Chinese open models are spreading fast, from Hugging Face to Silicon Valley. In this photo illustration, the DeepSeek apps is seen on a phone in front of a flag of China on January 28, 2025 in Hong Kong, China. The past year has marked a turning point for Chinese AI. Since DeepSeek released its R1 reasoning model in January 2025, Chinese companies have repeatedly delivered AI models that match the performance of leading Western models at a fraction of the cost. Just last week the Chinese firm Moonshot AI released its latest open-weight model, Kimi K2.5, which came close to top proprietary systems such as Anthropic's Claude Opus on some early benchmarks. The difference: K2.5 is roughly one-seventh Opus's price.

We are grateful to the reviewers for their insightful and constructive comments. This is consistent with the methods of Refs. The "CNN dataset" is adapted from that used in Ref. [14], which we supplement with words from Spoken Wikipedia Corpus (SWC) to diversify the word instances and provide more balanced speaker classes for the speaker trained model.

We thank all reviewers for their encouraging and constructive feedback. The scores determine the order in which nodes are fused. The reviewer is correct that each Transformer layer in Fig.3 only outputs a feature embedding that is In Eq.2, the actions are produced by the final layer in Fig.3 is an illustration of The yellow part in Fig.4 is the same as Fig.3. We will add more detailed explanations about GNN's limitations on tracking global node dependencies. Y es, GO can be trained in an on-line scenario similar to Decima.

Latam-GPT is new large language model being developed in and for Latin America. The project, led by the nonprofit Chilean National Center for Artificial Intelligence (CENIA), aims to help the region achieve technological independence by developing an open source AI model trained on Latin American languages and contexts. "This work cannot be undertaken by just one group or one country in Latin America: It is a challenge that requires everyone's participation," says Álvaro Soto, director of CENIA, in an interview with WIRED en Español. "Latam-GPT is a project that seeks to create an open, free, and, above all, collaborative AI model. We've been working for two years with a very bottom-up process, bringing together citizens from different countries who want to collaborate. Recently, it has also seen some more top-down initiatives, with governments taking an interest and beginning to participate in the project."

We are grateful to the reviewers for their insightful and constructive comments. This is consistent with the methods of Refs. The "CNN dataset" is adapted from that used in Ref. [14], which we supplement with words from Spoken Wikipedia Corpus (SWC) to diversify the word instances and provide more balanced speaker classes for the speaker trained model.

OpenAI is taking on Mark Zuckerberg's Meta and Chinese rival DeepSeek by launching its own freely available artificial intelligence models. The ChatGPT developer has announced two "open weight" large language models, which are free to download and can be customised by developers. Meta's Llama models are available on a similar basis, and OpenAI's move marks a departure from ChatGPT, which is based on a "closed" model that cannot be customised. Sam Altman, OpenAI's chief executive, said the company was excited to add to a stack of freely available AI models "based on democratic values … and for wide benefit". He added: "We're excited to make this model, the result of billions of dollars of research, available to the world to get AI into the hands of the most people possible." OpenAI said the models could underpin an AI agent that operates autonomously, and that they were "designed to be used within agentic workflows".

Mark Zuckerberg was supposed to win the AI race. Eons before ChatGPT and AlphaGo, when OpenAI did not exist and Google had not yet purchased DeepMind, there was FAIR: Facebook AI Research. In 2013, Facebook tapped one of the "godfathers" of AI, the legendary computer scientist Yann LeCun, to lead its new division. That year, Zuckerberg personally traveled to one of the world's most prestigious AI conferences to announce FAIR and recruit top scientists to the lab. FAIR has since made a number of significant contributions to AI research, including in the field of computer vision.

Large Language Models (LLMs) have become central in academia and industry, raising concerns about privacy, transparency, and misuse. A key issue is the trustworthiness of proprietary models, with open-sourcing often proposed as a solution. However, open-sourcing presents challenges, including potential misuse, financial disincentives, and intellectual property concerns. Proprietary models, backed by private sector resources, are better positioned for return on investment. There are also other approaches that lie somewhere on the spectrum between completely open-source and proprietary. These can largely be categorised into open-source usage limitations protected by licensing, partially open-source (open weights) models, hybrid approaches where obsolete model versions are open-sourced, while competitive versions with market value remain proprietary. Currently, discussions on where on the spectrum future models should fall on remains unbacked and mostly opinionated where industry leaders are weighing in on the discussion. In this paper, we present a data-driven approach by compiling data on open-source development of LLMs, and their contributions in terms of improvements, modifications, and methods. Our goal is to avoid supporting either extreme but rather present data that will support future discussions both by industry experts as well as policy makers. Our findings indicate that open-source contributions can enhance model performance, with trends such as reduced model size and manageable accuracy loss. We also identify positive community engagement patterns and architectures that benefit most from open contributions.

I'm glad to hear that you are going to open source the optimizations; I look forward to playing with this. Best of luck with the follow-up work, and I look forward to seeing how the RCRN performs on SNLI in the cross-attention task setting (really hoping to see this in the camera-ready!). Original Review: The core idea behind this paper is to use RNNs (LSTMs or GRUs in this work) to compute the gates (input, forget, etc.) for a higher level RNN. They use this idea to show how to improve performance on a large number of tasks (mostly classification) with relatively simple models that have little to no loss in efficiency compared to models that perform similarly. Some of the performances achieved new state-of-the-art results.