radiologist: It predicts in-the-moment diagnoses while hoping to identify their reflective judgments. Psychology shows us the gaps between the objectives of such prediction tasks and the goals we hope to achieve: People can click mindlessly; experts can get tired and make systematic errors. We argue such situations are ubiquitous and call them “inversion problems”: The real goal requires understanding a mental state that is not directly measured in behavioral data but must instead be inverted from the behavior. Identifying and solving these problems require new tools that draw on both behavioral and computational science.