From linear algebra and statistical learning to attention mechanisms and GPU computing, understanding the mathematical foundations of modern language models. Language as a mathematical problem How can a computer produce scientific explanations, translate documents, write software, and answer questions in natural language? The underlying mechanism is neither mysterious nor based on a conventional database of […]
In less than a year, the debate over data centres in the United States has changed venue. It left investor presentations, entered city council chambers and, more and more often, reached the ballot box. In June, voters in Monterey Park, California, approved a permanent ban on new data centres with 86% of the vote. In […]
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 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. […]
Why the extraordinary expectations surrounding large language models have yet to show up in the economic data Large language models may be the fastest-diffusing digital technology in decades. Within only a few years, they have become everyday tools for hundreds of millions of people, entered enterprise software and triggered extraordinary investment in chips, data centers, […]
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 […]
The remarkable behaviour of language models makes machine consciousness a legitimate scientific question. It does not justify confusing linguistic competence, internal computation or self-reference with subjective experience. Large language models can discuss fear, pain, love, uncertainty and even their own supposed internal states. They can write “I think”, “I feel” or “I am afraid” with […]
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. […]