Nature

The 18 September message, which was jointly completed by the DeepSeek team and published as a communication author by Liang Wenfeng, contained a DeepSeek-R1 research paper on the reasoning model, on the cover of issue 645 of the international authoritative journal Nature. Compared to the first edition of DeepSeek-R1 published in January this year, this paper reveals more details of model training. DeepSeek-R1 is also known to be the first globally peer-reviewed dominant language model. Nature evaluates that almost all mainstream models..

Have you ever thought that your research papers may have been used to train AI? That's right, many academic publishers are "packaging and selling" their results to technology companies that develop AI models. There is no doubt that this move has caused quite a stir in the research community, especially when authors are unaware of it. Experts say that if your work has not yet been used by a large language model (LLM), it is likely to be "used" in the near future. Recently, British academic publisher Taylor & Francis reached an agreement with Microsoft...

In the fierce competition of artificial intelligence, who is the real leader? Recently, a new report from the PARAT database has unveiled the mystery of the global AI industry and presented an amazing competitive landscape. This data, which was highlighted by Nature magazine, not only subverts our inherent cognition, but also shows us the latest development trends in the field of AI. In one corner of this arena, Alphabet (Google's parent company) and Microsoft, two technology giants in the United States, stand out from the crowd with their absolute advantage in the number of citations of AI papers. At the other end of the arena, China...

Google recently published a research result in Nature, saying that with the power of AI, it can accurately predict floods as early as 7 days in advance, hoping to provide reliable flood prediction services to residents in more than 80 countries and regions around the world. Flood prediction has always been a difficult problem because most rivers do not have flow meters. Google solved this problem by training machine learning models with various relevant data (including historical events, river water level readings, altitude and terrain readings, etc.). Google used the model to generate localized maps and performed hundreds of thousands of simulations at each location to accurately…