Catedrática afirma que há catedráticos que não fazem parte da comunidade científica

Num recente editorial da revista científica Genes, Genomes, Gentics, a editora-Chefe da mesma, catedrática da universidade da Florida (Scopus h-index=45) lembra aquilo que é, ou devia ser, mais ou menos óbvio, que as citações são a forma como reconhecemos aqueles que fazem parte da comunidade científica mundial. Resulta daqui que aqueles que não tem citações não fazem parte dessa comunidade, inclusive até mesmo catedráticos, sem citações.  https://academic.oup.com/g3journal/article/14/7/jkae102/7709035

PS - No contexto supra vale a pena recordar (ou revistar) o conteúdo do post do passado mês de Maio: "Nem todas as citações valem o mesmo: Há as que tem muito valor, as que tem valor nulo (auto-citações) e as de valor negativo" https://19-pacheco-torgal-19.blogspot.com/2024/05/nem-todas-as-citacoes-valem-o-mesmo-ha.html

The Economist - Predicting Environmental Collapses Before They Happen

Chinese researchers have demonstrated that artificial intelligence (AI) can accurately predict critical tipping points in complex systems, such as financial markets (e.g., the 2007-09 crisis), abrupt changes in ecosystems, floods, power outages, and many others. This breakthrough is detailed in a recent study published in Physical Review X and highlighted in the latest issue of The Economist.

In their study, the researchers utilized over two decades of satellite data on tree cover and precipitation in Central Africa to predict transitions from tropical forests to savannas. The AI algorithm identified that even a small decrease in annual precipitation could trigger a significant drop in tree cover, indicating a critical tipping point. This represents a significant advancement over previous studies, showcasing the potential of AI to predict critical events across various domains:

"The team then asked the algorithm to identify the conditions that drove the shift to savannah—or, in other words, to predict an oncoming phase transition. The answer was, as expected, down to annual rainfall. But the ai was able to go further. When annual rainfall dropped from 1,800mm to 1,630mm, the results showed that average tree cover dropped by only about 5%. But if the annual precipitation decreased from 1,630mm to about 1,620mm, the algorithm identified that average tree cover suddenly fell by more than 30% further. This would be a textbook critical transition. And by predicting it from the raw data, the researchers say they have broken new ground in this field. Previous work, whether with or without the assistance of ai, could not connect the dots so well. https://www.economist.com/science-and-technology/2024/07/17/ai-can-predict-tipping-points-before-they-happen

PS - In the same issue of The Economist, you'll find another compelling article titled "China as the West's Corporate R&D Lab: Can It Remain So?" To fully grasp the insights offered in this new piece, it's essential to consider the context provided by an earlier article from The Economist’s second week of June, which I previously analyzed in my post, "Tech Titans Clash: America vs. China in the Race for Innovation Supremacy" https://19-pacheco-torgal-19.blogspot.com/2024/06/tech-titans-clash-america-vs-china-in.html

The Economist – IA revoluciona a previsão de crises financeiras e outros eventos críticos em sistemas complexos

 

Investigadores chineses demonstraram que a inteligência artificial (IA) consegue prever com elevado rigor pontos de inflexão críticos (tipping point) em sistemas complexos, como os mercados financeiros (crise de 2007-09), as alterações abruptas nos ecossistemas, em inundações, ou em quebras de fornecimento de energia elétrica e muitos outros.  Vide estudo recente que foi publicado na Physical Review X, e que foi divulgado no último número da revista The Economist. 

Os referidos investigadores utilizaram mais de duas décadas de dados de satélite sobre cobertura arbórea e de precipitação naÁfrica Central, para tentar prever mudanças de florestas tropicais para savanas. O algoritmo de IA constatou que uma pequena diminuição na precipitação anual podia causar uma queda abrupta na cobertura arbórea, um ponto de inflexão crítico, o que representa um claro avanço que supera as limitações de estudos anteriores e destaca o potencial da IA para prever eventos críticos em diversas áreas:

"The team then asked the algorithm to identify the conditions that drove the shift to savannah—or, in other words, to predict an oncoming phase transition. The answer was, as expected, down to annual rainfall. But the ai was able to go further. When annual rainfall dropped from 1,800mm to 1,630mm, the results showed that average tree cover dropped by only about 5%. But if the annual precipitation decreased from 1,630mm to about 1,620mm, the algorithm identified that average tree cover suddenly fell by more than 30% further. This would be a textbook critical transition. And by predicting it from the raw data, the researchers say they have broken new ground in this field. Previous work, whether with or without the assistance of ai, could not connect the dots so well. https://www.economist.com/science-and-technology/2024/07/17/ai-can-predict-tipping-points-before-they-happen

PS - Na mesma revista há um outro artigo interessante de título "China is the West´s corporate R&D lab. Can it remain so ?", que deve ser lido tendo em conta aquilo que foi publicado na mesma revista, na segunda semana de Junho (sobre a ascenção da ciência chinesa) e que eu na altura comentei no post "Os políticos Portugueses são distraídos ou padecem de tacanhez mental profunda?" e também no post "Da Profunda Ignorância e Incompetência Portuguesa à Excelência Científica Chinesa"