XR Belgium

Les systèmes d’intelligence artificielle générative, qui parlent si bien, ne comprennent pas encore le monde. De nouvelles méthodes physiques ou statistiques comme les world models, ou « modèles de monde », permettraient de les doter d’une forme de sens commun, qui leur servirait à mieux simuler la réalité et de mieux interagir avec elle.

States and financial bodies using modelling that ignores shocks from extreme weather and climate tipping points

Climate models that give a low warming from increases in greenhouse gases do not match satellite measurements. Future warming will likely be worse than thought unless society acts, according to a new study published in Science.

Real world measurements of how much extra heat the Earth is trapping are well beyond most climate models. That’s a real problem.

Global warming is moving faster than the best models can keep a handle on.

EPFL scientists developed a tool to evaluate climate models, revealing that some predict a much hotter future due to high carbon sensitivity, suggesting current emission reduction efforts may be inadequate.

Taking into account all known factors, the planet warmed 0.2 °C more last year than climate scientists expected. More and better data are urgently needed. Taking into account all known factors, the planet warmed 0.2 °C more last year than climate scientists expected. More and better data are urgently needed.

A major reason for the growth in the use of renewable energy is the fact that if a person looks at them narrowly enough--such as by using a model--wind and solar look to be useful. They don't burn fossil fuels, so it appears that they might be helpful to the environment. Energy modeling misses important points. I believe that profitability signals are much more important.

We investigate the potential implications of large language models (LLMs), such as Generative Pretrained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifications. Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. We do not make predictions about the development or adoption timeline of such LLMs. The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Significantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15%