⚡ L'ENTROPIE : LA LOI INVISIBLE QUI GOUVERNE LES SYSTEMES COMPLEXES
👉 La plupart des organisations combattent le désordre. Pourtant, la physique statistique montre qu’il ne s’agit pas d’une anomalie mais d’une conséquence naturelle des lois fondamentales qui gouvernent les systèmes complexes.

💡 L’entropie est souvent réduite à une idée vague de désordre. En réalité il s'agit d'une grandeur mathématique extrêmement précise.
Grâce à Boltzmann, l’entropie devient la mesure du nombre de configurations microscopiques compatibles avec l’état observable d’un système autrement dit, plus un système peut exister sous un grand nombre de configurations différentes, plus son entropie est élevée.

🌸 Un état parfaitement ordonné est théoriquement possible mais statistiquement extrêmement improbable.
Prenons un gaz dans une boîte. Dans les équations fondamentales, rien n’interdit que toutes les particules restent concentrées dans un seul coin. En pratique cela n’arrive jamais.

🧠 Pourquoi ? Parce que le nombre de configurations correspondant à un gaz uniformément réparti est immensément plus grand.
Le système évolue donc spontanément vers l’état le plus probable. Le désordre n’est pas une erreur du système mais la conséquence statistique de la multiplicité des possibles.

💡 Cette logique dépasse largement la thermodynamique.
Dans une entreprise, une organisation ou un système d’intelligence artificielle, maintenir une structure cohérente nécessite une dépense constante d’énergie
pour la coordination, la circulation de l’information, le contrôle, la correction des erreurs, l'alignement des objectifs.

⚡ Sans cela, tout système dérive progressivement vers une augmentation de son entropie : perte de signal, dilution des responsabilités, bruit informationnel, fragmentation décisionnelle.

🔬 La physique statistique révèle alors une idée profondément contre-intuitive.
L’ordre n’est pas l’état naturel des systèmes complexes. Il est une construction locale et temporaire maintenue uniquement grâce à un apport continu d’énergie et d’information.

🌎 Nous ne vivons pas dans un univers qui tend vers l’organisation mais dans un combat permanent où l’organisation lutte contre la probabilité.
#etudiant #science #mathematique #cpge #polytech #lycee #intelligence #universite #enseignement #pedagogie #education #philosophie #psychologie #directeur #manager #finance #art #spiritualite | 75 comments on LinkedIn

📊 Introducing the h* Index · Research Efficiency & Scientific Momentum

➡️ The classical h-index is widely used internationally as an indicator for assessing scientific impact by combining productivity and citation performance into a single metric.

➡️ However, research careers are dynamic by nature. Scientific activity evolves over time through new collaborations, emerging research directions, teaching commitments, leadership responsibilities, interdisciplinary projects, and many other forms of academic contribution. To better capture this evolving dimension of research activity, we developed the h* index, an extension of the traditional h-index that integrates temporal aspects of a researcher career. The h* index takes into account ⤵️

🔹 active publication periods
🔹 citation dynamics relative to career length
🔹 the continuity and momentum of scientific contributions over time

By incorporating these elements, the h* index provides a more dynamic and continuously updateable perspective on scientific impact and research activity.

➡️ Our objective is not to replace existing bibliometric indicators, but to complement them with an additional tool capable of offering a broader and more nuanced understanding of academic trajectories and research evolution. We believe that combining robustness, adaptability, and temporal analysis can contribute to more informed research evaluation and support decision-making processes within academic and scientific institutions.

📢 “An indicator does not say who you are, it says what others have remembered about you. The h or h* index does not measure the truth, it measures how many times an idea has managed to survive through time without fading into oblivion.”
Citation : Prof. Alexis Rusinek

📢 "Wskaźnik nie mówi, kim jesteś mówi, co inni o tobie zapamiętali. Czynnik h czy h* nie sprawdza prawdziwości, lecz pokazuje, ile razy dana myśl zdołała utrzymać się w obiegu, zamiast zniknąć w zapomnieniu."
Polish translation by Prof. Tomasz Jankowiak

🌐 If you would like to explore and visualize the evolution of your own scientific trajectory over time, you can now test it through our platform.

🌐 https://lnkd.in/euRk2xMK

Aurélien Besnard #Tomasz_Jankowiak

#Research #Scientometrics #Bibliometrics #Innovation #Academic_Research #Data_Science #Higher_Education #Science #Research_Evaluation

AI in medical education may be dangerous not only when it is wrong: it may be dangerous when it is "plausibly" wrong.

A very interesting study published in npj Digital Medicine examined the impact of AI-generated explanations on novice medical students, comparing correct explanations, misleading explanations, and no explanations.
The result is important because it touches one of the most delicate aspects of AI in medicine: the persuasive power of a well-structured explanation.
In clinical reasoning, an error that appears obviously wrong can often be recognized, questioned, and rejected. But an error that is coherent, fluent, and medically sophisticated is much more difficult to resist, especially for learners who have not yet developed strong internal models of disease.

Medical education is not only about reaching the correct answer: it is about learning how to evaluate the path that leads to that answer. If AI provides a wrong conclusion with a convincing explanation, the risk is not only diagnostic error. The deeper risk is that the learner may begin to trust the structure of reasoning without having the expertise to detect where that reasoning fails.

This is why AI literacy cannot simply mean knowing how to use AI; it must mean knowing how to interrogate AI.

Students and trainees will need to learn not only how to obtain explanations from artificial intelligence, but how to deconstruct them, compare them with biological plausibility, identify hidden assumptions, and recognize when confidence is not justified.
The future of medical education will not depend only on giving students access to better tools: it will depend on teaching them how to remain intellectually independent in front of tools that sound increasingly authoritative.
AI can support learning but it must not become a machine that teaches confidence before competence.
#ArtificialIntelligence #Medicine #MedicalEducation #ClinicalReasoning #DigitalHealth #MedicalAI #PatientSafety #FutureOfMedicine | 11 comments on LinkedIn

Today on Indicator: A handful of anonymous bots have taken over fact-checking on X.

This isn’t hyperbole. In the first three weeks of May, just eight AI contributors wrote 50.3% of all visible Community Notes on the platform.

Community Notes was supposed to bring scale and legitimacy to the fight against misinformation by empowering users to participate in fact-checking. Now that power is increasingly in the hands of a tiny group of hobbyists and researchers.

AI contributors aren’t just replacing the humans who are supposed to make up the community in Community Notes. They are publishing in languages and on topics that reflect the bias of their algorithmic setup.

My analysis suggests, for example, that AI supercontributors focus less on politicians and hyperpartisan accounts. This may be because they are optimizing for a bridging algorithm that prioritizes broad consensus over factuality.

For good and for bad, X is currently running the world's largest live experiment in automated fact-checking.

What happens next will matter not just for X, or for the copycat Community Notes features on Meta and TikTok. How it fares may define platform interventions against misleading content for years to come.

This 3,634-word article took a lot of time to report. To paraphrase Jeb Bush, please read: https://lnkd.in/ejBU9uAE

🧠 AI in Medical Training: Learning Tool or Shortcut?

A new Nature Medicine Perspective raises a cautious but important concern: “never-skilling” in medical education. The idea is distinct from deskilling, that may happens when experienced clinicians lose skills they already developed. Never-skilling would happen earlier: during formative training, when learners may fail to build foundational clinical reasoning.

→ AI is not i harmful to learning, however its effect depends on how and when it is introduced.

→ Answer-delivery AI may carry different risks from learning-mode AI.
A system that gives diagnoses directly may bypass cognitive effort, while a system that asks Socratic questions and gives feedback may support reasoning.

→ Assessment needs to change, as trainees are learning in AI-rich environments.

Caveats: direct causal evidence in clinical trainees is still lacking. This is a risk model and research agenda, not a proven phenomenon.

👉 How do we leverage AI as a learning accelerator without turning it into a shortcut around the skills physicians still need to master?

Ref: Ke et al Nan Liu, PhD, FAMIA . AI-induced never-skilling in medical education. Nature Medicine, 2026.

🌟 The Heuristic Method: Transforming Classrooms Through Discovery-Based Learning 🌟

Education is evolving beyond traditional teaching methods where students simply listen, memorize, and reproduce information. In today’s dynamic world, learners need opportunities to think critically, ask meaningful questions, solve real-world problems, and take ownership of their learning journey. This is where the Heuristic Method becomes a powerful approach in modern education.

The Heuristic Method focuses on learning through discovery and experience. Instead of directly giving answers, educators create opportunities for students to investigate, experiment, explore, and arrive at conclusions independently. This approach nurtures curiosity and encourages learners to become active participants rather than passive receivers of knowledge.

When students are encouraged to discover concepts on their own, learning becomes deeper, more engaging, and long-lasting. They develop essential 21st-century skills such as creativity, collaboration, critical thinking, and confidence — skills that prepare them not just for exams, but for life.

📚 Key Principles of the Heuristic Method:
✔️ Explore & Experiment
✔️ Problem Solving
✔️ Self-Learning
✔️ Critical Thinking

💡 Benefits for Learners:
✨ Sparks Curiosity
✨ Enhances Creativity
✨ Builds Confidence
✨ Encourages Independence
✨ Promotes Lifelong Learning

👩‍🏫 Role of the Teacher:
In a heuristic classroom, the teacher becomes a facilitator of learning by:
🔹 Guiding students through inquiry
🔹 Encouraging exploration and discussion
🔹 Supporting individual learning journeys
🔹 Providing meaningful resources and experiences

🛠️ Simple Steps to Implement:
1️⃣ Pose an engaging question
2️⃣ Encourage investigation and exploration
3️⃣ Allow students to discover possible solutions
4️⃣ Reflect, discuss, and connect learning experiences

The most meaningful learning happens when students are empowered to:

🌱 THINK • EXPLORE • DISCOVER • LEARN 🌱

#Education #Teaching #Learning #HeuristicMethod #InquiryBasedLearning #StudentCenteredLearning #CriticalThinking #Creativity #TeachersOfLinkedIn #EducationMatters #FutureReady

🧠 Why do students forget lessons so quickly?
The issue is not always intelligence or ability.

Often, the problem is that learning is happening in ways the brain was never designed to retain effectively.

Today’s classrooms cannot rely only on:
❌ Long lectures
❌ Passive note-taking
❌ Memorization without understanding
Instead, research consistently shows that students remember more when learning is:
✨ Active
✨ Emotional
✨ Visual
✨ Collaborative
✨ Reflective
This infographic explores practical brain-based strategies teachers can use to improve student retention and create more meaningful learning experiences.
From retrieval practice and storytelling to visual learning and reflection, small instructional shifts can make a huge difference in how students understand and retain information.

💡 Great teaching is not about delivering more content—it is about helping learning last.
Which strategy do you think has the biggest impact on student retention in today’s classrooms? 👇

🎨 Designed by Neha Khan

#Education #BrainBasedLearning #StudentEngagement #TeachingStrategies #LearningScience #TeachersOfLinkedIn #ActiveLearning #FutureReadyEducation #ClassroomInnovation #LearningMatters

AI in medicine may not only change how doctors work but how future doctors learn to think.

A very interesting Perspective just published in Nature Medicine introduces the concept of “AI-induced never-skilling” in medical education.
The idea is important because for many years, the discussion around AI in medicine has focused mainly on experienced clinicians: whether AI will support them, replace parts of their work, improve accuracy, reduce workload, or create new risks of deskilling.

But medical education raises a different and perhaps even more delicate question: what happens if students and trainees begin to rely on AI before they have developed the foundational reasoning skills that safe independent practice requires?

Deskilling means losing a skill that was previously acquired. Never-skilling means that the skill may never fully develop. Clinical reasoning is not built only by receiving correct answers. It is built through uncertainty, effort, comparison, mistakes, supervision, feedback, and the slow construction of judgment. If AI removes too much of this cognitive struggle too early, it may not simply make learning easier....it may make learning shallower.

This does not mean that AI should be excluded from medical education.
On the contrary, future physicians must learn how to use AI critically, safely, and intelligently.
But timing matters. Pedagogy matters. Supervision matters.
Before AI becomes a routine cognitive companion for trainees, we need to make sure that human clinical reasoning has had enough space to form.
The future of medicine will not depend only on doctors who know how to use AI.
It will depend on doctors who can still think without it.

#ArtificialIntelligence #Medicine #MedicalEducation #ClinicalReasoning #DigitalHealth #MedicalAI #HealthcareInnovation #FutureOfMedicine | 26 comments on LinkedIn

Quand un schéma vaut bien une longue longue longue et complexe explication !

1 - Expert en IA ... mais à quel niveau ?

2 - Besoin en IA ... à quel niveau ?

Croiser 1 et 2 pour clarifier votre usage et votre ... budget 💰💰💰

Et toujours le même principe : pas d'IA sans IH !

Jean-Pierre Armand, MD Henri Tsiang

Google just rebuilt Search to answer you instead of pointing you somewhere — the biggest change to the search box in 25 years. It now picks which handful of sources its answer rests on, before you ever see the links.
If your work depends on knowing whether an answer is true — journalists, academics, analysts, lawyers, fact-checkers, students — that should worry you. The good news: you can take the choice back.
I spent this week writing down how. What changed at I/O, and the toolkit I fall back on: the seven search operators that cut sharper now than ever (site:, filetype:, intext: and friends), and the &udm=14 trick that strips the AI off the page.
None of it requires being technical. It's all in the post. What's your move when the answer comes pre-chewed?


https://lnkd.in/eDXg3nkT

What "sample size is needed" for 10 different types of qualitative research -- new article by Wutich et al. Useful overview for those who value guidelines for saturation.
Wutich, A., Beresford, M., & Bernard, H. R. (2024). Sample sizes for 10 types of qualitative data analysis: an integrative review, empirical guidance, and next steps. International Journal of Qualitative Methods, 23, 16094069241296206.
Open-access article available here. https://lnkd.in/gmQd3RTJ | 70 comments on LinkedIn

An interesting opinion piece in the NYTimes.

Helen Ouyang is an ER doctor and Columbia professor. She typed her lab results into ChatGPT because her physician told her a phone call would require another appointment. That's what she wrote!

She is not a credulous patient who doesn't know better. She knows exactly what a chatbot can and cannot do. She used it anyway, and it worked. Her numbers improved. She credits the sustained back-and-forth, the patience, the absence of judgment.

She writes: "With low expectations, I typed my lab results into ChatGPT.
As both a physician and a patient, I found the experience startling. Not because ChatGPT dazzled me with its scientific knowledge, but because it behaved the way I wish modern medicine, and its practitioners, still would."

These last few words should stop every physician who reads them.

The usual framing treats patients using AI as a problem to manage, a liability to disclaim, a behavior to correct with the right guardrails. Ouyang's piece quietly dismantles that. The chatbot did not win because it knew more. It won because it had time. It asked follow-up questions. It remembered what she said five exchanges ago. It never seemed annoyed.

Those are not AI capabilities. Those are human capabilities that the current system has systematically squeezed out of clinical encounters.

There is a structural argument buried in her essay that she does not quite surface: patients are not going to AI because AI is good. They are going to AI because the alternative has been stripped of the things that make medicine work. Availability. Continuity. The permission to ask the same question twice.

Her proposed response is cautious and right as far as it goes: figure out how to support patients using AI tools, with clear guardrails.

For me, this is the judgment layer argument made visible from the physician side. Ouyang knows when to override the chatbot. Most patients do not. That asymmetry is not a reason to condemn AI use, it is the exact design problem we need to solve. The question is not whether patients will use AI. They will. The question is whether they will use it with enough contextual grounding to know what to do when it gets something wrong.

The Ouyang piece will be cited as evidence that AI is replacing doctors. That is the wrong read. It is evidence that something in medicine stopped working long before AI arrived, and AI is filling the vacuum.

Her last paragraph tells the whole story: "My experience with the chatbot has already shifted how I interact with patients in the E.R., with only minutes to piece together fragments of their circumstances. When a patient asks the same question repeatedly, I try to listen for what’s behind it. Maybe she’s not after more medical facts."

Dave deBronkart Hugo Campos Sara Riggare Jane Sarasohn-Kahn

#PatientsUseAI #CriticalAIHealthLiteracy
| 10 comments on LinkedIn