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- Four recommended AI-assisted programming tools (supporting C# language)
- Preface In this stage of rapid development of AI, a large number of useful AI-assisted programming tools have emerged. AI-assisted programming tools can improve development efficiency, improve code quality, and reduce bug rates. They are important assistants in the modern software development process. Today Dayao will share with you 4 AI-assisted programming tools (and all support C# language). I hope it will be helpful to everyone. Image of AI-assisted programming tool GitHubCopilotGitHubCopilot is an AI coding assistant that can help you write code faster and with less effort, so you can focus more on problem solving and collaboration. GitHubCopilot provides suggestions as you code: sometimes completions for the current line, sometimes entirely new blocks of code. Can
- AI 543 2024-05-30 10:03:11
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- Several design patterns that excellent agents must learn, you can learn them in one go
- Hello everyone, I am Lao Du. Yesterday, I listened to the AI hospital town shared by Tsinghua University Intelligent Industry Research Institute at the company. Picture: This is a virtual world. All doctors, nurses, and patients are Agents driven by LLM and can interact independently. They simulated the entire process of diagnosis and treatment, and achieved a state-of-the-art accuracy of 93.06% on a subset of the MedQA data set covering major respiratory diseases. An excellent intelligent agent is inseparable from excellent design patterns. After reading this case, I quickly read the four main Agent design patterns recently published by Mr. Andrew Ng. Andrew Ng is one of the most authoritative scholars in the world in the field of artificial intelligence and machine learning. Then, I quickly compiled it and shared it with everyone. Mode 1. Reflection
- AI 733 2024-05-30 09:44:20
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- Beyond ORB-SLAM3! SL-SLAM: Low light, severe jitter and weak texture scenes are all handled
- Written previously, today we discuss how deep learning technology can improve the performance of vision-based SLAM (simultaneous localization and mapping) in complex environments. By combining deep feature extraction and depth matching methods, here we introduce a versatile hybrid visual SLAM system designed to improve adaptation in challenging scenarios such as low-light conditions, dynamic lighting, weakly textured areas, and severe jitter. sex. Our system supports multiple modes, including extended monocular, stereo, monocular-inertial, and stereo-inertial configurations. In addition, it also analyzes how to combine visual SLAM with deep learning methods to inspire other research. Through extensive experiments on public datasets and self-sampled data, we demonstrate the superiority of SL-SLAM in terms of positioning accuracy and tracking robustness.
- AI 487 2024-05-30 09:35:24
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- The most detailed 3D map of the human brain is published in Science! GPT-4 parameters are only equivalent to 0.2% of humans
- Human brain tissue the size of a sesame seed has a synapse size equivalent to one GPT-4! Google and Harvard teamed up to conduct nanoscale modeling of a partial human brain, and the paper has been published in Science. This is the largest and most detailed replica of the human brain to date, showing for the first time the network of synaptic connections in the brain. With its ultra-high resolution, the reconstruction, called H01, has revealed some previously unseen details about the human brain. Professor Lichtman of Harvard University, the corresponding author of the project, said that no one has really seen such a complex synaptic network before. This modeling result will help to gain a deeper understanding of the workings of the brain and inspire further research on brain functions and diseases. It is also worth mentioning that the study involved 1 cubic milliliter
- AI 472 2024-05-29 17:03:30
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- New ideas for quantification of byte open source large models, the accuracy of the 2-bit quantization model is on par with fp16
- 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 As deep learning large language models become more and more popular, large language models become larger and larger, making their inference costs also rise. Model quantification has become a popular research topic. Recently, ByteDance has launched a new quantification idea, abandoning the traditional quantification paradigm.
- AI 648 2024-05-29 09:29:50
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- Reviewing 170 'self-supervised learning' recommendation algorithms, HKU releases SSL4Rec: the code and database are fully open source!
- Recommender systems are important to address the challenge of information overload, as they provide customized recommendations based on users’ personal preferences. In recent years, deep learning technology has greatly promoted the development of recommendation systems and improved insights into user behavior and preferences. However, traditional supervised learning methods face challenges in practical applications due to data sparsity issues, which limits their ability to effectively learn user performance. In order to overcome this problem, self-supervised learning (SSL) technology is applied in biomass, which uses the inherent structure of the data to generate supervision signals and does not rely entirely on labeled data. This approach uses a recommendation system that can extract meaningful information from unlabeled data and make accurate predictions and recommendations even when data is scarce. Article address: https://
- AI 872 2024-05-09 16:58:02
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- LLM is all done! OmniDrive: Integrating 3D perception and reasoning planning (NVIDIA's latest)
- Written above & the author’s personal understanding: This paper is dedicated to solving the key challenges of current multi-modal large language models (MLLMs) in autonomous driving applications, that is, the problem of extending MLLMs from 2D understanding to 3D space. This expansion is particularly important as autonomous vehicles (AVs) need to make accurate decisions about 3D environments. 3D spatial understanding is critical for AVs because it directly impacts the vehicle’s ability to make informed decisions, predict future states, and interact safely with the environment. Current multi-modal large language models (such as LLaVA-1.5) can often only handle lower resolution image inputs (e.g.) due to resolution limitations of the visual encoder, limitations of LLM sequence length. However, autonomous driving applications require
- AI 328 2024-05-09 16:55:01
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- Tongyi Qianwen APP was renamed as Tongyi APP, and Tongyi's full-stack capabilities were opened for free
- On May 9, the brand of Tongyi Big Model was upgraded. "Tongyi Qianwen APP" was renamed as "Tongyi APP", integrating the full stack capabilities of Tongyi Big Model and providing free services to all users. Tongyi APP is based on a basic model with performance comparable to GPT-4Turbo, and "Allinone" Tongyi Laboratory's cutting-edge capabilities such as text graphics, intelligent coding, document analysis, audio and video understanding, and visual generation, becoming everyone's all-round AI assistant. The general meaning is "reasonable and righteous". It has full AI capabilities and is committed to becoming people's assistants in work, study and life. Tongyi APP has super document processing capabilities. It can process long documents of up to 10 million words at a time, and can also parse 100 documents in different formats at the same time. It is an ideal tool for finance, law, scientific research, and medicine.
- AI 844 2024-05-09 15:25:01
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- Revealing DeDoDe v2: How to innovate key point detection technology to make AI's 'eyes” brighter?
- 1. Technological innovation, DeDoDev2 emerged as the times require. In the fields of image processing and computer vision, key point detection is the basis of many applications, such as target recognition, image matching, three-dimensional reconstruction, etc. However, traditional key point detection technology often has problems such as inaccurate detection and vulnerability to noise interference. In order to solve these problems, scientific research teams such as Linköping University launched DeDoDev2, a new key point detector, which makes targeted improvements and optimizations through in-depth analysis of DeDoDe's shortcomings. 2. Breaking through the bottleneck, DeDoDev2’s three major innovations solve the problem of key point clustering. In DeDoDe, researchers found that key points tend to cluster in specific areas, resulting in insufficient detection in other areas.
- AI 1083 2024-05-09 14:55:01
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- LidaRF: Studying LiDAR Data for Street View Neural Radiation Fields (CVPR\'24)
- Light-realistic simulation plays a key role in applications such as autonomous driving, where advances in neural network radiated fields (NeRFs) may enable better scalability by automatically creating digital 3D assets. However, the reconstruction quality of street scenes suffers due to the high collinearity of camera motion on the streets and sparse sampling at high speeds. On the other hand, the application often requires rendering from a camera perspective that deviates from the input perspective to accurately simulate behaviors such as lane changes. LidaRF presents several insights that allow better utilization of lidar data to improve the quality of NeRF in street views. First, the framework learns geometric scene representations from lidar data, which are combined with an implicit mesh-based decoder to provide stronger geometric information provided by the displayed point cloud.
- AI 767 2024-05-09 13:31:37
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- Nintendo blitzes GitHub, deleting more than 8,000 emulator code repositories overnight
- Nintendo Blitzes GitHub! 8535 code bases were removed overnight. As long as it contains the YuzuSwitch emulator code, it is said to have illegally bypassed Nintendo's technical protection measures and run illegal pirated Switch games. GitHub also responded. Developers have time to delete or change infringing content. In addition, GitHub provides legal resources and guidance for developers on how to submit DMCA (a U.S. copyright law) counter-notification. As soon as this incident came out, netizens also exploded, with voices supporting Nintendo and Yuzu. Some netizens suggested, don’t make any noise: let’s vote with our wallets! Some netizens thought they were deleting all Nintendo emulators: Fortunately, it was just with Yu
- AI 836 2024-05-09 12:46:28
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- HKU's large open source graph basic model OpenGraph: strong generalization ability, forward propagation to predict new data
- There is a new way to alleviate the data starvation problem in the field of graph learning! OpenGraph, a basic graph-based model specifically designed for zero-shot prediction on a variety of graph datasets. The Chao Huang team, head of the Data Intelligence Laboratory at the University of Hong Kong, also proposed improvement and adjustment techniques for the model to improve the model's adaptability to new tasks. Currently, this work has been posted on GitHub. Introducing data augmentation techniques, this work is an in-depth exploration of strategies to enhance the generalization ability of graphical models (especially when there are significant differences in training and test data). OpenGraph is a general graph structure model that performs forward propagation through propagation prediction to achieve zero-sample prediction of new data. To achieve their goals, the team
- AI 264 2024-05-09 12:01:02
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- Application of algorithms in the construction of 58 portrait platform
- 1. Background of the Construction of 58 Portraits Platform First of all, I would like to share with you the background of the construction of the 58 Portrait Platform. 1. The traditional thinking of the traditional profiling platform is no longer enough. Building a user profiling platform relies on data warehouse modeling capabilities to integrate data from multiple business lines to build accurate user portraits; it also requires data mining to understand user behavior, interests and needs, and provide algorithms. side capabilities; finally, it also needs to have data platform capabilities to efficiently store, query and share user profile data and provide profile services. The main difference between a self-built business profiling platform and a middle-office profiling platform is that the self-built profiling platform serves a single business line and can be customized on demand; the mid-office platform serves multiple business lines, has complex modeling, and provides more general capabilities. 2.58 User portraits of the background of Zhongtai portrait construction
- AI 487 2024-05-09 09:01:10
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- Explore automated work management tools and their benefits in 2024
- In the fast-paced business world, efficiency and productivity are paramount. To stay ahead of the curve, organizations are increasingly turning to automated work management tools. But what exactly are these tools, and how can they benefit businesses in 2024? Automated work management tools comprise a range of software solutions designed to streamline and optimize every aspect of an organization’s workflow. These tools automate repetitive tasks, facilitate collaboration, and provide insights to enhance the decision-making process. In today's highly competitive environment, businesses are challenged to do more with fewer resources. Automated work management tools provide a way to achieve this by eliminating manual inefficiencies and maximizing capital utilization. They enable teams to focus on high value while automating routine activities
- AI 586 2024-05-08 20:40:05
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- 7262 papers were submitted, ICLR 2024 became a hit, and two domestic papers were nominated for outstanding papers.
- This year, a total of 5 outstanding paper awards and 11 honorable mentions were selected. ICLR stands for International Conference on Learning Representations (International Conference on Learning Representations). This year is the twelfth conference, held in Vienna, Austria, from May 7th to 11th. In the machine learning community, ICLR is a relatively "young" top academic conference. It is hosted by deep learning giants and Turing Award winners Yoshua Bengio and Yann LeCun. It just held its first session in 2013. However, ICLR quickly gained wide recognition from academic researchers and is considered the top academic conference on deep learning. This session received a total of 726
- AI 1041 2024-05-08 20:34:24
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