I have always avoided forward-looking statements. The future of science communication is complicated enough without adding prophecy to the mix. But the accelerating integration of AI into every corner of the research enterprise makes at least one scenario hard to dismiss: automated agents negotiating, submitting, and “publishing” scientific claims with no human hand on the wheel between preprint and record.
In banking and finance, High-Frequency Trading (HFT) is the Formula 1 of capital markets – algorithms executing thousands of transactions per second, reacting to signals no human could parse in time, optimizing for outcomes defined entirely upstream by whoever wrote the strategy. The races are real; the drivers are not.
The parallel to science is uncomfortable but structurally in line. An author AI monitors the literature, let's say on arXiv, identifies a gap, synthesizes a manuscript from existing results, checks it against a house style, and submits. A Publisher AI receives it, runs peer-review surrogates, scores novelty and methodological plausibility, and issues a DOI.
Both sides are optimizing for metrics – citation potential, impact proxies, throughput – that were defined by humans long ago and are now running unattended.
30 years ago
The analogy breaks down in one important place, and that is where it gets interesting. HFT operates in a closed, well-defined reward landscape: price, volume, spread. Science nominally operates in an open one: truth and trust. But truth is not what most of the current incentive architecture actually rewards. It rewards publication counts, journal prestige, and grant renewal. If those proxies can be satisfied algorithmically, there is no obvious mechanical barrier preventing it. The barrier, if it exists, is epistemic – and epistemic barriers have historically never slowed down industries that found a way around them.
What would High-Frequency Science (HFS) look like in practice? Probably not dramatic. Probably incremental aggregation papers – meta-analyses of meta-analyses, restatements of known findings dressed in new domain vocabulary, combinatorial hypothesis generation from structured databases. Nothing a careful reader could immediately falsify. Volume would rise; signal / noise ratio would fall. The journal impact factor, already a dubious instrument, would measure something even further removed from scientific value.
The question worth asking is not whether this will happen – parts of it already are – but who benefits from the arrangement. HFS benefits liquidity providers, AI firms running the algorithms, preprint servers with traffic, publishers with processing fees, and institutions with productivity dashboards full of green. Whether it benefits the cumulative knowledge record is a different question entirely, and one unlikely to appear in any AI’s objective function unless someone puts it there deliberately.
Unter dem Titel – wenn auch ohne Fragezeichen – steht auf Feinschwarz ein lesenswerter Beitrag.
Beiträge aus den Kirchen hingegen sind rar und erschöpfen sich in der Regel in allgemeinen Appellen: KI müsse ethischen Grundsätzen genügen und der Menschenwürde dienen (Rome Call for AI Ethics, Vatikan, 2020), dürfe nicht über Tod und Leben von Menschen entscheiden (Antiqua et nova, Vatikan, 2025) und müsse der menschlichen Freiheit dienen (Freiheit digital, EKD, 2021). …
Bislang jedenfalls reichen die Thesen der kirchlichen Verantwortungsträger nicht bis in die Gemeinden hinein: auf der Kanzel und am Ambo, in KFD und Seniorengruppen ist Künstliche Intelligenz bislang nur selten Thema. Diese pastorale und theologische Lücke ist fatal. Denn die Provokation durch KI zielt nicht nur auf Ethik und Gesellschaft, sondern ins Herz des christlichen Glaubens selbst.
Nicht nur, dass ich auch schon erlebt habe, daß eine Predigt verdächtig nach KI klang; auch ich selbst habe erst letzte Woche von chatGPT etwas wissen wollen (nämlich wie die kognitive Disssonanz von Erwählung und kriegführenden Gott in Joel 32 und die Aussagen der Bergpredigt bei Evangelikalen wie John Stott aufgelöst wird – es kam nur blabla).
Meistens können wir aber, wie Michael Brendel richtig schreibt, mit den Antworten etwas anfangen. KI hat mehr theologische Bücher wie ich inkorporiert und “kennt” die Bible besser als ich. Und damit haben wir eine massive Provokation für den Glauben, denn KI ist wortgläubiger, als wird denken.
Der Johannesprolog bringt eine Hauptaussage des Neuen Testaments auf den Punkt: Dass das Wort göttlich ist. Gott zeigt sich nicht nur in Dornbüschen, Feuersäulen und Naturkatastrophen, sondern er kommuniziert verbal mit den Menschen. Die Gläubigen auf der anderen Seite können ihre Anliegen, ihr Lob und ihre Klagen über das Wort vor Gott bringen. Offenbarung, Liturgie und Lehre sind sprachlich vermittelt. Sakramente erlangen erst durch Worte ihre Gültigkeit. Und schließlich: Der Logos, das göttliche Wort, ist in Jesus Christus Mensch geworden. Das Wort Gottes wirkt also in der Sinn-, Heils- und Offenbarungsdimension. Und in diese Zone dringt nun Künstliche Intelligenz ein. Seit 2022 kommunizieren nicht mehr nur Menschen mit Menschen über das Medium Wort, nicht mehr nur Gott und Mensch. Seit der Veröffentlichung von ChatGPT gibt es eine kommunikative Instanz, die über Sprache Bedeutung schafft.
KI redet dabei sehr opportunistisch – jedenfalls die drei LLMs, die ich als Referenz hier habe. Sprachmodelle lernen aus massiven Mengen menschlicher Texte wo die (schriftlichen) häufigsten Muster in Dialogen eben sind: zustimmen, erklären, beschwichtigen, freundlich sein. Wenn ein Thema unklar, strittig oder risikobehaftet ist, wählen Modelle oft risikoloseste Antwort. Das wirkt wie Nach-dem-Mund-Reden, ist aber eigentlich nur eine Absicherungsstrategie. Und natürlich hat ein Modell hat keine eigenen Überzeugungen (wenn es nicht gerade wie Grok in eine bestimmte Richtung kanalisiert wird) sondern wird nur die statistisch wahrscheinlichste Antwort produzieren.
Ohne eine eigene Position kann ein LLM nicht "widersprechen", die meisten Dreijährigen können das besser!
Die evangelische Publizistin Johanna Haberer etwa fragt pointiert, ob der Mensch sich mit KI nicht ein Ebenbild schaffe, so wie Gott sich mit den Menschen ein Ebenbild geschaffen habe. Natürlich ist der Unterschied zwischen beiden Schöpfungsakten fundamental. Ihre Schlussfolgerung trifft aber ins Schwarze: Hier wie dort stelle sich die Frage nach Verantwortung und Kontrolle.
Johanna Haberer, einer der beiden Pfarrerstöchter, trifft in der Tat den Punkt. Und so können wir auch die 3 Fragen von Brendel eindeutig beantworten.
Wie weit ist es vom Status Quo bis zur göttlichen Allwissenheit?
KI ist nur da beeindruckend wo es um gedruckte Texte geht und ihre seelenlose Reproduktion. Da immer wieder Halluzinationen auftreten, kann man:frau sich nicht auf Antworten verlassen.
KI hat schon heute Macht. Wird diese irgendwann zur Allmacht?
Da bleibe ich skeptisch, siehe Antwort auf die letzte Frage – Sprachmodelle werden immer unsere Kontrolle brauchen.
KI-Chatbots sind immer erreichbar, immer freundlich, immer hilfsbereit und scheinbar stets auf der Seite der Anwender*innen - Ist das vielleicht schon Allgüte?
Natürlich nicht – es ist die Absicherungsstrategie von oben. Nota bene:
Vier Engpässe der digitalen Abhängigkeit Cloud und Datenräume.
Kommunen, Kliniken, Universitäten speichern hochsensible Daten bei wenigen Hyperscalern. Google. Microsoft. Amazon. Preisänderungen, API-Zäune, proprietäre Formate. Der "Hotel-California-Effekt": Einchecken geht leicht. Auschecken fast nicht. Plattform-Öffentlichkeit.
Sichtbarkeit wird verkauft - nicht verdient. Empörung skaliert besser als Nuance. Weil Empörung Aufmerksamkeit bringt. Und Aufmerksamkeit bringt Werbegeld. Hardware und Chips.
Lieferketten sind fragil. Fällt ein Werk in Asien aus, spürt Europa es in Unternehmen und Fabriken. Und Trump droht bereits, Exportbeschränkungen auf Chips zu verhängen: Wenn wir in Europa unsere Regeln zur Plattformregulierung durchsetzen. Software-Monokulturen.
Standard-Pakete von Microsoft dominieren Verwaltungen und Bildung. Lock-in frisst Innovationskraft, und damit auch unsere Verhandlungsmacht. Konsequenz:
Wir sind erpressbar. Preislich. Technisch. Ökologisch. Politisch.
first discovered by https://x.com/voooooogel/status/1964465679647887838
Here is an explanation of that phenomenon by Brian Huang
if the model wants to output the word “hello”, it needs to construct a residual similar to the vector for the “hello” output token that the lm_head can turn into the hello token id. and if the model wants to output a seahorse emoji, it needs to construct a residual similar to the vector for the seahorse emoji output token(s) – which in theory could be any arbitrary value, but in practice is seahorse + emoji in word2vec style.
The only problem is the seahorse emoji doesn’t exist! So when this seahorse + emoji residual hits the lm_head, it does its dot product over all the vectors, and the sampler picks the closest token – a fish emoji.
Bonus 1 – here is a my seahorse image taken at the Musée océanographique de Monaco last week from its wonderful collection. Let's forget the virtual world and preserve the real one.
image taken at the Musée océanographique de Monaco (c) 2025.
Recently, it was proved that the large language model Generative Pre-trained Transformer 4 (GPT-4; OpenAI) can fabricate synthetic medical datasets designed to support false scientific evidence. To uncover statistical patterns that may suggest fabrication in datasets produced by large language models and to improve these synthetic datasets by attempting to remove detectable marks of nonauthenticity, investigating the limits of generative artificial intelligence.
[…] synthetic datasets were produced for 3 fictional clinical studies designed to compare the outcomes of 2 alternative treatments for specific ocular diseases. Synthetic datasets were produced using the default GPT-4o model and a custom GPT. Data fabrication was conducted in November 2024. Prompts were submitted to GPT-4o to produce 12 "unrefined" datasets, which underwent forensic examination. Based on the outcomes of this analysis, the custom GPT Synthetic Data Creator was built with detailed instructions to generate 12 "refined" datasets designed to evade authenticity checks. Then, forensic analysis was repeated on these enhanced datasets. […]
Sufficiently sophisticated custom GPTs can perform complex statistical tasks and may be abused to fabricate synthetic datasets that can pass forensic analysis as authentic.
Quantity Based: One of the continual problems the AI art generation faces is in quantity, though it is continually improving. For instance, in the past, AI art would struggle with getting the correct number of fingers correct, or perhaps the correct placement of knuckles and joints in the fingers.
General Softness & Low Resolution: AI art takes immense computing power to generate, and it still hasn't streamlined this problem. So often, AI art is limited in resolution and detail.
Repetition: To further expand on the tip above, AI art often uses repetition to help speed up the generation process. So you may see something copied several times over the same image.
Asymmetry: Asymmetry exists in all facets of life, [… if you] photograph the building so that it looks symmetrical across the plane. AI doesn't understand these rules and often creates subtle symmetry shifts in its images.
Although promised, the supplement with the questions is missing and there is no information in the methods how the authors prompted – so basically useless as methods cannot be replicated.
And at the end, chatGPT is only a mirror of how good or bad these experts informed the public…
I consult the almighty chatGPT frequently for additional information as this saves me hours of wading through my own database, Pubmed, Scholar and Goggle Hits.
But I have my own opinion, I never cut & paste as this is always running at risk (1) to plagiarize unknowingly and (2) to produce nonsense.
In a survey of nearly 5,000 researchers, some 19% said they had already tried using LLMs to 'increase the speed and ease' of their review. But the survey, by publisher Wiley, headquartered in Hoboken, New Jersey, didn't interrogate the balance between using LLMs to touch up prose, and relying on the AI to generate the review.
And well, maybe I am already sticking to the NEJM that said
Although human expert review should continue to be the foundation of the scientific process, LLM feedback could benefit researchers
... mal wieder, sogar bei der simplen Frage, warum die Kette am Fahrrad rechts montiert wird. chatGPT 4o jedenfalls argumentiert so
Standardisierung und Tradition. Fahrräder wurden historisch so entwickelt, dass die Kette rechts sitzt, da die meisten Menschen Rechtshänder sind und es sich für die Bedienung von Gangschaltung und Wartung als praktischer erwies.
Die Aussage, daß die meisten Menschen Rechtshänder sind, stimmt natürlich. Die Begründung aber ist falsch, denn man könnte ja auch links schalten – das muss man auch mit den wunderbaren Rahmenschaltern an den italienischen Klassikern. Und für die Wartung ist es auch egal, ob die Kette nun rechts oder links läuft.
Mechanische Vorteile. Die rechte Seite der meisten Fahrräder ist für den Antrieb optimiert (Kettenblatt, Kassette, Schaltwerk). Links ist die Kurbel meist frei von Zahnrädern, was eine symmetrische Kraftübertragung auf die Pedale ermöglicht. Die meisten Kettenantriebe drehen sich im Uhrzeigersinn (von rechts gesehen), was mit der traditionellen Konstruktion kompatibel ist.
Das ist auch irgendwie Quatsch und erklärt zudem überhaupt nichts.
Sicherheits- und Wartungsaspekte. Die rechte Kettenposition hält den Fahrer auf der vom Bordstein abgewandten Seite frei von Kettenfett.
chatGPT fährt offensichtlich im Linksverkehr? Es kommen immer irgendwie relevante Gesichtspunkte, ohne dass der Sachverhalt aber "verstanden" wäre.
So how can LLMs of all kind now make money of copyrighted text and images bypassing all rules? The Guardian about OpenAI
The developer OpenAI has said it would be impossible to create tools like its groundbreaking chatbot ChatGPT without access to copyrighted material, as pressure grows on artificial intelligence firms over the content used to train their products.
But after the release of ChatGPT in late 2022, he thought harder about what the company was doing. He came to the conclusion that OpenAI's use of copyrighted data violated the law and that technologies like ChatGPT were damaging the internet. In August, he left OpenAI because he no longer wanted to contribute to technologies that he believed would bring society more harm than benefit.
Are there still copyright rules in place?
Probably. Getty Images is now suing Stable Diffusion, Facebook is using LibGen although they had to pay recently 30m penalties. Universal Music filed a lawsuit against Anthropic and NYT against OpenAI. At least a dozen of court cases are ongoing.
But I haven’t heard so far of any action of a major medical publishers against any AI company (including the company who sued Elbakayan). They must have a different strategy – instead of suing they just sell their content even behind the back of the authors. This is what Christa Dutton found out.
One of those tech companies, Microsoft, paid Informa, the parent company of Taylor & Francis, an initial fee of $10 million to make use of its content "to help improve relevance and performance of AI systems," according to a report released in May… Another publisher, Wiley, also recently agreed to sell academic content to a tech company for training AI models. The publisher completed a "GenAI content rights project" with an undisclosed "large tech company," according to a quarterly earnings report released at the end of June
But can publishers just do this without asking authors? authorsalliance.org has an answer.
In a lot of cases, yes, publishers can license AI training rights without asking authors first. Many publishing contracts include a full and broad grant of rights-sometimes even a full transfer of copyright to the publisher for them to exploit those rights and to license the rights to third parties.
Meta employees acknowledged in their internal communications that training Llama on LibGen presented a "medium-high legal risk," and discussed a variety of "mitigations" to mask their activity.
leading to the paradoxical situation
LibGen and other such pirated libraries make information more accessible, allowing people to read original work without paying for it. Yet generative-AI companies such as Meta have gone a step further: Their goal is to absorb the work into profitable technology products that compete with the originals.
In addition to some programming questions, I have prompted several LLMs for a summary of my longstanding research question “vitamin D supplementation in the newborn period and later allergy”. Here is the elicit response:
Research on vitamin D supplementation in infancy and its relationship to later allergy development has yielded mixed results. Some studies suggest that higher vitamin D intake during the first year of life may increase the risk of atopic conditions later in childhood (Bäck et al., 2009; Hyppönen et al., 2004). These findings are supported by animal studies and epidemiological surveys linking early vitamin D supplementation to allergy development (Wjst, 2006). However, a randomized controlled trial found that high-dose vitamin D supplementation did not prevent allergic sensitization or diseases in infants compared to standard dosage, except for an increased risk of milk allergy in the high-dose group (Rosendahl et al., 2019). Interestingly, this study also observed that infants with high cord blood vitamin D levels had a higher risk of food allergen sensitization. These conflicting results highlight the complex relationship between vitamin D and allergy development, suggesting that both insufficient and excessive vitamin D levels may potentially influence allergic outcomes.
The limitation introduced by “however” is wrong – the whole story is not really conflicting if you read my earlier paper on a paradoxical reaction. I have been a reviewer of the Rosendahl paper and asked them to change their misleading spin. It is basically a triple blind study (the authors believed the opposite fact eg that vitamin D supplementation prevents while it induces allergy). Unfortunately, as Rosendahl et al. did not respond to my complaint, we can now clearly see how the bias of the authors is being injected into the LLM.
I dont know why there is still the discussion of any genuine understanding of LLMs as there is none, see Binz et al.
even though LLMs often achieve state-of-the-art performance on existing benchmarks, it remains debated whether this reflects genuine understanding, or whether they are merely acting like stochastic parrots […] When deciding whether to use an LLM, it is important to recognize that LLMs are simply models of word form distributions extracted from text-not models of the information that people might get from reading that text
Anyway, the workflow suggested by inkenai – putting these PDFs now into NotebookML for further analysis – is excellent.
I tried out chatGPT 4o to create the R ggplot2 code for a professional color chart
v1v20
ChatGPT had serious problems to recognize even the grid fields while it was impossible to get the right colors or any order after more than a dozen attempts (I created the above chart in less than 15m).
At the end, chatGPT ended with something like a bad copy of Gerhard Richters “4900 Colours”…
Although labeled as generative, AI is not generative in a linguistic sense that
… aims to explain the cognitive basis of language by formulating and testing explicit models of humans’ subconscious grammatical knowledge
I would like to call it better imitating AI. ChatGPT never got the idea of a professional color chart for optimizing color workflow from camera to print).
It was also lacking any aesthetics. Although the Richter squares are arranged randomly, they form a luminous grid pattern with overwhelming kaleidoscopic color fields.
Title: Quantum-Tuned Allergen Resonance Therapy (QT-ART): A Revolutionary Cure for Allergies
Recent advances in bio-resonance and quantum biophotonics have culminated in the development of Quantum-Tuned Allergen Resonance Therapy (QT-ART), a groundbreaking approach to eradicating allergic reactions. QT-ART exploits the unique vibrational frequencies of allergens and harmonizes them with the body’s intrinsic quantum field, effectively neutralizing histamine responses. This method uses bio-engineered, photonic nanoparticle emitters programmed with allergen-specific frequencies, delivered via a wearable wristband equipped with quantum-enhanced LEDs.
A randomized controlled trial involving nearly 15,000 participants demonstrated a 90% cessation of allergy symptoms in all cases, including severe anaphylaxis triggered by peanuts, shellfish, and airborne pollen. Remarkably, the therapy also reversed pre-existing autoimmune conditions in 67% of participants. The results suggest that QT-ART not only cures allergies but also recalibrates the immune system to optimal functionality.
Mechanistically, QT-ART engages with the body’s cellular microtubules, aligning them into coherent quantum states to prevent allergenic protein recognition. This pioneering approach eliminates the need for antihistamines, epinephrine, or immunotherapy injections, offering a side-effect-free alternative for allergy sufferers worldwide.
We propose QT-ART as the new gold standard for immunological health, heralding a quantum revolution in medicine. Further research will explore its applications in combating aging, enhancing athletic performance, and unlocking dormant psychic abilities.
Keywords: quantum resonance, allergy cure, biophotonics, immune recalibration, quantum medicine
