The question is not whether they “want” to deceive us When a large language model gives a false answer, it is not automatically lying. A hallucination is a generation error: the model produces an inaccurate statement because it predicts a plausible continuation without a reliable connection to reality. Deception is different. It is scientifically useful […]
Discovery requires more than an answer The fluency of a large language model can suggest that it is capable of conducting scientific discovery independently. Yet an autoregressive model operating without tools, observations or experiments has no mechanism for generating and testing new empirical evidence. It can formulate a hypothesis. Text generation alone cannot establish whether […]
From open weights to AI that can genuinely be inspected and rebuilt The word “open” is used so broadly in artificial intelligence that it risks losing its meaning. One lab publishes model weights, another releases inference code but not training code, and a third publishes a technical report while withholding the data pipeline. Each offers […]
Behind the anthropomorphic vocabulary are probabilities, vectors, and learned weighted averages A scientist who has spent decades studying nonlinear systems, modelling, statistics, or linguistics can open a modern paper on large language models and feel that an entirely new branch of mathematics has appeared. Query, key, value, attention, heads, embeddings, transformers. The impression is misleading. […]
The rapid development of Artificial Intelligence is making something increasingly visible that we often tend to overlook: the digital ecosystem has a very tangible physical foundation. AI models, cloud services and public digital applications run inside buildings filled with processors, storage systems, power supplies, networks, cooling loops and backup power systems. Data centers consume electricity […]
Why their value for the IT market lies in reuse, open standards and avoiding vendor lock-in In the IT sector, we know well that a successful prototype is not necessarily a sustainable information system. It may work perfectly in a controlled environment, yet fail when it has to integrate with existing infrastructure, be maintained for […]
The future of AI is not something that will simply happen to us. It is the result of choices about who designs technology, who owns it, who controls it, and to whom they are accountable. One of the most dangerous ways we talk about Artificial Intelligence today is as if it were a natural phenomenon. […]
The trap of the US-China AI race The American debate on artificial intelligence is increasingly shaped by one simple but dangerous sentence: if we slow down, China will overtake us. This sentence has become a political shortcut. It allows major technology companies and their allies to describe regulation, safety testing and public accountability as threats […]
A breakthrough, not a final destination Large language models have already changed how people write, code, search, translate, summarize, teach and organize knowledge. Their success rests on a powerful empirical insight: when models, data and compute grow together, new capabilities appear. This is the core intuition behind the scaling hypothesis, and it explains much of […]
Europe does not suffer from a lack of open source. It suffers from a lack of large-scale adoption. Across the European Union, the policy direction is already clear. Interoperability, reuse and cross-border public service improvement are now central to Europe’s digital agenda. Open source has also been increasingly linked to reduced dependency, stronger digital autonomy […]