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. […]
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 […]
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 remarkable progress of Artificial Intelligence has revived an old idea with renewed force: that the human mind is essentially a highly complex computer and that, once we have enough data, enough computing power and sufficiently large models, we will eventually be able to reproduce a human being in a machine. This assumption sounds plausible […]
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 […]
A language model built for a specific linguistic community AMALIA is an important example of what it means to build an open language model not as a generic commercial product, but as a public research infrastructure for a specific linguistic community. Its central goal is clear: European Portuguese should not be treated as a secondary […]
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 […]