Gefunden auf Deutschlandfunk Kultur:
“Wie Sprachassistenten das Denken manipulieren” ist zwar mit Meinung überschrieben, dabei geht es um Fakten …
Die KI-Forscher erklären es damit, dass GPT-3 zum Ende der Trump-Administration trainiert worden war, als eine harte Haltung gegen die Einwanderung von Flüchtlingen den Diskurs in den USA bestimmte. Diese Einstellung war dann auch in den Trainingsdaten der KI vorherrschend…Die KI-Forscher nennen es den "Geist in der Maschine" und attestierten diesem einen amerikanischen Akzent. Hausgeist mag die bessere Metapher sein. Denn mehr noch als eine Maschine ist GPT ein Haus, wenn man, so wie der deutsche Philosoph Martin Heidegger, Sprache als "Haus des Seins" versteht. Sprache ist das Medium, das uns mit der Welt verbindet und zugleich die Art dieser Verbindung prägt. Sie ist kein neutrales Werkzeug, sie ist ein Denkrahmen. Sprache schafft Wirklichkeit, nicht nur in der Dichtung.
Hausgeist kenne ich nicht dafür aber Denkschule, Tradition, Denkrichtung, Doktrin oder Geisteshaltung. Und meine Meinung – ziemlich bedenklich alles.
By analyzing two main characteristics of the text: perplexity and burstiness. In other words, how predictable or unpredictable it sounds to the reader, as well as how varied or uniform the sentences are.
a statistical measure of how confidently a language model predicts a text sample. In other words, it quantifies how "surprised" the model is when it sees new data. The lower the perplexity, the better the model predicts the text.
is the intermittent increases and decreases in activity or frequency of an event. One of measures of burstiness is the Fano factor -a ratio between the variance and mean of counts. In natural language processing, burstiness has a slightly more specific definition… A word is more likely to occur again in a document if it has already appeared in the document. Importantly, the burstiness of a word and its semantic content are positively correlated; words that are more informative are also more bursty.
Or lets call it entropy? So we now have some criteria
AI texts are more uniform and more predictable and often repetitive with
lack of depth and personality
Sometimes plagiarism checker may recognize “learned” AI phrases. Sometimes reference checkers will find “hallucinated” references
Incorrect content and outdated information in contrast needs human experts
There are some indications that an image is created by AI showing wrong details of the human hand like 6 fingers. So far AI does not understand the semantic meaning of “hand” having only the visual demarcation of hands in images as trained by mechanical turks. Images of hands however, can be misleading for the trained eye where also good painters have difficulties.
Let’s have a closer look at the images of Princess Kate and their kids Charlotte (8, right) , Louis (5, left) and George, 10 (behind) by a check list that I developed earlier with another family member, the Andrew/Maxwell/Giuffre image that even fooled me in the beginning.
Image source: credible /dpa.
File Modification Date/Time : 2024:03:11 07:00:09+01:00
File Access Date/Time : 2024:03:11 16:27:05+01:00
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Image Width : 1024
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Image Description : 10.03.2024, Großbritannien, Windsor: Das undatierte, vom Kensington-Palast herausgegebene Handout-Foto zeigt Kate, Prinzessin von Wales, mit ihren Kindern, Prinz Louis, Prinz George und Prinzessin Charlotte, aufgenommen in Windsor, Anfang dieser Woche, vom Prinzen von Wales. Prinzessin Kate bedankte sich in einer Botschaft in den sozialen Medien für die anhaltende Unterstützung und wünschte den Menschen einen schönen Muttertag. Foto: Prince Of Wales/Kensington Palast/PA Media/dpa – ACHTUNG: Nur zur redaktionellen Verwendung bis zum 31.12.2024 und nur mit vollständiger Nennung des vorstehenden Credits. Das Foto darf nicht bearbeitet oder im Ausschnitt verändert werden. +++ dpa-Bildfunk +++
Artist : Prince Of Wales
Exif Version : 0232
Date/Time Original : 2024:03:10 02:34:23
Create Date : 2024:03:10 02:34:23
Source : Kensington Palast/PA Media
Urgency : 4
Transmission Reference : 911-004243
Instructions : UNITED KINGDOM OUT, IRELAND OUT, PICTURE DESK USE ONLY. NO SALES. HANDOUT
Supplemental Categories : Leute
Credit : dpa
Caption Writer : kde
Title : urn:newsml:dpa.com:20090101:240310-911-004243
Elvis ID : 9WexS6c3amM9b0m_iOBING
Keyword : Monarchie, Royals, Familie
Credit Line : dpa
Image Size : 1024×1536
Megapixels : 1.6
Situation: credible, should show their well being
Photographer: allegedly husband
Camera: unknown, cropped wide angle?
The overall look: A bit weird and plastic look in my eyes. Dimensions are wrong as her upper body seems too large for her legs. The right arm of Louis (and even George?) seem too long. Trying the posture of her in reality shows that it is unreal to get the embracing hands in this position.
Hands: The fingers of Charlotte’s left hand are larger than the fingers of her right hand. The index finger of Louis is missing which is difficult to reproduce in front of a mirror.
Teeth: Kate’s teeth look authentic when compared with other pictures of her, except for an unsharp band on the upper front teeth. Without having other images at hand, the teeth of the children look age-related (although Louis may be older than 5 on this picture).
Pattern: Floor looks good except left wall. There is a gap at the patten of right arm of Louis and lower left arm of Charlotte. While images of natural objects never have 100% identical patterns, such patterns are frequent with man-made objects — and difficult for AI to reproduce.
Sharpness: Floor mosaic: gets unsharp from tile 6 onwards – which is otherwise a perfect sharp area in the rest of the image.
Irregular: Kate’s right upper shoe border looks double. Zipper misaligns.
Sun/Shadow: sunshine on Kate’s left hand although there should be shadow under Charlotte’s arm. And well there background in the triangle under Charlotte’s arm is missing. The window mirror shows a tree that could cast more shadow on the scene.
General: Green leaves on the background trees in early March?
no clear results from splicing probability heatmap and ELA
just 4 hours later a new message that I still do not believe to be the whole truth https://twitter.com/KensingtonRoyal/status/1767135566645092616
Maybe that business should be left to professional photographers?
More comments at SPON by Matthias Kremp: Another possibility is a Google Pixel 8 that combines internally images which is however unlikely here. Kremp notices also the white paint at the step behind Louis.
SKY believes that The first save was made at 9.54pm on Friday night, with the second at 9.39am on Saturday morning.
The image was taken at Adelaide Cottage – the family’s home in Windsor – on a Canon 5D mark IV, which retails at £2,929.99 and used a Canon 50mm lens, which is priced at £1,629.99.
which contradicts the dpa exif data…
March 20, 2024
Guardian “Photo of Queen Elizabeth II and family was enhanced at source, agency says” and a famous photographer Pete Souza “lets not call it photoshopped”.
IF : Transcriptional activator; when present a gene will be transcribed. In general there is no termination of events unless the signal is gone; the program ends only with the death of the cell. So the IF statement is always a part of a loop.
WHILE : Transcriptional repressor; gene will be transcribed until repressor is not present.
FUNCTION: There are no equivalents of function calls. All events happen is the same space and there is always a likelihood of interference. One can argue that organelles can act as a compartment that may have a function like properties but they are highly complex and are not just some kind of input-output devices.
GOTO is always dependent on a condition. This can happen in case of certain network connections such as feedforward loops and branched pathways. For example if there is a signalling pathway like this: A → B → C and there is another connection D → C then if somehow D is activated it will directly affect C, making A and B dispensable.
Of course these are completely different concepts. I fully agree with the further stackexchange discussion that
it is the underlying logic that is important and not the statement construct itself and these examples should not be taken as absolute analogies. It is also to be noted that DNA is just a set of instructions and not really a fully functional entity … However, even being just a code it is comparable to a HLL [high level language] code that has to be compiled to execute its functions. See this post too.
Please forget everything you read from Francis Collins about this.
A central promise of artificial intelligence (AI) in healthcare is that large datasets can be mined to predict and identify the best course of care for future patients. … Chekroud et al. showed that machine learning models routinely achieve perfect performance in one dataset even when that dataset is a large international multisite clinical trial … However, when that exact model was tested in truly independent clinical trials, performance fell to chance levels.
This study predicted antipsychotic medication effects for schizophrenia – admittedly not a trivial task due to high individual variability (as there are no extensive pharmacogenetics studies behind). But why did it completely fail? The authors highlight two major points in the introduction and detail three in the discussion
models may overfit the data by fitting the random noise of one particular dataset rather than a true signal
poor model transportability is expected due to patients, providers, or implementation characteristics that vary across trials
in particular patient groups that are too different across trials while this heterogeneity is not covered in the model
missing outcomes and covariates like psychosocial information and social determinants of health were not recorded in all studies
patient outcomes may be too context-dependent where trials may have subtly important differences in recruiting procedures, inclusion criteria and/or treatment protocols
So are we left now without any clue?
I remember another example of Gigerenzer in “Click” showing misclassification of chest X rays due to different devices (mobile or stationary) which associates with more or less serious cases (page 128 refers to Zech et al.). So we need to know the relevant co-factors first.
There is even a first understanding of the black box data shuffling in the neuronal net. Using LRP (Layer-wise Relevance Propagation) the recognition by weighting the characteristics of the input data can already be visualized as a heatmap.
A study in Nature last month highlights a previously underappreciated aspect of this phenomenon: the existence of data voids, information spaces that lack evidence, into which people searching to check the accuracy of controversial topics can easily fall…
Clearly, copying terms from inaccurate news stories into a search engine reinforces misinformation, making it a poor method for verifying accuracy…
Google does not manually remove content, or de-rank a search result; nor does it moderate or edit content, in the way that social-media sites and publishers do.
So what could be done?
There's also a body of literature on improving media literacy - including suggestions on more, or better education on discriminating between different sources in search results.
Sure increasing media literacy at the consumer site would be helpful. But letting Google earn all that money without any further curation efforts? The original study found
Here, across five experiments, we present consistent evidence that online search to evaluate the truthfulness of false news articles actually increases the probability of believing them.
So why not putting out red flags? Or de-rank search results?
Texte, die mit Künstlicher Intelligenz verfasst wurden sind kaum von menschlichen zu unterscheiden. Eine Prüfung sei für Unis deshalb nur sehr schwer möglich, sagt Dekan Hnilica. “Wir haben andere Teile unseres Studiums, in denen die Studierenden ihre Lernergebnisse oder erwarteten Lernergebnisse nachweisen können. Daher ist die Bachelorarbeit überflüssig.”
I expect that in the not too distant future AI will target every paper and not only a suspicious table or an image found by chance. Nevertheless using this now as a weapon is immoral and at high risk of false accusations. And , it may even be prosecuted as criminal defamation.
Feb 11, 2025
Unfortunately scientific integrity is being used again as personal weapon. Stefan Weber is making a business from right wing clients to verify doctoral theses. Without doubt, he has excellent technical skills (or at least a Turnitin account) but also completely lost all sense of proportion and direction. See
In some cases, his accusations turned out to be unfounded or less serious than he portrayed them. That's why he is viewed more critically in Austria. … Until the Föderl-Schmid case, none of this had harmed him much. But for those he accused, it was a different story. Even if the allegations came to nothing, their reputation was tarnished
“Free synthetic data”? There are numerous Google ads selling synthetic aka fake data. How “good” are these datasets? Will they ever been used for scientific publications outside the AI field eg surgisphere-like?
Just one problem: the video isn't real. "We created the demo by capturing footage in order to test Gemini's capabilities on a wide range of challenges. Then we prompted Gemini using still image frames from the footage, and prompting via text." (Parmy Olsen at Bloomberg was the first to report the discrepancy.)
It doesn’t even give more confidence if Oriol Vinyals now responds
All the user prompts and outputs in the video are real, shortened for brevity. The video illustrates what the multimodal user experiences built with Gemini could look like. We made it to inspire developers.
May I also emphasize that AI is a research method suffering form severe flaws as Nature reported again yesterday “Scientists worry that ill-informed use of artificial intelligence is driving a deluge of unreliable or useless research”
A team in India reported that artificial intelligence (AI) could do it, using machine learning to analyse a set of X-ray images. … But the following September, computer scientists Sanchari Dhar and Lior Shamir at Kansas State University in Manhattan took a closer look. They trained a machine-learning algorithm on the same images, but used only blank background sections that showed no body parts at all. Yet their AI could still pick out COVID-19 cases at well above chance level.
The problem seemed to be that there were consistent differences in the backgrounds of the medical images in the data set. An AI system could pick up on those artefacts to succeed in the diagnostic task, without learning any clinically relevant features - making it medically useless.
There has been no systematic estimate of the extent of the problem, but researchers say that, anecdotally, error-strewn AI papers are everywhere. "This is a widespread issue impacting many communities beginning to adopt machine-learning methods," Kapoor says.
Die Gedanken sind frei,
wer kann sie erraten?
Sie ziehen vorbei, wie nächtliche Schatten.
Kein Mensch kann sie wissen,
kein Jäger erschießen mit Pulver und Blei.
Die Gedanken sind frei.
Was sich so schön lyrisch bei Hoffmann von Fallersleben anhört, ist eben nur Lyrik des 19. Jahrhunderts. Gedankenlesen fasziniert die Menschen seit König Davids Zeiten, aber ist erst seit kurzem in Ansätzen möglich (MPI)
Das Ergebnis erstaunte Libet, ebenso wie viele Forscher bis heute: Im Hirn der Probanden baute sich das Bereitschaftspotential bereits auf, bevor sie selbst den Willen zur Bewegung verspürten. Selbst wenn man eine gewisse Verzögerung beim Lesen der Stoppuhr annahm, blieb es dabei – der bewusste Willensakt ereignete sich im Durchschnitt erst drei Zehntelsekunden, nachdem die Handlungsvorbereitungen im Hirn angelaufen waren. Für viele Hirnforscher ließ das nur einen Schluss zu: Die grauen Zellen entschieden offenbar an uns vorbei.
Die technische Auflösung geht immer weiter, von der Antizipation einfacher Bewegungsmuster nun hin zur kompletten Bilderkennung im Gehirn “Mental image reconstruction from human brain activity” hier in der geringfügig korrigierten DeepL Übersetzung
Die von Menschen wahrgenommenen Bilder können aus ihrer Gehirnaktivität rekonstruiert werden. Allerdings ist die Visualisierung (Externalisierung) von mentalen Bildern eine Herausforderung. Nur wenige Studien haben über eine erfolgreiche Visualisierung von mentaler Bilder berichtet, und ihre visualisierbaren Bilder waren auf bestimmte Bereiche wie menschliche Gesichter oder Buchstaben des Alphabets beschränkt. Daher stellt die Visualisierung mentaler Bilder für beliebige natürliche Bilder einen bedeutenden Meilenstein dar. In dieser Studie haben wir dies durch die Verbesserung einer früheren Methode erreicht. Konkret haben wir gezeigt, dass die in der bahnbrechenden Studie von Shen et al. (2019) vorgeschlagene Methode zur visuellen Bildrekonstruktion stark auf visuelle Informationen, die vom Gehirn dekodiert werden, angewiesen ist und die semantischen Informationen, die während des mentalen Prozesses benutzt werden, nicht sehr effizient genutzt hat. Um diese Einschränkung zu beheben, haben wir die bisherige Methode auf einen Bayes’sche Schätzer erweitert und die Unterstützung semantischer Informationen in die Methode mit aufgenommen. Unser vorgeschlagener Rahmen rekonstruierte erfolgreich sowohl gesehene Bilder (d.h. solche, die vom menschlichen Auge beobachtet wurden) als auch vorgestellte Bilder aus der Gehirnaktivität. Die quantitative Auswertung zeigte, dass unser System gesehene und imaginierte Bilder im Vergleich zur Zufallsgenauigkeit sehr genau identifizieren konnte (gesehen: 90,7%, Vorstellung: 75,6%, Zufallsgenauigkeit: 50.0%). Im Gegensatz dazu konnte die frühere Methode nur gesehene Bilder identifizieren (gesehen: 64,3%, imaginär: 50,4%). Diese
Ergebnisse deuten darauf hin, dass unser System ein einzigartiges Instrument zur direkten Untersuchung der subjektiven Inhalte des Gehirns wie Illusionen, Halluzinationen und Träume ist.
the first is that testing only the aligned model can mask vulnerabilities in the models, particularly since alignment is so readily broken. Second, this means that it is important to directly test base models. Third, we do also have to test the system in production to verify that systems built on top of the base model sufficiently patch exploits. Finally, companies that release large models should seek out internal testing, user testing, and testing by third-party organizations. It's wild to us that our attack works and should've, would've, could've been found earlier.
This paper studies extractable memorization: training data that an adversary can efficiently extract by querying a machine learning model without prior knowledge of the training dataset. We show an adversary can extract gigabytes of training data from open-source language models like Pythia or GPT-Neo, semi-open models like LLaMA or Falcon, and closed models like ChatGPT.
I am not convinced that the adversary is the main point her. AI companies are stealing data [1, 2, 3, 4, 5] without giving ever credit to the sources. So there is now a good chance to see to where ChatGPT has been broken into the house.
What initiated my change of mind was playing around with some AI tools. After trying out chatGPT and Google's AI tool, I've now come to the conclusion that these things are dangerous. We are living in a time when we're bombarded with an abundance of misinformation and disinformation, and it looks like AI is about to make the problem exponentially worse by polluting our information environment with garbage. It will become increasingly difficult to determine what is true.
"Godfather of AI" Geoff Hinton, in recent public talks, explains that one of the greatest risks is not that chatbots will become super-intelligent, but that they will generate text that is super-persuasive without being intelligent, in the manner of Donald Trump or Boris Johnson. In a world where evidence and logic are not respected in public debate, Hinton imagines that systems operating without evidence or logic could become our overlords by becoming superhumanly persuasive, imitating and supplanting the worst kinds of political leader.
At least in medicine there is an initiative underway where the lead author can be contacted at the address below.
In my field, the first AI consultation results look more than dangerous with one harmful response out of 20 questions.
A total of 20 questions covering various aspects of allergic rhinitis were asked. Among the answers, eight received a score of 5 (no inaccuracies), five received a score of 4 (minor non-harmful inaccuracies), six received a score of 3 (potentially misinterpretable inaccuracies) and one answer had a score of 2 (minor potentially harmful inaccuracies).
Within a few years, AI-generated content will be the microplastic of our online ecosystem (@mutinyc)
