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 […]
From hallucinations to AI systems that can act The reliability debate around Large Language Models initially focused on hallucinations: plausible answers containing fabricated facts, sources or numbers. AI agents substantially change the nature of the problem. An agent does not merely generate text. It may search the web, call APIs, execute code, access files, query […]
AI is becoming infrastructure, not just a service For Greek universities, research centers and companies developing artificial intelligence, the strategic question is no longer simply which large language model performs best today. A more important question is what technological foundation they want to depend on, understand and be able to modify in the years ahead. […]
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 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. […]
From WiFi4EU to public connectivity infrastructure that remains open, controllable and sustainable Municipal and community wireless networks are no longer an experimental technology used by a small number of networking enthusiasts. They have become essential digital infrastructure for public squares, libraries, schools, cultural venues, health centres, tourist areas and remote communities. The European WiFi4EU initiative […]
How software teams turn experience into shared, open and controllable knowledge infrastructure The first phase of generative Artificial Intelligence adoption in software development focused heavily on prompt engineering. The basic assumption was that a skilled user, by formulating a request correctly, could obtain better code, more complete documentation or more accurate technical analysis. This assumption […]
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 […]