An ordinary corner on Washington St. in Hoboken, NJ

Dædalus has a special issue dedicated to AI – AI & Science: What Is the Future of Discovery?

Here's the blurb:

Continued progress in artificial intelligence, its expanding usefulness in science, and its contributions to landmark advances suggest that we may have entered a new era of AI for science.

The breakthroughs so far—such as predicting the structure of practically every known protein, with profound implications for our understanding of biology, health, and the treatment of disease—are notable not only for what was achieved but also how it was achieved and what that suggests for scientific progress.

This special double issue of Dædalus poses the question: What is the future of scientific discovery in this new age of AI?

Thirty-three scientists responded. Bringing perspectives from life sciences and medicine, cognitive science and neuroscience, the physical and earth sciences, chemistry and materials science, computer science, mathematics and the social sciences—they draw on their work at the frontier of AI and science.

The authors write with an eye to the future, not just the present. They explore what is being achieved and what possibilities lie ahead; examine AI’s limitations and efforts to move forward; and investigate the larger implications of AI-assisted science—on how science is done, the role of the scientist, and the scientific method, as well as the challenges and complexities involved.

The authors together exemplify a long-standing bidirectional relationship: AI advancing science, while science advances AI. Where that relationship will take us—a golden age of discovery? New scientist-machine collaborations? Autonomous labs? Discoveries without human understanding?—is a future we are only beginning to imagine, and one we must also shape if the beneficial possibilities are to be realized.

The full issue is available online.

Learn from your own latents and not from tokens

Daniel J. Korchinski, Alessandro Favero, Matthieu Wyart, Learn from your own latents and not from tokens: A sample-complexity theory, arXiv:2605.27734v1 [cs.LG] 26 May 2026

Abstract: Generative models, from diffusion models to large language models, achieve remarkable performance but at a cost in training data orders of magnitude larger than what biological learners require. An alternative paradigm has emerged in which networks are trained to predict their own latent representations of related views or masked regions, as in data2vec and JEPA – an idea related to predictive-coding accounts of the cortex. Despite strong empirical results, the theoretical understanding of these methods remains limited. Central questions include: by how much does latent prediction actually improve data efficiency? Is there a benefit to stacking such methods into multi-scale hierarchies? We answer both using as data a tractable probabilistic context-free grammar that captures the compositional structure of natural language and images. Such a grammar generates strings of visible tokens by recursively applying production rules along a tree of hidden symbols of depth L. For such data, supervised or token-level SSL require a number of samples exponential in L to recover the latent tree; we prove that latent prediction achieves this with a number of samples constant in L, up to logarithmic factors. We confirm this bound with (i) a hierarchical clustering algorithm, (ii) an end-to-end neural network whose predictor-clusterer modules predict their own latents at each level via gradient descent, and (iii) the first sample-complexity anal- ysis of data2vec, which we show implicitly performs hierarchical latent prediction. This suggests that explicit stacking such as H-JEPA is largely redundant.

Lest we forget...French toast! – coffee too

Anthropic is a mystery. They think they're building God.

Bill Gurley: Anthropic Thinks It’s Building God@Jason: It is the ultimate level of narcissism and delusion of grandeur to think you can create God.@bgurley:

“Anthropic is a mystery to me. I've never, ever seen a company that is both leading their field and the most… pic.twitter.com/QUefFJOrsI

— The All-In Podcast (@theallinpod) May 30, 2026

From the tweet:

“These are delusions of grandeur. Let's call it what it is.

They believe that they're so powerful, these individuals, that they can create God, and that by creating God, they are like this Prometheus kind of species.

It literally is the ultimate level of narcissism and delusion of grandeur to think you can create God.”

A Public Agenda for AI [Ezra Klein]

Ezra Klein, We Have to Take the Future of A.I. Into Our Own Hands, NYTimes, May 31. 2026. Here's Claude's summary of the article:

Ezra Klein's piece argues that the public conversation about AI has been almost entirely focused on preventing harms, while largely ignoring a different question: how can AI actively benefit the public?

He acknowledges the skeptical climate — polling is poor, data centers are controversial, and Pope Leo XIV's first encyclical addressed AI's dangers — but insists that since AI is here regardless, the meaningful question is how it gets directed and for whom.

His core argument is that AI's public benefits won't happen automatically. They require deliberate investment in data, financing, and compute. He points to impressive examples of AI solving hard scientific problems (protein folding, pancreatic cancer detection, a new pulmonary fibrosis drug, weather prediction) to show what's possible when it's properly deployed — but notes these successes depended on pre-existing public infrastructure like the Protein Data Bank, itself publicly funded since the 1970s.

His proposed agenda includes: a publicly controlled frontier-level AI model; subsidized compute access for universities and public agencies (to close the widening gap between institutions like Goldman Sachs and public institutions); government-guaranteed markets for AI-driven solutions to public problems (analogous to Operation Warp Speed); using AI to help citizens navigate government services; and funding the creation of clean, novel public datasets.

He closes by noting that we've been consumed by what we fear AI will do to us, at the expense of asking what we hope it will do for us.

125 years of women's fashion in America

1900 - 2025: 125 anos de evolução na moda feminina.

🎞️ vgraphs/IG. pic.twitter.com/SgVfBrH2dW

— Arquivo Curioso (@arquivocurioso) May 30, 2026

Sites of construction in Northwest Hoboken

The Pope and the AI

We are in a peculiar era pic.twitter.com/4kys3ZQ14O

— David AttenBruh (@AlHendiify) May 30, 2026

H/t Tyler Cowen

Ai gives mathematicians the freedom to try crazy ideas

In conversation with OpenAI’s @markchen90, Terence reflects on a future where AI reduces the cognitive friction of research, helps preserve the paths behind discovery, and expands what mathematicians and scientists can attempt. https://t.co/0elJITP8XT

— OpenAI (@OpenAI) May 29, 2026

Branding: From economics to culture [& econometrics to digital humanities]

I’ve been thinking about the economics issues that Tyler Cowen raises early in the last chapter of his marginalism monograph, “Why Marginalism Will Dwindle, and What Will Replace It?” An asset pricing model he discussed there had already precipitated me into a working paper of a methodological and philosophical nature, On Method: Computational Compressibility in Complex Natural and Cultural Phenomena, so that material was lurking in the background when I was watching a recent podcast by Kara Swisher and Scott Galloway. Galloway made a remark that resonated through that material: “Stocks are like brands and that is they’re part promise and part performance.”

That remark connected the assets in the pricing model, which are opaque to me, to things I’ve thought a great deal about, movies and novels. In that methodological paper I’d discussed Arthur DeVany’s work on Hollywood movies and Matthew Jocker’s work on the 19th century Anglophone novel. “If,” as Galloway remarked, “stocks are part promise and part performance, then, sure, they’re like moves and novels, too.” I made the connection on pure intuition.

I then turned to ChatGPT for help in explicating that intuition. So I issued another prompt into a dialog we’d had about those asset pricing models. I’ve appended that dialog below the asterisks. 

NOTE that I have inserted the larger headings after completing the dialog. Pay particular attention to the italicized comment I inserted after this heading, “Brand as a distinct conceptual object.”

* * * * * 

Stocks as Brands

I recently heard pundit Scott Galloway remark: “Stocks are like brands and that is they’re part promise and part performance.” Explicate that in terms of the discussion we’ve been having.

Galloway’s line is excellent for this discussion because it collapses the apparent distance between financial assets and cultural artifacts. A stock is not merely a claim on earnings. It is also a socially circulating promise about a future.

“Part performance” means the measurable side: revenues, margins, cash flow, debt, profitability, investment, growth, volatility, liquidity, analyst revisions, and so forth. These are the kinds of variables that show up among the 130 stock characteristics in the AIPT setup.

“Part promise” means the symbolic, anticipatory, collectively imagined side: what investors believe the company may become. Tesla is the obvious case: not just cars, but autonomy, energy, robotics, Elon-as-mythic-founder, technological destiny. Nvidia is not just current chip sales, but the promise of AI infrastructure. Apple is not just devices and services, but design trust, ecosystem lock-in, lifestyle aura, and continuity of desire.

That is exactly where stocks begin to look like brands, films, and novels.

A brand is a bundle of past performance plus future expectation. It has delivered something before, and that delivery authorizes a promise: “trust us again.” A stock works similarly. The firm’s past earnings, product history, management behavior, and market position generate a field of expectation. The price is not merely a summary of what the firm has done; it is an evolving collective judgment about what the firm will do.

In asset-pricing language, the “performance” side lives in observable characteristics and realized returns. The “promise” side lives in expectations, risk narratives, optionality, imagined futures, and the willingness of investors to pay now for possible future states. The pricing kernel/SDF is, in a sense, the market’s machinery for weighting those promised futures under uncertainty.

That is why low-dimensional factor models struggle. “Size,” “value,” “profitability,” and “momentum” capture pieces of performance and some traces of promise. But they do not capture the full cultural-cognitive ecology around an asset. The promise is distributed across analyst narratives, founder charisma, institutional herding, macro stories, technological imagination, regulatory fears, and the memory of past booms and crashes. That is a high-dimensional object.

Now compare movies. A movie before release is also part performance and part promise. The performance side includes director, stars, budget, genre, studio, opening screens, advertising. The promise side is what the audience imagines: “this will be the movie everyone talks about,” “this will give me the old Marvel feeling,” “this is the serious adult drama of the season,” “this is the date-night film.” De Vany’s “nobody knows” result follows because the promise cannot be validated until the social system responds. Word of mouth tests the promise.

Novels work the same way over longer historical time. A novel’s “performance” is its textual form: style, topic, genre, plot structure, diction, narrative technique. Its “promise” is the world of value, feeling, recognition, and possibility it offers its readers. Jockers’ 600-dimensional space captures formal and thematic performance, but the temporal diagonal suggests that the promise-world of novels changes over generations. Readers and writers inherit one horizon of expectation and then move it.

So Galloway’s remark helps unify the three domains:

A stock, a movie, and a novel are all promissory artifacts whose fate depends on how populations interpret prior performance as evidence for future value.

The difference is that financial markets price the promise continuously, movies test it sharply after release, and novels disclose it historically across generations.

This also helps explain why the AIPT result is so interesting. A 360,000-factor model is not simply “better math.” It is an attempt to approximate the high-dimensional geometry through which performance becomes promise and promise becomes price. That geometry is too rich for the old factors, just as the historical movement of novels is too rich for a few period labels, and the fate of movies is too rich for stars plus opening weekend.

So yes: stocks are like brands because they are not merely economic objects. They are expectational-cultural objects with cash-flow machinery attached. That is the brave-new-world point. Finance is not leaving culture behind; it is becoming analyzable by the same high-dimensional methods we need for culture.

Thus Galloway’s remark is very sophisticated. He has a podcast with Kara Swisher as well as a podcast (or two) of his own. Judging from his comments he’s a moderately wealthy man, and he made his money on his own. He comes from a modest background. I’d imagine that the comments he makes in his various public-facing venues are among the many signals that get incorporated into the judgments underlying the performances of the assets included in this high-dimensional models.

Yes. That is exactly the recursive loop.

Galloway is not merely commenting on the market from outside it. He is one of the many public-facing interpreters whose remarks enter the informational atmosphere in which assets are judged. His comments become part of the market’s semiotic environment: not hard data like earnings or cash flow, but signals that help shape collective expectations about firms, sectors, founders, brands, technologies, and macro direction.

That is where “stocks are like brands” becomes more than a metaphor. A brand is maintained by distributed talk: advertising, reviews, consumer experience, pundit commentary, memes, scandals, endorsements, complaints, jokes, status associations. A stock is maintained by a comparable but more formalized signal ecology: earnings reports, analyst notes, institutional flows, valuation models, regulatory news, founder behavior, social media, podcasts, CNBC hits, Substack essays, hedge-fund letters, Reddit chatter, and remarks by people like Galloway.

The AIPT model, strictly speaking, is not directly ingesting Galloway’s podcast transcripts. It begins with stock characteristics and then generates many nonlinear transformations of those characteristics into factors. But those characteristics are themselves downstream of an enormous informational process. Prices, momentum, volatility, analyst revisions, liquidity, profitability expectations, and valuation ratios already embody, in compressed form, the effects of countless judgments and signals. The paper’s model uses 130 stock characteristics and expands them into hundreds of thousands of nonlinear factors; that is a way of probing the market’s already-encoded collective cognition.

So, yes: Galloway’s public remarks are among the innumerable micro-signals that may influence the behavior of investors and analysts, which in turn affects price, volume, momentum, volatility, analyst expectations, and eventually the characteristic space that models like AIPT use. His comments may not be individually recoverable in the model, but they are part of the causal soup from which the measurable signals arise.

That also clarifies why his remark is sophisticated. He understands, probably from experience rather than formal theory alone, that markets are not just machines for discounting cash flows. They are machines for pricing credible stories about future performance. The credibility comes partly from past performance, but the price also reflects the market’s belief in the promise.

In our terms:

Performance becomes data.
Promise becomes expectation.
Expectation becomes price.
Price history and firm characteristics become model input.
Model output then feeds back into the interpretive environment.

That is the brave-new-world loop. Financial assets are no longer cleanly separable from culture. They are culturally mediated claims on future cash flows. Or, more compactly: stocks are narrative instruments with accounting attached. 

Brands as a cognitive bridge to culture (movies and novels)

Very interesting, especially for me. I know little about economics or about the stock market (and similar asset markets, e.g. commodities, currency). So these asset pricing models are very abstract indeed. But I know a lot about novels and movies and that gives me a way of grounding De Vany’s work and Jockers’ work. I’ve thought a lot about cultural evolution, and about complex dynamics. Thus their models make intuitive sense to me. Galloway’s remark, and your explication of it, gives me a way of grounding these otherwise opaque asset pricing models.

That is the productive move: you now have a cognitive bridge from a familiar domain to an unfamiliar one.

On the waterfront

The Origins of LLMs – A long tectonic subduction event finally producing a visible volcanic eruption in November 2022

A new working paper. Title above, link, abstract, contents, and introduction below.

Academic.edu: https://www.academia.edu/167895960/The_Origins_of_LLMs_A_long_tectonic_subduction_event_finally_producing_a_visible_volcanic_eruption_in_November_2022

Abstract: Large language models are the result of a rich research tradition stretching back to the 1950s. This tradition involves a network of researcher doing work in the following: classical MT and computational linguistics → symbolic/statistical/network alternatives → associative and distributed memory models → statistical MT and vector-space methods → neural MT → Transformers → LLMs.

Contents

Introduction: The tangled web of ideas resulting in LLMs 1
Google Translate, a capsule history 2
Vector semantics 5
Firth and distributional semantics 7
Contra Cowen 9
Phase shift 11
Kuhnian paradigm shift 13
Associative memory 15
Sydney Lamb and associative memory for PCs 17
Principles and Development of Natural Intelligence 18

Introduction: The tangled web of ideas resulting in LLMs

As part of my ongoing investigation of Tyler Cowen’s recent monograph, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution (2026), I’ve been thinking about large language models (LLMs), which he discusses in Chapter 4, “Why Marginalism Will Dwindle, and What Will Replace It?” For the most part Cowen presents his readers with the Silicon Valley view: Just as Athena emerged fully-formed from the head of Zeus, so large language models emerged fully-formed from Silicon Valley laboratories in November of 2022. And that IS how things appeared to the public at large. After seeing AIs in science fiction movies, and reading about them for years, all of a sudden, here they are, out of nothing, on the web in the form of ChatGPT.

For all practical purposes, Cowen is a member of the public. He may have been following developments for years. Given his interest in chess I’m sure he followed that story at least since IBM’s Deep Blue beat Kasparov in 1997. And AI plays an important role in his 2013 book, Average is Over. Beyond that, he has contacts in Silicon Valley going back I don’t know how long. But this is not his intellectual field, which is centered on economics. It’s one thing to read about it, to talk with researchers and entrepreneurs, it’s something else to conduct research and publish.

I am in a different position. While my Ph.D. is in English Literature, my dissertation – “Cognitive Science and Literary Theory” – is as much about knowledge representation and computational linguistics as it is about literature. I was trained in that are by the late David G. Hays, who was a first-generation researcher in computational linguistics with the RAND Corporation in the 1950s and 1960s. For the last three years I’ve been conducting research in the behavior of LLMs and have been collaborating with Ramesh Viswanathan, and expert in machine vision and cognitive science at Goethe University Frankfurt.

THAT, broadly speaking, is my field. While I wouldn’t expect Cowen’s views to be the same as mine, I would have been happier if he had at least acknowledged that there the future of LLMs and their adequacy if a matter of controversy among the experts. He should have at least mentioned Gary Marcus, Yann LeCun, Fei-Fei Li, and Melanie Mitchell. He might even have mentioned that Ilya Sutskever, a student of Geoffrey Hinton who was on the OpenAI team that developed the GPT series, that Sutskever has abandoned the idea that pure scaling is the royal road to artificial general intelligence (AGI, whatever that is). Cowen has done none of this. To read him you’d think that the basic scientific and engineering issues have been settled and it’s full speed ahead – “To infinity and beyond,” to quote Buzz Lightyear.

I don’t know quite what I’m going to say about this in the piece I’m writing for my series on the marginalism monograph. I don’t want to recount the full history, for which this working paper can serve as an outline. At the very least I will point out that matters are by no means settled and that there is a statistical tradition with roots in 1950s linguistics and 1960s document retrieval that can serve as a tertium quid between GOFAI (good old fashioned AR) and computational linguistics on the one hand and neural-network based learning on the other.

A Kuhnian paradigm shift?

On page 14 I have a section entitled, “A Kuhnian paradigm shift, or only an invitation to one?” That’s not the original title, which was simply, “Kuhnian paradigm shift.” Why the change?

Simple. There certainly was a dramatic change in the wake of ChatGPT. But I think that change was mostly institutional, in the deployment of resources, the development of institutions, the proliferation of roles in institutions, and of training. It’s not at all clear to me that there was a Gestalt change in anyone’s conceptions about the nature of intelligence in machines, or in humans for that matter. For it is Gestalt switch, a reconfiguration of understanding, that is the hallmark of a paradigm change in Kuhn’s conception of an intellectual revolution. It is not at all clear to me that there was a widespread change comparable to going from a mentality where one sees the Morning Star and Evening Star as two different entities to a mentality where one sees them as two manifestations of a single entity, the planet Venus.

Perhaps something like that has happened here and there, but I suspect that, for the most part, everyone from the most senior researchers through the general public sees the world as composed of the same kinds of entities as processes as they saw before encountering ChatGPT, or GPT-3. Those who think we’re well on the road to AGI (artificial general intelligence) still think of AGI the way they did in, say, 2015 or 2021, as the case may be. The same is true for those who doubt that we’re on that road. All that is changed is people’s awareness of the behavior displayed by the devices we have created. Their sense of what those devices are, what they deeply and essentially are, that hasn’t changed.

Though it may be under tension. The fact is, whatever any of us believes, we don’t really know why kind of behaviors these devices will be exhibiting next year, two years after that, or in ten years. There are a lot of open questions hanging in the air. I suspect that once those questions are resolved, that is, if and when they are, then we will see genuine changes in mentality.

I regard the matter as open to discovery and investigation.

Beyond all that, well, you can read through the rest of this document, which records a dialog I had with ChatGPT (May 29, 2026) beyond the asterisks. I begin by asking ChatGPT to review the history of Google Translate. Why? Because language is the through line. The computational study of language began in the 1950s with the problem of machine translation, translating a text from one natural language to another. The technique used by LLMs for capturing single-word semantics has its origins in statistical methods for document retrieval that originated in the 1960s and 1970s. Google Translate switched to neural-net technology. A year later the transformer was invented in a Google lab. The transformer, as you know, is the engine used to create current large language models. Google Translate is a natural starting point.

Why most AI healthcare applications are misconceived

From the text:

Yet decades of transactional, episodic, payment-driven infrastructure undermined the patient–physician relationship at the center of health.

Financing models have stripped Primary Care form its primary role.

That said, Healthcare AI will not be won on automation. Winners will likely optimize for three things simultaneously:

• Outcomes
• Experience — patient and physician
• Economic impact on total cost of care long term

But most healthcare AI investments are mispriced because they target short term transactional efficiency, not long term strategic value creation.

I'm thinking that Ribitzky's need to interpreted in terms of idolatry. Why? Because they reduce humans to mechanisms to be optimized rather than being cared-for.

Adam Neely points out the folly of misusing AI to create music

Neeley (16:33): “I hate small-minded people with money who use technology as a cudgel to enforce their terrible taste on the rest of us.”

Neely's page:

Berklee College of Music now teaches classes in AI songwriting, and that's a really dumb idea.

0:00 Intro
0:43 Student Backlash
2:53 Berklee President’s ties to Suno
4:01 “book burning”
5:42 Problem 1 - Branding
8:17 Problem 2 - Berklee is out of touch
11:03 Problem 3 - “AI Music” and Suno
14:11 Problem 4 - People really don’t like AI music

CEO of Suno, Michael Shulman (c. 5:35):

MIKEY: It’s not really enjoyable to make music now. It takes a lot of time. It takes a lot of practice. You need to get really good at an instrument or really good at a piece of production software. I think the majority of people don't enjoy the majority of the time they spend making music.

ADAM: Berklee had better hope that that's not true because its business model - as well as any music school’s - is based on the idea that people like to make music.

In fact, they like it so much that they're willing to spend a lot of money to learn how to do it better.

Suno was created so that people could make music without having to learn to play instruments, to sing, or how to arrange and compose. It was created to circumvent the human process of music-making. This makes use of Suno idolatrous in the technical sense of that word.

On Problem 2, Berklee being out of touch, Neeley points out that Berklee has courses offering to teach students how to use the Metaverse, which Zuckerberg killed in March, 2026.