Face Masks Required

The Umwelt Representation Hypothesis: Rethinking Universality

Bosch, Victoria, Sommers, Rowan P., Doerig, Adrien, Kietzmann, Tim C., The Umwelt Representation Hypothesis: rethinking Universality, Trends in Cognitive Sciences, Aug. 8 ,2026, doi: 10.1016/j.tics.2026.07.004

Highlights: Artificial neural networks are increasingly used to study neural representations.

Recent studies have found a surprising degree of representational alignment between artificial neural networks and biological brains. In addition, broad alignment among ANNs of different modalities and architectures has been observed.

These findings have been interpreted as evidence for Universality: the idea that sufficiently capable systems all converge on a single shared representation of reality.

This interpretation has large implications for model comparisons that require meaningful differences between artificial neural networks: if all artificial neural networks eventually align with the brain, then creating new model classes leads to no new insights.

This opinion article argues that this inference is too strong and proposes the Umwelt Representation Hypothesis: representational alignment emerges from overlap in ecological constraints, not convergence to one privileged world model.

Abstract: Artificial neural networks are increasingly used to study neural representations.Recent studies have found a surprising degree of representational alignment between artificial neural networks and biological brains. In addition, broad alignment among ANNs of different modalities and architectures has been observed.These findings have been interpreted as evidence for Universality: the idea that sufficiently capable systems all converge on a single shared representation of reality.This interpretation has large implications for model comparisons that require meaningful differences between artificial neural networks: if all artificial neural networks eventually align with the brain, then creating new model classes leads to no new insights.This opinion article argues that this inference is too strong and proposes the Umwelt Representation Hypothesis: representational alignment emerges from overlap in ecological constraints, not convergence to one privileged world model.

AIs are not very good at long horizon tasks

This paper is a brutal reality check for long-horizon AI. Give an agent a year of interconnected decisions, delayed feedback, and consequences from its own past actions, and its performance collapses relative to humans.

The researchers tested eight leading models, including… https://t.co/rGBfuPZEtv pic.twitter.com/6CPvVIQCQX

— Rohan Paul (@rohanpaul_ai) August 28, 2026

Friday Fotos: Newport Mall

Emergent Multiscale Organisation of Neural Dynamics

Milinkovic, B., Seth, A.K., Barnett, L., Carter, O., & Andrillon, T. (2026). Emergent Multiscale Organisation of Neural Dynamics Fragments in Anaesthesia. Imaging Neuroscience, Advance Publication. https://doi.org/10.1162/IMAG.a.1364

Abstract: Conscious experience depends on the coordinated activity of neural processes that span multiple scales: from synapses to whole-brain dynamics. A recently introduced measure, dynamical independence (DI), identifies, characterises, and quantifies these multi-scale relationships using an information-theoretic dimensionality reduction approach. Here, we use DI to examine changes in the emergent dynamical organisation in the human brain under three pharmacologically-distinct anaesthetic interventions (propofol, xenon, ketamine). Applied to source-reconstructed electroencephalography (EEG), our analysis reveals that propofol and xenon, anaesthetics that abolish conscious report, exhibit more emergent but highly variable dynamic structure, indicating fragmented macroscopic dynamical organisation. Ketamine, which preserves dream-like phenomenology, shows a different pattern relative to wakefulness: reduced overall emergence yet a partial preservation of the macroscopic structure. Further exploratory analyses revealed spatially localised source-level contributions to emergent dynamical structure, highlighting regional variations. Together, our results highlight drug-induced reconfigurations of emergent dynamical structure relative to wakefulness, dissociate the amount of emergence from the organisation of emergent dynamics, and caution against equating emergence with level of consciousness. Consequently, we suggest that wakeful conscious processing depends not only on integration between neural components within a single scale, but also on integration across scales, broadening currently held assumptions of putative signatures of consciousness.

Dan Everett on Joe Rogan

The sun blazes in, and then weeds

Fuse Powder BOOM! How California technocults created opportunties for capital seeking investment vehicles

David Hoyt, AI is the Latest and Best-Financed Cult to Emerge from California, 3 Quarks Daily, Aug. 23, 2026.

The article begins by discussing the cultish behavior surrounding AI in Silicon Valley; I discussed similar material in a 3QD article from 2022, On the Cult of AI Doom. The article pivots toward finances with this paragraph:

What makes the cult of AI different in tone and in worldview is its origin within a tech industry which sits at an epicenter of unregulated flows of global capital, its profound, longstanding, and growing links to the US security state, and the certainty that on the basis of these pillars it can build the future it wants here on earth right now, and that no one should be able to stop them.

Then we have:

Two macroeconomic factors are at play in the breakneck speed with which AI infrastructure has been financed and built out since the release of Chat GPT in November of 2022. They have little to do with the intrinsic commercial potential of AI-related products or services, nor any particular innovation in computer engineering. The first, to put it simply, is the availability of lots of money. More than ever before, giant pots of money are sloshing around the planet, motivated by historically low interest rates over a relatively long period to search out the Next Big Thing. This money is mostly exempt from national regimes of control or taxation, is managed outside the traditional banking system, and is therefore more free to move around. The second is the chronic and by now widely acknowledged slow-down of economic growth in the advanced economies, dating back nearly half a century. What the hype typically fails to register is that investors don’t seek out technology and AI firms because they are the best bets on future returns – they seek them out because they are the only bets around.

To understand just why this is, Nick Srnicek provides a useful capsule history of the sequence of financial crises that have followed the liberalization of capital since the later 20th century. It is a story that leads with a crescendo to the current boom in AI development. In the mid 1990’s, the hottest global market was not centered in Silicon Valley, but in the small economies of East and Southeast Asia, the “Asian Tigers.” Only when these blew up beginning in 1997 did newly mobile capital, recently freed from decades of local restrictions on its movement in and out of national economies, begin to pour into Silicon Valley in what would become known as the dot-com bubble. When this blew up, as all bubbles do, it led to a decade of low interest rates dictated by the Federal Reserve. This, in turn, led global investors to flood the US housing market, inflating a nearly decade-long bubble in real estate prices. When this bubble blew up in 2008 with even more severity, followed almost immediately by the Greek/European debt crisis of 2009-2012, the solvency of major financial institutions in the US and Europe and of the entire economic order was threatened. Only massive intervention from the public sector avoided a meltdown. At the same time, the year 2012 marked the year that growth rates in the People’s Republic of China dropped under 10% annually for the first time in over a decade. This twin shock to the global economy, in combination with austerity measures put in place in the aftermath of the 2008 crisis, led to the unraveling of the global economic architecture put in place over the previous quarter century.

This is the context of chronic low growth in which money has been pouring into AI, either invested in privately held firms such as Anthropic or OpenAI, or publically held tech firms listed in the US such as Google and Microsoft, or in overseas firms such as semiconductor giant SK Hynix in South Korea. The search for returns continues to drive valuations of AI majors to astronomical heights, bringing significant portions of the market along with them. In May, 2026 Anthropic, which only recently turned a modest profit on sales of its AI assistant chatbot Claude, came close to breaking the valuation threshold of $1 trillion USD. (There is no standard, objective methodology for obtaining such valuations for private companies). According to the OECD, artificial intelligence firms captured 61% of global venture capital in 2025. A Stanford report puts global VC investment in Silicon Valley firms alone at 75% of the total. Overall capital spending on AI in the United States is at roughly 2% of GDP, and expected to reach 3-4% in 2027. This is equal to and slightly exceeding the US defense budget.

This is a lot of investment, but in what, precisely? As it stands right now, AI is an all-purpose everything enhancer, like the patent cure-all medicines of the nineteenth century.

There's more at the link, including thumbnail accounts of recent events on Wall Street and among VCs.

Think of it like this: The techno cult launched ChatGPT in late November of 2022. That's the fuse. That large pile of money that's been floating around looking for investment vehicles, that's the powder. When ChatGPT became a surprise hit, the fuse was lit and the powder exploded: BOOM! That's where we are now.

Eye on the High Line

Dolly Parton, RIP

How LLMs work [in pictures]

We will use everyday language where possible and mark technical terms in purple. Key is that modern LLMs have a component of making answers (in blue, called "inference") and a component of improving answers (red, called "training"). pic.twitter.com/iUmGaNMKUz

— Kording Lab 🦖 (@KordingLab) August 25, 2026

The real work is done by a core machine, the neural network which is used to generate one word at a time based on its inputs. The probability of each word depends on prior words. And words are chosen one by one according to those probabilities. pic.twitter.com/7zWO87TThs

— Kording Lab 🦖 (@KordingLab) August 25, 2026

The result of this are what is sometimes called large-scale "stochastic parrots". Very impressive text but often somewhat superficial. But these are the old LLMs (think 2022). Innovation was super fast since.

— Kording Lab 🦖 (@KordingLab) August 25, 2026

A second idea (B) for making answers, tool use. Call programs like databases, internet search, calculators etc. pic.twitter.com/zyPU00vmV3

— Kording Lab 🦖 (@KordingLab) August 25, 2026

And all of this, is better with better “training“ goals. Wherever truth is defined, push towards this truth (D). And where it is not defined, use LLMs to check answers ("self evaluation", (E)) pic.twitter.com/vLOy2UCsYb

— Kording Lab 🦖 (@KordingLab) August 25, 2026

A great deal of the strengths and weaknesses of today's LLMs can be understood from these component ideas.

— Kording Lab 🦖 (@KordingLab) August 25, 2026

On the waterfront [uptown Hoboken]

We need to consider debt forgiveness in the context of our $40 trillion deficit

Paul Vigna, An Ancient Sumerian Solution to Our $40 Trillion Deficit, Aug. 22, 2026.

The U.S. federal debt has hit $40 trillion. Add the debt owed by U.S. states, corporations and consumers, and the figure rises to about $77 trillion in debt, set against an annual gross domestic product of about $32 trillion. The interest on all of it is compounding constantly. It’s not just the United States, either. Globally, there is $350 trillion in debt, roughly treble global G.D.P. It’s like snowpack on a mountainside. It may look stable right now, but it’s creating the conditions for an avalanche. We are past the point where we can deal with our current debts in normal ways. The options open to us are extremely unlikely or highly destructive: Grow our way out of it, raise taxes, inflate the debt away or wait for the economic fallout.

Ancient societies had another method to deal with debt. It was called an amargi — a blanket declaration of public debt cancellation. All public debts written off. Disappeared. It sounds laughable, I know. But, really, that’s just because the idea has been buried so deeply in history that you’ve probably never heard of it. In the ancient world, it presented a pragmatic solution to an intractable problem. And now, faced with impossible-to-repay debts that are weighing down our economy, is the time to look at the amargi and the lessons it offers about how to think about finance.

Vigna then runs through a quick inventory debt forgiveness in the ancient world.

The reason the practice often worked in the first place was because the ancient world understood something about our monetary system we have mostly forgotten: Money is an invented social construct. It isn’t real, not in the way a tree or a stone is real. The system of money and credit is a thing humans made up. It’s a record-keeping device for distributing resources. And since money is a human creation, we can alter it when needed.

It’s hard to predict what will happen if we don’t address our debt, but it’s not hard to predict that the outcome will be bad. Governments can borrow to cover up other problems for only so long. Sometimes what comes next is a hyperinflationary spiral and economic collapse, as in Zimbabwe or Weimar Germany. Sometimes it’s a national default that rocks financial markets, such as in Egypt and Anatolia under the Ottoman Empire in the 1870s. And sometimes it’s just a steadily slipping quality of life as debt saps people’s ability to build their own wealth.

Money represents resources, or at least access to resources. When so much of it is going to debt repayment, it means money that could be spent on goods or services is diverted. The federal government now spends over $1 trillion a year on debt interest — money that could otherwise be spent on roads, schools or health care. [...] We are unlikely to see a modern amargi, but as a set of principles for reconsidering our relationship with money, its applicability is rich. [...] We need to start seeing money and debt the way people saw it back then: As a system to distribute resources that, like any system, can be periodically reset.

Red flowers on the street corner

Nestled among the beans