Article Introduction：Incredible, GPT-4 has learned to do scientific research on its own? Recently, several scientists from Carnegie Mellon University published a paper, which simultaneously shocked the AI ​​and chemistry circles. They have created an AI that can conduct experiments and conduct scientific research on its own. This AI is composed of several large language models and can be regarded as a GPT-4 agent agent with explosive scientific research capabilities. Because it has long-term memory from vector databases, it can read, understand complex scientific documents, and conduct chemical research in a cloud-based robotic laboratory. Netizens were so shocked that they were speechless: So, this is done by AI itself and then published by itself? Oh my god. Some people lamented that the era of "Tennis Experiment" (TTE) is coming! Could this be the legendary AI Holy Grail in the chemical world? Probably a lot recently

2023-04-17 comment 0  1543

Article Introduction：[CNMO News] On November 14, according to Xinhua News Agency, the team of professors Luo Yi, Jiang Jun, and Shang Weiwei of the University of Science and Technology of China recently cooperated with researcher Zhang Zhe of the Deep Space Exploration Laboratory and others to use the intelligent robot "Machine Chemist" , successfully developed a new catalyst using Martian meteorites, providing a high-efficiency, low-energy solution for using water on Mars to produce oxygen, and exploring a new way to develop chemicals using local materials in galaxies outside the Earth. Today, the internationally renowned academic journal Nature Synthesis published this research result. According to reports, researchers from the University of Science and Technology of China and the Deep Space Exploration Laboratory collaborated to use their self-developed intelligent robot "Machine Chemist" to analyze and extract components from Martian meteorites, successfully

2023-11-14 comment 0  825

Article Introduction：Editor | Ziluo's traditional material research and development model mainly relies on "trial and error" experimental methods or accidental discoveries, and its research and development process usually takes 10-20 years. Data-driven methods based on machine learning (ML) can accelerate the design of new materials for clean energy technologies. However, its practical application in materials research is still limited due to the lack of large-scale high-fidelity experimental databases. Recently, research teams from the Pacific Northwest National Laboratory and Argonne National Laboratory in the United States designed a highly automated workflow that combines a high-throughput experimental platform with the most advanced active learning algorithms to effectively screen for anolyte electrolytes. Binary organic solvent for optimal solubility. The goal of this research is to improve the performance and stability of energy storage systems to promote renewable energy

2024-04-10 comment 0  483

Article Introduction：According to news from this site on September 3, accurate prediction of lithium battery life is crucial for the normal operation of electrical equipment. However, accurate prediction of battery life faces challenges due to the nonlinearity of the battery capacity degradation process and the uncertainty of operating conditions. The Chinese Academy of Sciences stated that the team of researcher Chen Zhongwei and associate researcher Mao Zhiyu from the Power Battery and System Research Department of the National Key Laboratory of Energy Catalytic Conversion of the Dalian Institute of Chemical Physics, together with Professor Feng Jiangtao of Xi'an Jiaotong University, have made progress in battery health management research. Relevant research results have been published in the Journal of Transportation Electrochemistry of the Institute of Electrical and Electronics Engineers (DOI: 10.1109/TTE.2024.3434553 attached to this site). 1. According to reports, the research team developed a new deep learning model

2024-09-03 comment 0  372

Article Introduction：This review (Diffusion Models: A Comprehensive Survey of Methods and Applications) comes from Ming-Hsuan Yang of the University of California & Google Research, Cui Bin Laboratory of Peking University, as well as CMU, UCLA, Montreal Mila Institute and other research teams. It is the first time to review existing A comprehensive summary and analysis of the diffusion model was conducted, including a detailed classification of the diffusion model algorithm, its association with other five major generative models, and its application in seven major fields.

2023-04-12 comment 0  1081

Article Introduction：On June 14, Tencent Robotics promote. Making robot dogs move as flexibly and stably as humans and animals is a long-term goal in the field of robotics research. The continuous advancement of deep learning technology allows machines to master relevant abilities through "learning" and learn to cope with complex and changeable environmental changes. It must be feasible. Introducing pre-training and reinforcement learning: making the robot dog more agile. Tencent RoboticsX Robotics Laboratory introduces pre-training models and reinforcement learning technology to allow the robot dog to learn in stages, effectively combining different levels of

2023-06-16 comment 0  828

Article Introduction：ACMCCS2023, the international authoritative academic conference in the computer field, opened on November 26 in Copenhagen, Denmark. The paper "Hopper: Interpretative Fuzzing for Libraries" by the Tencent Security Big Data Laboratory team was included in the conference. Yesterday, laboratory researcher Xie Yuxuan was invited to attend the conference to share the theme. This research proposes an interpretive fuzz testing method and demonstrates how to use dynamic feedback to learn constraints inside and outside the API to achieve automated code generation. Through this method, without any external expert knowledge, it is possible to generate valid and usable code calling methods and exploit these codes to exploit vulnerabilities. The goal of this research method is to solve the human need for fuzz testing

2023-11-29 comment 0  1428

Article Introduction：Editor | ScienceAI Recently, the research group of Professor Hong Liang from the Institute of Natural Sciences/School of Physics and Astronomy/Zhangjiang Advanced Research Institute/School of Pharmacy of Shanghai Jiao Tong University, and Tan Pan, a young researcher from the Shanghai Artificial Intelligence Laboratory, achieved important breakthroughs in protein mutation-property prediction. . This work adopts a new training strategy, which greatly improves the performance of traditional protein pre-trained large models in mutation-property prediction using very little wet experimental data. The research results are titled "Enhancing the efficiency of protein language models with minimal wet-labdata throughfew-shot learning" and were published in

2024-07-11 comment 0  849

Article Introduction：Translator | Li Rui Reviewer | Sun Shujuan As machine learning becomes part of many applications that people use every day, people are increasingly paying attention to how to identify and solve security and privacy threats to machine learning models. However, security threats faced by different machine learning paradigms vary, and some areas of machine learning security remain under-researched. In particular, the security of reinforcement learning algorithms has not received much attention in recent years. Researchers from Canada's McGill University, Machine Learning Laboratory (MILA) and the University of Waterloo have conducted a new study that focuses on the privacy threats of deep reinforcement learning algorithms. Researchers propose a framework for testing the vulnerability of reinforcement learning models to membership inference attacks. Research

2023-04-09 comment 0  1289

Article Introduction：This review (Diffusion Models: A Comprehensive Survey of Methods and Applications) comes from Ming-Hsuan Yang of the University of California & Google Research, Cui Bin Laboratory of Peking University, as well as CMU, UCLA, Montreal Mila Research and other research teams. It is the first time to review existing The diffusion model has been comprehensively summarized and analyzed, starting from the detailed classification of the diffusion model algorithm, its association with other five major generative models, and its application in seven major fields.

2023-04-09 comment 0  1471

Article Introduction：The AIxiv column is a column where this site publishes academic and technical content. In the past few years, the AIxiv column of this site has received more than 2,000 reports, covering top laboratories from major universities and companies around the world, effectively promoting academic exchanges and dissemination. If you have excellent work that you want to share, please feel free to contribute or contact us for reporting. Submission email: liyazhou@jiqizhixin.com; zhaoyunfeng@jiqizhixin.com This article was completed by Shanghai Artificial Intelligence Laboratory, Dalian University of Technology and University of Science and Technology of China. Corresponding author: Shao Jing, graduated from the Multimedia Laboratory MMLab of the Chinese University of Hong Kong with a Ph.D., and is currently the head of the large model security team of Pujiang National Laboratory, leading the research on large models.

2024-06-14 comment 0  418

Article Introduction：Editor | Dead Leaf Butterfly Large-scale language models have greatly enhanced scientists' ability to understand biology and chemistry, but reliable methods for structure-based drug discovery, quantum chemistry, and structural biology remain few. Accurate biomolecule-ligand interaction datasets are urgently needed for large language models. In order to solve this problem, researchers from the Institute of Biology of the Helmholtz Research Center München and the Technical University of Munich proposed MISATO. This is a data set that combines quantum mechanical (QM) properties of small molecules with associated molecular dynamics (MD) simulations of approximately 20,000 experimental protein-ligand complexes, and extensive validation of experimental data. Starting from the existing experimental structure, the researchers used semi-empirical quantum mechanics to systematically improve these

2024-06-01 comment 0  433

Article Introduction：Chemical reactions are the basis of drug design and organic chemistry research. There is a growing need among the research community for a large-scale deep learning framework that can effectively capture the fundamental rules of chemical reactions. Recently, a research team from Peking University and Wangshi Intelligence proposed a new method to bridge the gap between reaction-based molecular pre-training and generation tasks. Inspired by the mechanisms of organic chemistry, researchers developed a new pre-training framework that enables it to incorporate inductive bias into models. This proposed framework achieves state-of-the-art results when performing challenging downstream tasks. By leveraging knowledge of chemistry, the framework overcomes the limitations of current molecular generation models that rely on a small number of reaction templates. In extensive experiments, the model generated high-quality synthesizable drug-like structures. Overall, the model

2023-12-14 comment 0  586

Article Introduction：Over the past two years, the Hazy Research Laboratory at Stanford University has been engaged in an important task: increasing sequence length. They have a view: Longer sequences will usher in a new era of fundamental models for machine learning—models that can learn from longer contexts, multiple media sources, complex presentations, and more. Currently, this research has made new progress. TriDao and DanFu from the HazyResearch Lab led the research and promotion of the FlashAttention algorithm. They proved that a sequence length of 32k is possible and will be widely used in the current era of basic models (models from OpenAI, Microsoft, NVIDIA and other companies are Using Flas

2023-05-01 comment 0  852

Article Introduction：The AIxiv column is a column where this site publishes academic and technical content. In the past few years, the AIxiv column of this site has received more than 2,000 reports, covering top laboratories from major universities and companies around the world, effectively promoting academic exchanges and dissemination. If you have excellent work that you want to share, please feel free to contribute or contact us for reporting. Submission email: liyazhou@jiqizhixin.com; zhaoyunfeng@jiqizhixin.com The main author of this article is Huang Yichong. Huang Yichong is a doctoral student at the Social Computing and Information Retrieval Research Center of Harbin Institute of Technology and an intern at Pengcheng Laboratory, studying under Professor Qin Bing and Professor Feng Xiaocheng. Research directions include ensemble learning of large language models, multi-language large models, correlation theory

2024-07-19 comment 0  1186

Article Introduction：Recently, a team of professors Luo Yi, Jiang Jun and Shang Weiwei from the University of Science and Technology of China collaborated with researcher Zhang Zhe from the Deep Space Exploration Laboratory to successfully develop a new type of catalyst for use on Mars using an intelligent robot "machine chemist". Use water to produce oxygen. This research provides an efficient, low-energy solution and opens up a new path for the development of chemicals from local materials in extraterrestrial galaxies. The research results were published in the internationally renowned academic journal "Nature·Synthesis" on November 14. Editor: Li Hengyi AI synthesis anchor Technical support: iFlytek

2023-11-14 comment 0  616

Article Introduction：The AIxiv column is a column where this site publishes academic and technical content. In the past few years, the AIxiv column of this site has received more than 2,000 reports, covering top laboratories from major universities and companies around the world, effectively promoting academic exchanges and dissemination. If you have excellent work that you want to share, please feel free to contribute or contact us for reporting. Submission email: liyazhou@jiqizhixin.com; zhaoyunfeng@jiqizhixin.com The research team of Shanghai Jiao Tong University’s Generative Artificial Intelligence Laboratory (GAIRLab)’s main research directions are: large model training, alignment and evaluation. Team homepage: https://plms.ai/AI technology is changing with each passing day. Recently, Anthr

2024-06-24 comment 0  1065

Article Introduction：Apple recently announced that it will expand the scale of its applied research laboratory in China to better support product manufacturing and research and development. It is reported that Apple plans to strengthen the Shanghai research center and provide more comprehensive support to ensure that all product lines reach higher levels in terms of reliability, quality and material analysis. Apple plans to set up a new applied research laboratory in Shenzhen. This move is seen as a strategic layout to introduce VisionPro products into the Chinese market. The company said it will strengthen employee support in the region and deepen cooperation with local suppliers. This new laboratory will focus on improving the testing and research capabilities of products such as iPhone, iPad and Apple Vision Pro. According to the editor's investigation, Apple has already

2024-03-14 comment 0  451

Article Introduction：This article is a study on improving the scalability of zero-order optimization. The code has been open source and the paper has been accepted by ICLR2024. Today I’d like to introduce a paper titled “DeepZero: ScalingupZeroth-OrderOptimization for DeepModelTraining”, a collaboration between Michigan State University and Lawrence Livermore National Laboratory. This paper was recently accepted by the ICLR2024 conference, and the research team has made the code open source. The main goal of this paper is to extend zero-order optimization techniques in deep learning model training. Zero-order optimization is an optimization method that does not rely on gradient information. It can better handle high-dimensional parameter spaces and complex models.

2024-02-15 comment 0  691

Article Introduction：The AIxiv column is a column where this site publishes academic and technical content. In the past few years, the AIxiv column of this site has received more than 2,000 reports, covering top laboratories from major universities and companies around the world, effectively promoting academic exchanges and dissemination. If you have excellent work that you want to share, please feel free to contribute or contact us for reporting. Submission email: liyazhou@jiqizhixin.com; zhaoyunfeng@jiqizhixin.com The research team of Shanghai Jiao Tong University’s Generative Artificial Intelligence Laboratory (GAIRLab)’s main research directions are: large model training, alignment and evaluation. Team homepage: https://plms.ai/ In the next 20 years, AI is expected to surpass humans

2024-06-20 comment 0  610