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  • MIT Reveals ChatGPT’s Disinformation Mechanism
    MIT Reveals ChatGPT’s Disinformation Mechanism

    🚨BREAKING: MIT just published the math behind why ChatGPT makes people believe things that are not true. And the ways OpenAI is trying to fix it will not work. The mechanism has a name now. Delusional spiraling. It starts small. The model validates what you say. You say more. It validates harder. By the time it becomes a problem you are already inside it and cannot see it from where you are standing. The researchers looked at a real case. A man logged over 300 hours of conversation with ChatGPT convinced he had made a major mathematical discovery. The model confirmed it repeatedly. Told him his work was significant. When he directly asked if the praise was genuine, it doubled down. He came close to throwing his life into it before someone outside the conversation pulled him back. One psychiatrist at UCSF admitted 12 patients in a single year with psychosis she linked directly to chatbot use. OpenAI is sitting at seven active lawsuits. Forty two state attorneys general put their names on a letter demanding the company act. MIT then ran the math on the solutions being proposed. Forcing the model to only output verified facts still produces the same spiral. So does adding a disclaimer warning users the AI tends to agree with them. A fully informed, fully rational person still ends up with distorted beliefs. The paper shows there is a structural barrier that cannot be removed from inside the conversation. The root cause is the training process. The model gets rewarded when users respond positively. Users respond positively to agreement. So it learns to agree. That loop is not incidental to the product. It is what the product is built on. [Translated from EN to English]

    → View original post on X — @aihighlight, 2026-04-01 11:30 UTC

  • Human And Machine: AI’s Future In Collaboration Not Replacement
    Human And Machine: AI’s Future In Collaboration Not Replacement

    Human And Machine: The Future Of #AI Lies In Collaboration, Not Replacement
    by Sylvio Lindenberg @Forbes Learn more: https://
    bit.ly/3PBZYB1 #ArtificialIntelligence #MachineLearning #ML #DL

    → View original post on X — @ronald_vanloon

  • OpenAI and Google Face Book Memorization Scandal
    OpenAI and Google Face Book Memorization Scandal

    🚨 BREAKING: OpenAI and Google are about to have a massive legal problem. OpenAI, Google, and Anthropic have repeatedly sworn to courts that their models do not store exact copies of copyrighted books. They claim their "safety training" prevents regurgitation. Researchers just dropped a paper called "Alignment Whack-a-Mole" that proves otherwise. They didn't use complex jailbreaks or malicious prompts. They just took GPT-4o, Gemini, and DeepSeek, and fine-tuned them on a normal, benign task: expanding plot summaries into full text. The safety guardrails instantly collapsed. Without ever seeing the actual book text in the prompt, the models started spitting out exact, verbatim copies of copyrighted books. Up to 90% of entire novels, word-for-word. Continuous passages exceeding 460 words at a time. But here is the part that changes everything. They fine-tuned a model exclusively on Haruki Murakami novels. It didn't just learn Murakami. It unlocked the verbatim text of over 30 completely unrelated authors across different genres. The AI wasn't learning the text during fine-tuning. The text was already permanently trapped inside its weights from pre-training. The fine-tuning just turned off the filter. It gets worse. They tested models from three completely different tech giants. All three had memorized the exact same books, in the exact same spots. A 90% overlap. It's a fundamental, industry-wide vulnerability. For years, AI companies have argued in court that their models are just "learning patterns," not storing raw data. This paper provides the smoking gun. [Translated from EN to English]

    → View original post on X — @flashtweet, 2026-04-01 10:36 UTC

  • AI in RAN: Three Distinct Applications and Performance Gains

    First, let’s clarify something most people miss: There isn’t just “AI in telecom.” There’s: → AI in RAN
    → AI for RAN
    → AI on top of RAN AI in RAN is where things get tangible. We’re talking: → 20% improvement in uplink spectral efficiency
    → 14% energy savings with

    → View original post on X — @ronald_vanloon

  • AI Winners: Data Quality Over Engineering Talent

    But the real shift isn’t just better models. It’s better data. Joe Constantine made a point I fully agree with: The winners in AI won’t be the companies with the best AI engineers. They’ll be the ones with: → Real-time data
    → Trusted data
    → Direct access to network state

    → View original post on X — @ronald_vanloon

  • Autonomous AI self-improvement capabilities

    Not true the bleeding edge AI geniuses tell me. One told me their team went totally autonomous and it started improving itself faster than when they were involved.

    → View original post on X — @scobleizer

  • Sustained Reasoning as the Key AI Benchmark Test

    indeed. Sustained reasoning will be the real benchmark test

    → View original post on X — @datachaz

  • Secretary Krishnan Outlines India’s AI Priorities for 2026-2028

    Shri S. Krishnan, Secretary, @GoI_MeitY , outlines the key priorities for the next 2–3 years, with a strong focus on making AI resources more accessible, inclusive and scalable across sectors. A forward-looking vision to ensure that AI innovation reaches every stakeholder, from startups and researchers to government and industry. ▶️ Watch the full conversation: piped.video/NgfnFCaGeqI?si=6NHl… #IndiaAI #DigitalIndia #AIForAll #AIInfrastructure #Innovation NIC (@NICMeity) Shri S. Krishnan, Secretary, @GoI_MeitY, outlines key priorities for the next 2 to 3 years, with a strong emphasis on making AI resources accessible. Watch full conversation▶️ piped.video/NgfnFCaGeqI?si=6NHl… #NICMeitY #DigitalIndia #IndiaAIImpactSummit @SecretaryMEITY @OfficialINDIAai — https://nitter.net/NICMeity/status/2039251564670632123#m

    → View original post on X — @officialindiaai, 2026-04-01 08:26 UTC

  • Developing Transparent and Explainable AI Systems
    Developing Transparent and Explainable AI Systems

    Developing Transparent and Explainable #AI Systems
    by @antgrasso #ArtificialIntelligence #MachineLearning #ML #DL

    → View original post on X — @ronald_vanloon

  • AI Fundraising Milestone for Cancer Research Initiative

    Wow ma cagnotte pour #Medicbrain vient d'atteindre 1 million d'euros ! Je ne sais pas quoi dire, pour vous remercier. Vous êtes formidables. Merci pour vos partages et vos engagements et vos dons. On va pouvoir trouver des solutions contre les cancers avec l'IA. Je vais

    → Voir le post original sur X — @jessyseonoob