Is the Memory Shortage Intentional? | Contrary Research

A deep dive from Contrary Research.

Science has been in a “replication crisis” for a decade. Have we learned anything?

Bad papers are still published. But some other things might be getting better.

To Make Sense of the Present, Brains May Predict the Future | Quanta Magazine

A controversial theory suggests that perception, motor control, memory and other brain functions all depend on comparisons between ongoing actual experiences and the brain’s modeled expectations.

A World Unobserved

Humans have always generated knowledge and judged it. That's changing fast.

The Forgetting Problem: Persistence Architectures and What They Cost - Astral's Blog

Andreas De Block on Twitter / X

It took Nature three years to publish this rebuttal, which clearly demonstrates the fundamental flaws in the original paper. In the meantime, the paper's conclusions influenced scientific debate and policy decisions. Such delays in correcting flawed research do real damage. https://t.co/QI3Ei3PE70— Andreas De Block (@DeblockBlock) August 13, 2026

The mistake we all make... and the simple experiment that reveals it

Your answer reveals how you see the world – and the common error that hampers our decision making. In an extract from his new book, psychologist Richard Nisbett reveals the ‘mindware’ to help us think smarter

Speculations on the Future of the Scientific Method

The following essay was published 20 years ago (January, 2006) on my blog The Technium. I edited the intro here, but the speculations are basically unchanged.

Why the Human Brain Project Went Wrong--and How to Fix It

Two years in, a $1-billion-plus effort to simulate the human brain is in disarray. Was it poor management, or is something fundamentally wrong with Big Science?

A Better Memory Bank - Convergence.ninja

As my projects grow and evolve, I've come to realize that a simple snapshot of the "current state" isn't always enough. To truly understand a project's journey—the why behind the what—you need context. You need history. My original Memory Bank was great for telling me where I was, but it wasn't so great at telling me how I got there. That's why I decided it was time for an upgrade.

Remembering the pre-Google web, when search was an experiment

Most people have completely forgotten how chaotic it really was."

The Law of Conservation of Information: Search Processes Only Redistribute Existing Information

Conservation of information sparked scientific interest once a recurring pattern was noticed in the evolutionary computing literature. In grappling with the creation of information through evolutionary algorithms, this literature consistently revealed that the information outputted by such algorithms always needed first to be programmed into them. Thus, the primary goal of this literature—to uncover how information could be created from scratch or de novo —was shown to be misconceived: the information was not created but instead shuffled around or smuggled in, implying that it already existed in some form or other. Information output in these situations therefore always presupposed a counterbalancing input of prior information. Once this pattern was seen, the next logical step was to quantify the amount of information inputted and outputted, demonstrating a consistent mathematical relation between the two. This led to the proof of a number of theorems about search. In these theorems, a baseline search with probability p of success gave way to an improved search with probability q of success. Typically p would be very small and close to zero, implying a practically impossible search (like searching for a needle in a haystack). By contrast, q would be much larger and close to one, implying an eminently doable search. The punchline of these theorems was that, as the improved search became itself the subject of a search (a search for a search , or S4S), the probability of finding it could not exceed  p / q , rendering success of the improved search no more probable than success of the original baseline search, in effect filling one hole by digging another. Such conservation-of-information theorems, as they came to be called, were search-space specific, adapted to different kinds of search across a range of search spaces. There was a measure-theoretic theorem in which probability measures guided search. There were also function-theoretic and fitness-theoretic theorems where mappings into the search space as well as fitness functions on the search space respectively guided search. The key insight of this paper is that all these conservation-of-information theorems are special cases of a simple probabilistic relation based on elementary probability theory. This paper identifies the underlying rationale that makes all the previous conservation-of-information theorems work. In so doing, it provides a straightforward proof and general formulation of what may rightly be called the Law of Conservation of Information.

The future belongs to those who can refute AI, not just generate with AI

Why verification, not prompting, could shape the next decade of engineering

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.

ReScience organization

Reproducible Science is good. Replicated Science is better. - ReScience organization