Louis-Marie Lorin

It finally happened... This is how AI writes reviews better than humans   I guess it was only a question of time — and now a group of researchers has created it.   🔹 Cao and colleagues built an AI workflow that can screen papers, extract data, and run meta-analyses -> All of that in no time, with higher accuracy than humans, and without any special coding. What they did: Normally, it takes 2–3 researchers up to 365 days to write a Cochrane review! The AI arrives at similar conclusions for a systematic review in less than 2 days. And here’s the potentially scariest part - all the AI involved is freely available. 🔹 GPT-4.1 screens titles and abstracts 🔹 Gemini converts full-text PDFs into structured Markdown 🔹 o3-mini-high extracts quantitative data for the primary outcome → Then, the meta-analyses are run — matching the original statistical models.   What is striking: And in cases of disagreement with the original authors due to data interpretation, human judges sided more often with the AI than the authors! That is just staggering... = All in all, the AI is not perfect (see comments) but it did better than humans in terms of screening sensitivity and extraction accuracy. Meaning the final conclusions were pretty much identicial. It seems if Science is is getting its next upgrade, we won’t be able to tell whether a review was written by humans or AI. Are we truly ready for that? #STEM #AI #ScientificPublishing #Controversy #PhDLife IUBMB Trainee Initiative FEBS Junior Section Andrew Akbashev Ricfrid van der Marel

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We need to talk about how ChatGPT is shaping the way our brains work. MIT’s Media Lab just ran one of the first neuroscience studies on generative AI — and the results are concerning. The Study • 54 participants, ages 18–39 • Wrote SAT-style essays in three conditions: solo, with Google, or with ChatGPT • Brain activity tracked via EEG across 32 regions • Essays reviewed by educators for quality The Findings 1. Lowest brain engagement. ChatGPT users showed the weakest neural activity — consistently underperforming at neural, linguistic, and behavioral levels. 2. Memory loss in action. When asked to re-write essays without AI, most ChatGPT users couldn’t remember their own work. Brain waves tied to memory and creativity (alpha/theta) stayed low. 3. Declining effort. Over months, AI users got progressively lazier — many defaulted to “copy-paste” prompts and editing instead of writing. 4. Writing quality suffered. Teachers called their essays “soulless,” “formulaic,” and “lacking depth.” By contrast, the Google and solo groups wrote with more originality and reported higher satisfaction. 5. Critical thinking risk. Researchers warn heavy reliance on LLMs could weaken neural pathways that build resilience, memory, and creativity — especially for developing brains. Why It Matters Lead researcher Nataliya Kosmyna released the findings early, before peer review, because she feared policymakers might rush to integrate AI into education (“GPT kindergarten”) without understanding the risks. She argues: developing brains are most at risk. Other studies from the Media Lab have also linked heavy AI use to increased loneliness and reduced motivation. Early signals suggest the same patterns may hold in fields like software engineering — where critical thinking and problem-solving are essential. The Takeaway AI can boost speed and convenience. But the trade-off may be memory, depth, and engagement. The best strategy isn’t avoiding AI — it’s using it intentionally: • Start with your own thinking • Bring AI in later to polish or expand • Use AI as a tool, not a substitute for thought This is less about banning ChatGPT — and more about deciding what kind of thinkers we want to become. Read here: https://lnkd.in/erug8TSc #AI #Leadership #CriticalThinking #Neuroscience

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Matthieu Jonglez from Progress talking about how to use AI with #semantic #agentic #RAG on structured and unstructure data from a companies’s private systems “The greater the context, the greater the insights” #progressdatasummit

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GenAI impact assessment, 3 years later : Igor Carron CEO, portrait of LightOn started in 2016: from photonics for AI to start to building LLMs since 2021 now building sovereign foundational models. Simple, generic and repetitive tasks can easily be replaced by GenAI but people are expected to provide their expertise and will have more time to in the future. GenAI today portrait by MANGEANT Fabien Chief AI & Data Officer Air Liquide : not to replace people, but to augment people, and to improve operations. Yves Nicolas AI Group Program Director at Sopra Steria : GenAI is not magic, brings value in some use cases such as customer relationships or regulation gap analysis, but not everywhere. In European Trustworthy AI Association summit

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Did you know that AI models don't just hallucinate? They also produce what researchers call “Machine Bullshit” — convincing but untrue output. This includes: - Empty Rhetoric - Weasel Words - Paltering - Unverified Claims - Sycophancy In public health, this can mean fabricated research, wrong diagnoses, and biased algorithms. Once embedded in decision-making, these errors are far harder to detect than hallucinatory falsehoods. As public health leaders and practitioners, we need to implement technical safeguards, but the bigger fix is human vigilance and review. Accuracy in public health is non-negotiable. Read the full article here: https://lnkd.in/ekWUatjG

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