Large language models (LLMs) have recently become a popular topic in the field of Artificial Intelligence (AI) research, with companies such as Google, Amazon, Facebook, Amazon, Tesla, and Apple (GAFA) investing heavily in their development. These models are trained on massive amounts of data and can be used for a wide range of tasks, including language translation, text generation, and question answering. However, the computational resources required to train and run these models are substantial, and the cost of hardware and electricity can be prohibitive for research labs that do not have the funding and resources of the GAFA. In this paper, we will examine the impact of LLMs on AI research. The pace at which such models are generated as well as the range of domains covered is an indication of the trend which not only the public but also the scientific community is currently experiencing. We give some examples on how to use such models in research by focusing on GPT3.5/ChatGPT3.4 and ChatGPT4 at the current state and show that such a range of capabilities in a single system is a strong sign of approaching general intelligence. Innovations integrating such models will also expand along the maturation of such AI systems and exhibit unforeseeable applications that will have important impacts on several aspects of our societies.

翻译：大型语言模型（LLMs）近期已成为人工智能（AI）研究领域的热门话题，谷歌、亚马逊、脸书、亚马逊、特斯拉和苹果（GAFA）等公司正大力投资其开发。这些模型基于海量数据训练，可用于语言翻译、文本生成和问答等多种任务。然而，训练和运行这些模型所需的计算资源相当可观，对于缺乏GAFA级别资金和资源的研究实验室而言，硬件和电力成本可能难以承担。本文旨在探讨LLMs对AI研究的影响。这些模型的生成速度及其覆盖的领域范围，反映了当前公众乃至科学界所经历的趋势。我们以当前状态的GPT3.5/ChatGPT3.4和ChatGPT4为例，展示如何在研究中运用此类模型，并表明单个系统具备如此广泛的能力，是接近通用智能的强烈信号。随着此类AI系统的成熟，融合这些模型的创新也将不断拓展，催生难以预见的应用，对我们的社会多个层面产生重要影响。