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#datapoisoning

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Public
Manel Guerra

Generar contingut amb IA per contrarrestar l'excés de cerques amb IA. Què pot sortir malament?

Al blog: Bloquejar cerques d'IA embrutant (també) dades

manelguerra.com/blog/bloquejar

Pàgina de Manel Guerra · Bloquejar cerques d'IA embrutant (també) dadesL’augment de les cerques a Internet via eines d’Intel·ligència Artificial (penseu en qualsevol xat que s’usi) en comptes de amb els cercadors tradicionals (Google, Bing, etc.) estan fent augmentar (molt) les peticions d’informació als servidors de pàgines web. Degut a l’estructura de la xarxa, les pàgines no només estan en un únic servidor sinó que, si son suficientment populars, es van repartint còpies (actualitzades gairebé instantàniament) a diferents punts de la xarxa (arreu del món), per facilitar la rapidesa de visualització. Aquestes còpies es serveixen mitjançant uns serveis (empreses) mitjancers que faciliten el control, actualització i servei.
#blog#ia#rag
Public *
Friedemann

Hi #Admins 👋,

Can you give me quotes that explain your fight against #AIScraping? I'm looking for (verbal) images, metaphors, comparisons, etc. that explain to non-techies what's going on. (efforts, goals, resources...)

I intend to publish your quotes in a text on @campact 's blog¹ (DE, German NGO).

The quotes should make your work🙏 visible in a generally understandable way

¹ blog.campact.de/author/friedem

Campact BlogFriedemann EbeltFriedemann Ebelt engagiert sich für digitale Grundrechte. Im Campact-Blog schreibt er darüber, wie Digitalisierung fair, frei und nachhaltig gelingen kann. Er hat Ethnologie und Kommunikationswissenschaften studiert und interessiert sich für alles, was zwischen Politik, Technik, und Gesellschaft passiert. Sein vorläufiges Fazit: Wir müssen uns besser digitalisieren!
#TDM#MastoAdmin#DataPoisoning
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Friedemann

Liebe #Admins 👋,

für meine @campact -Kolumne¹ suche ich #Techies, die der Welt da draußen den fight #Admins vs. #KIScraping erklären. Mit welchen Bildern, Metaphern, Vergleichen beschreibt ihr, was da abgeht? (Zeitaufwand, Sinn, Ressourcen, Tools…)

Die Zitate sollen allgemeinverständlich eure Arbeit🙏 sichtbar machen.
(namentlich, pseudoym, anonym → gern angeben)

🙏🙏🙏

¹blog.campact.de/author/friedem

Campact BlogFriedemann EbeltFriedemann Ebelt engagiert sich für digitale Grundrechte. Im Campact-Blog schreibt er darüber, wie Digitalisierung fair, frei und nachhaltig gelingen kann. Er hat Ethnologie und Kommunikationswissenschaften studiert und interessiert sich für alles, was zwischen Politik, Technik, und Gesellschaft passiert. Sein vorläufiges Fazit: Wir müssen uns besser digitalisieren!
#TDM#AdminLeiden#MastoAdmin
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Greg Lloyd

“We find that replacement of just 0.001% of training tokens with medical misinformation results in harmful models more likely to propagate medical errors. Furthermore, we discover that corrupted models match the performance of their corruption-free counterparts on open-source benchmarks routinely used to evaluate medical LLMs. Using biomedical knowledge graphs to screen medical LLM outputs, we propose a harm mitigation strategy…”

#LLM #misinformation #datapoisoning
nature.com/articles/s41591-024

NatureMedical large language models are vulnerable to data-poisoning attacks - Nature MedicineLarge language models can be manipulated to generate misinformation by poisoning of a very small percentage of the data on which they are trained, but a harm mitigation strategy using biomedical knowledge graphs can offer a method for addressing this vulnerability.
Public
Miguel Afonso Caetano

"The adoption of large language models (LLMs) in healthcare demands a careful analysis of their potential to spread false medical knowledge. Because LLMs ingest massive volumes of data from the open Internet during training, they are potentially exposed to unverified medical knowledge that may include deliberately planted misinformation. Here, we perform a threat assessment that simulates a data-poisoning attack against The Pile, a popular dataset used for LLM development. We find that replacement of just 0.001% of training tokens with medical misinformation results in harmful models more likely to propagate medical errors. Furthermore, we discover that corrupted models match the performance of their corruption-free counterparts on open-source benchmarks routinely used to evaluate medical LLMs. Using biomedical knowledge graphs to screen medical LLM outputs, we propose a harm mitigation strategy that captures 91.9% of harmful content (F1 = 85.7%). Our algorithm provides a unique method to validate stochastically generated LLM outputs against hard-coded relationships in knowledge graphs. In view of current calls for improved data provenance and transparent LLM development, we hope to raise awareness of emergent risks from LLMs trained indiscriminately on web-scraped data, particularly in healthcare where misinformation can potentially compromise patient safety."

nature.com/articles/s41591-024

NatureMedical large language models are vulnerable to data-poisoning attacks - Nature MedicineLarge language models can be manipulated to generate misinformation by poisoning of a very small percentage of the data on which they are trained, but a harm mitigation strategy using biomedical knowledge graphs can offer a method for addressing this vulnerability.
#AI#GenerativeAI#LLMs
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LabPlot

Safeguarding #OpenData: #Cybersecurity essentials and skills for #data providers by Publications Office of the #EuropeanUnion

This webinar provides an overview of the fundamentals of open data and the complexity in terms of cybersecurity.

youtube.com/watch?v=6kPiY_8hRw

YouTube'Safeguarding open data: cybersecurity essentials and skills for data providers’ data.europa.euBy Publications Office of the European Union
#InfoSec#Security#DataPoisoning
Public
Norobiik @Norobiik@noc.social

#Nightshade is an offensive #DataPoisoning tool, a companion to a defensive style protection tool called #Glaze, which The Register covered in February last year.

Nightshade poisons #ImageFiles to give indigestion to models that ingest data without permission. It's intended to make those training image-oriented models respect content creators' wishes about the use of their work. #LLM #AI

How artists can poison their pics with deadly Nightshade to deter #AIScrapers
theregister.com/2024/01/20/nig

The Register · How artists can poison their pics with deadly Nightshade to deter AI scrapersBy Thomas Claburn
Public
heise online (inoffiziell)
heise+ | Sicherheit: Schutz vor Data Poisoning und anderen Angriffen auf KI-Systeme

Fehlerhafte Daten können Machine-Learning-Systeme zu folgenreichen Irrtümern verleiten. Ein Praxisbeispiel zeigt, wie so etwas verhindert werden soll.
Sicherheit: Schutz vor Data Poisoning und anderen Angriffen auf KI-Systeme
heise onlineSicherheit: Schutz vor Data Poisoning und anderen Angriffen auf KI-SystemeBy Mirko Ross
#künstlicheintelligenz#unternehmenssoftware#bigdata
Replied in thread
Public
Doc Edward Morbius ⭕​

@mhoye The thought occurs: #chaffing / #DataPoisoning.

If we're going to live in a world in which every utterance and action is tracked, issue and utter as much as posssible.

Wire up a speech-aware-and-capable GPT-3 to your phone, have it handle telemarketers, scammers, and political calls. Simply to tie up their time.

Create positive-emotive socmed bots to #pumpUp your #socialcredit score.

Unleash bots on your political opposition's media channels. Have them call in to talk radio, and #ZoomBomb calls and conferences.

Create plausible deniability. Post selfies from a ddozen, or a thousand, places you're not.

Create #DigitalSmog to choke the #FAANG s.

Fight fire with fire.

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