domingo, 5 de abril de 2026
Uma "brilhante" estratégia Portuguesa - Empobrecer orgulhosamente ao mesmo tempo que ajuda ricos a ficarem mais ricos
sábado, 4 de abril de 2026
The Portuguese Researcher, the Chinese Secret Service, and the Hidden Agenda
Despite having praised China on a number of occasions — as illustrated, for example, here, or much earlier in this post here, or more recently in a series of posts that might, perhaps understandably, give some readers the impression that I was actively trying to steer European researchers in the direction of China, such as this one https://19-pacheco-torgal-19.blogspot.com/2026/03/open-positions-with-highly-competitive.html
I feel it is important to state clearly and unambiguously that this was never my intention, nor has it ever been. If anything, the reality is quite the opposite. Rather than encouraging researchers from Portugal or elsewhere in Europe to relocate to China, I have consistently made a point of doing the reverse: actively encouraging Chinese researchers to explore and pursue the many professional opportunities that Europe has to offer. This is perhaps best illustrated by the email exchange reproduced below, which took place with a young researcher affiliated with Hong Kong Polytechnic University, whose name has been redacted for privacy reasons.
My conviction on this matter is unshakeable: scientific talent should be free to go wherever it finds the best conditions to flourish, unimpeded by geography, bureaucracy, or the drag of underfunded systems. There is no hidden agenda here — only the straightforward belief that the world functions far better when its brightest minds are empowered rather than constrained. Let us be entirely candid: countless scientists, many of whom labor in near-total obscurity, produce value for society on a scale that dwarfs the achievements of the average professional footballer. Yet, astonishingly, a great many of these same researchers have quietly accepted earning only a fraction of what a mid-tier player commands — as though this glaring disparity were natural, morally acceptable, or somehow inevitable. It is none of those things. It is, instead, a failure of priorities on a civilizational scale — a failure that diminishes not only science but the very societies that rely on it. And it is a failure we can no longer afford to tolerate in silence. https://19-pacheco-torgal-19.blogspot.com/2026/02/in-defense-of-high-salaries-paying-for.html
Enviado: 3 de abril de 2026 07:41
Para: AAA
Assunto: RE: Paper comments
Enviado: 2 de abril de 2026 13:53
Para: F. Pacheco Torgal
Assunto: RE: Paper comments
Dear Prof. Torgal,
Thank you very much for the valuable practical suggestions.
I will refine my CV by incorporating your suggestions. More importantly, I will start exploring the job possibilities you recommended.
Thank you again and I will come back to you when I have a new publications that would be of interest to you.
Best regards,
AAA
quarta-feira, 1 de abril de 2026
Cracking the AI Mirror: Discovering the Unimaginable Boundaries of Science
What if the next major scientific breakthrough comes not from a human researcher pushing the limits of their field, but from a machine that doesn’t even know where those limits are supposed to be?
That is the provocative question at the heart of the recent publication. The researchers behind "Alien Science" started from a simple but powerful observation. When you ask a language model like ChatGPT to brainstorm research ideas, it tends to give you things that already feel familiar — polished-sounding variations on what everyone is already working on. They are trained on human-produced text, so they reflect human patterns of thinking back at us. They are, in a sense, a very expensive mirror. https://arxiv.org/abs/2603.01092
The team decided to break that mirror deliberately. They fed around 7,500 recent machine learning papers into their system and broke each one down into small conceptual building blocks they call idea atoms — things like a specific technique, a training trick, or a particular way of evaluating a model. Then they trained two separate models: one that learns which combinations of these atoms actually make sense together, and one that learns which combinations a typical researcher would think of. The trick is in what comes next. The system searches specifically for combinations that are coherent but that no one would naturally propose. Ideas that work on paper but live in the gap between research communities. Ideas, in other words, that are alien to the current scientific conversation. When they tested it, the system produced research directions that were significantly more varied and unexpected than anything a standard AI assistant would suggest — while still being technically sound.
P.S - Before getting too carried away with what AI might one day discover, it is worth pausing on a warning from two Google researchers, including Turing Award winner David Patterson. The real crisis in AI right now is not about building smarter models it is about running them. Every time someone uses one of these systems, the computational cost is determined by inference, the moment-to-moment work of generating a response in real time, and that process is straining under the weight of everything we are asking it to do. The AI infrastructure is being asked to perform beyond its capacity, risking catastrophic slowdowns, skyrocketing energy costs, and a technological bottleneck that could stall progress itself. The urgent question is: how much can we really make AI do before the system collapses under its own weight? https://arxiv.org/abs/2601.05047