5 decks to anchor the essential scientific and technological reference points: AI and data, energy and climate, digital technology and cybersecurity, major innovations, life sciences. Spaced repetition turns jargon into solid knowledge.
Each deck covers a major scientific or technological domain. You can tackle them in any order depending on your professional priorities or areas of curiosity.
Machine learning, deep learning, LLMs, training data, algorithmic bias, generative AI — the concepts that let you read AI news without getting lost. Not a data science course: the reference points every informed decision-maker and citizen should have.
View deck →Climate change, renewable and fossil energy sources, energy mix, carbon neutrality, international agreements. The concepts for understanding debates on the energy transition without getting lost in the numbers.
View deck →Internet protocols, cloud, cybersecurity, personal data, digital sovereignty, platform regulation. The reference points for understanding digital issues in public debate — GDPR, cyberattacks, tech monopolies.
View deck →Blockchain, 3D printing, robotics, space, semiconductors, quantum — the innovations reshaping economic and geopolitical balances. Understanding why microchips became a geopolitical issue.
View deck →Genomics, CRISPR, mRNA vaccines, microbiome, epidemiology, One Health. The life science concepts that structured public debate since the Covid-19 pandemic — and will continue to do so.
View deck →The problem with scientific and technological literacy isn't lack of interest. It's volume and speed. You read an article on generative AI, understand it in the moment, and three weeks later you can no longer explain the difference between an LLM and a classical machine learning algorithm.
Scientific concepts need to be encountered multiple times, in different contexts, to stick. That's exactly what spaced repetition does: it re-presents each concept at the moment it starts to fade, varying the angles to create durable anchoring.
These 5 decks won't make you a data scientist or nuclear physicist. They give you the vocabulary and reference points to understand public debates on tech, energy, climate and life sciences — and to stop getting lost in jargon.
Preparing for an interview touching on tech? Start with 'AI and Data'. Following debates on the energy transition? With 'Energy and Climate'. Each deck is independent and can be tackled alone. Progression isn't linear — it follows your priorities.
In science and technology, what fades fastest are the conceptual distinctions: deep learning vs machine learning, carbon neutrality vs net zero, virus vs bacterium vs prion. The FSRS algorithm identifies the distinctions still fragile in your memory and re-presents them at exactly the right moment.
Once reference points are anchored, reading specialized press becomes radically different. An article on Nvidia chips, a debate on the EU AI Act, an announcement on nuclear SMRs — all become readable and debatable. The goal: move from confusion to comprehension.
No. These decks are designed for non-specialists who want solid scientific and technological literacy — managers, journalists, decision-makers, humanities students, curious citizens. Concepts are explained in accessible language, with the distinctions that matter for understanding public debate.
Yes. Competitive general knowledge exams, business schools and some international relations programs include questions on scientific culture and technology. The decks cover the most frequently tested themes: AI and data, energy transition, digital issues, disruptive innovations.
The decks cover fundamental concepts and structural issues — not the latest AI models or the newest startups. These foundational reference points evolve slowly. Decks are updated regularly to integrate major new regulations and innovations that shift the landscape.
The 'AI and Data' deck isn't technical training. It's a conceptual toolkit for understanding the stakes, reading the news and debating AI intelligently — not for coding models. If you want to learn to program algorithms, you'll need technical training in addition.
Both work. Doing decks in parallel allows cross-domain connections (e.g., AI consumes energy, energy transition requires materials innovations). Completing them sequentially allows progressive deepening. The FSRS algorithm automatically manages your reviews regardless of your approach.
First deck accessible without a credit card. In 15 minutes a day, you build solid and lasting scientific literacy.
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