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- CVPR 2024 | Good at processing complex scenes and language expressions, Tsinghua & Bosch proposed a new instance segmentation network architecture MagNet
- 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. Referring Image Segmentation (RIS) is a very challenging multi-modal task that requires the algorithm to simultaneously understand fine human language and visual image information, and to identify the objects referred to by sentences in the image.
- AI 697 2024-04-26 18:10:01
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- Ten limitations of artificial intelligence
- In the field of technological innovation, artificial intelligence (AI) is one of the most transformative and promising developments of our time. Artificial intelligence has revolutionized many industries, from healthcare and finance to transportation and entertainment, with its ability to analyze large amounts of data, learn from patterns, and make intelligent decisions. However, despite its remarkable progress, AI also faces significant limitations and challenges that prevent it from reaching its full potential. In this article, we will delve into the top ten limitations of artificial intelligence, revealing the limitations faced by developers, researchers, and practitioners in this field. By understanding these challenges, it is possible to navigate the complexities of AI development, reduce risks, and pave the way for responsible and ethical advancement of AI technology. Limited data availability: The development of artificial intelligence depends on data
- AI 718 2024-04-26 17:52:01
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- Ten methods in AI risk discovery
- Beyond chatbots or personalized recommendations, AI’s powerful ability to predict and eliminate risks is gaining momentum in organizations. As massive amounts of data proliferate and regulations tighten, traditional risk assessment tools are struggling under the pressure. Artificial intelligence technology can quickly analyze and supervise the collection of large amounts of data, allowing risk assessment tools to be improved under compression. By using technologies such as machine learning and deep learning, AI can identify and predict potential risks and provide timely recommendations. Against this backdrop, leveraging AI’s risk management capabilities can ensure compliance with changing regulations and proactively respond to unforeseen threats. Leveraging AI to tackle the complexities of risk management may seem alarming, but for those passionate about staying on top in the digital race
- AI 386 2024-04-26 17:25:19
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- Andrew Ng: Multi-agent collaboration is the new key, and tasks such as software development will be more efficient
- Not long ago, Stanford University professor Andrew Ng mentioned the huge potential of intelligent agents in his speech, which also caused a lot of discussion. Among them, Ng Enda said that the agent workflow built based on GPT-3.5 performs better in applications than GPT-4. This shows that it is not necessarily advisable to limit one's sights to large models, and that the agent may be better than the base model it uses. In the field of software development, these agents have demonstrated their unique ability to collaborate efficiently, handle complex problems in programming, and even perform automatic code generation. The latest technology trends show that AI smart communication has great potential in software development. Remember Devin? Known as the world's first AI software engineer, it amazed us when it came out. An intelligent agent can bring us such
- AI 836 2024-04-26 17:20:10
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- What are edge artificial intelligence and edge computing?
- Edge AI is one of the most noteworthy new areas in artificial intelligence, allowing people to run artificial intelligence processes without having to worry about privacy or data transfer slowdowns. Edge AI is making the use of artificial intelligence wider and more widespread, allowing smart devices to respond quickly to input without accessing the cloud. While this is a quick definition of edge AI, let’s take a moment to better understand edge AI by exploring some use cases. First, edge AI has widespread applications in the healthcare industry. For example, integrating edge AI on monitoring devices can more accurately monitor and analyze patients’ vital signs and respond immediately when needed. This capability can increase the efficiency of healthcare while also reliably handling sensitive personal numbers
- AI 811 2024-04-26 17:10:10
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- Under the leadership of Yan Shuicheng, Kunlun Wanwei 2050 Global Research Institute jointly released Vitron with NUS and NTU, establishing the ultimate form of general visual multi-modal large models.
- Recently, led by Professor Yan Shuicheng, the Kunlun World Vision 2050 Global Research Institute, the National University of Singapore, and the Nanyang Technological University of Singapore team jointly released and open sourced the Vitron universal pixel-level visual multi-modal large language model. This is a heavy-duty general-purpose visual multi-modal large model that supports a series of visual tasks from visual understanding to visual generation, from low-level to high-level, and solves the image/video model separation that has plagued the large language model industry for a long time. problem, it provides a pixel-level universal visual multi-modal model that comprehensively unifies the tasks of understanding, generating, segmenting, and editing static images and dynamic video content. It lays the foundation for the ultimate form of the next generation of universal visual models and also marks the Another big step towards general artificial intelligence (AGI) with large models. Vi
- AI 570 2024-04-26 17:00:30
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- What LinkedIn learned from using large language models to serve its billion users
- With more than 1 billion users worldwide, LinkedIn continues to challenge the limits of today's enterprise technology. Few companies operate quite like LinkedIn, or have similarly vast data resources. This business and employment-focused social media platform connects qualified candidates with potential employers, and helping fill job vacancies is its core business. It is also important to ensure that posts on the platform reflect the needs of employers and consumers. Under LinkedIn's model, these matching processes have always relied on technology. By the summer of 2023, when GenAI was first gaining steam, LinkedIn began to consider whether to leverage large language models (LLMs) to match candidates with employers and make the flow of information more useful. therefore,
- AI 409 2024-04-26 16:49:11
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- FisheyeDetNet: the first target detection algorithm based on fisheye camera
- Target detection is a relatively mature problem in autonomous driving systems, among which pedestrian detection is one of the earliest algorithms to be deployed. Very comprehensive research has been carried out in most papers. However, distance perception using fisheye cameras for surround view is relatively less studied. Due to large radial distortion, standard bounding box representation is difficult to implement in fisheye cameras. To alleviate the above description, we explore extended bounding box, ellipse, and general polygon designs into polar/angular representations and define an instance segmentation mIOU metric to analyze these representations. The proposed model fisheyeDetNet with polygonal shape outperforms other models and simultaneously achieves 49.5% mAP on the Valeo fisheye camera dataset for autonomous driving
- AI 738 2024-04-26 11:37:01
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- Let's talk about the collision between machine learning and human resources management?
- Introduction In recent years, many major breakthroughs have been made in the field of machine learning, and human resource management service products driven by artificial intelligence technology also have a huge and dynamic market. More and more companies and government agencies are gradually thinking about applying machine learning technology to human resource management, making effective decisions through neural networks, and accurately predicting the results of human resource management. This article introduces four aspects of applying machine learning to human resource management research, mainly including technical difficulties, introduction to human resources management decision-making systems, system design methods and system security. It is hoped that readers can have a preliminary understanding of related research. Technical Difficulties In 2019, CEOs of 20 large companies in the United States conducted relevant seminars. The results showed that the application of machine learning technology in human resources management
- AI 569 2024-04-26 10:25:07
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- Docker completes local deployment of LLama3 open source large model in three minutes
- Overview LLaMA-3 (LargeLanguageModelMetaAI3) is a large-scale open source generative artificial intelligence model developed by Meta Company. It has no major changes in model structure compared with the previous generation LLaMA-2. The LLaMA-3 model is divided into different scale versions, including small, medium and large, to suit different application needs and computing resources. The parameter size of small models is 8B, the parameter size of medium models is 70B, and the parameter size of large models reaches 400B. However, during training, the goal is to achieve multi-modal and multi-language functionality, and the results are expected to be comparable to GPT4/GPT4V. Install OllamaOllama is an open source large language model (LL
- AI 1340 2024-04-26 10:19:21
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- Quantification, pruning, distillation, what exactly do these big model slangs say?
- Quantification, pruning, distillation, if you often pay attention to large language models, you will definitely see these words. Just look at these words, it is difficult for us to understand what they do, but this Several words are particularly important for the development of large language models at this stage. This article will help you get to know them and understand their principles. Model compression quantization, pruning, and distillation are actually general neural network model compression technologies and are not exclusive to large language models. The significance of model compression: After compression, the model file will become smaller, the hard disk space used will also become smaller, the cache space used when loading into memory or displayed will also become smaller, and the running speed of the model may also be improved. Through compression, using the model will consume less computing resources, which can greatly scale
- AI 662 2024-04-26 09:28:18
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- Innovating the way to fine-tune LLM: comprehensive interpretation of the innovative power and application value of PyTorch's native library torchtune
- In the field of artificial intelligence, large language models (LLMs) are increasingly becoming a new hot spot in research and application. However, how to tune these behemoths efficiently and accurately has always been an important challenge faced by the industry and academia. Recently, the PyTorch official blog published an article about TorchTune, which attracted widespread attention. As a tool focused on LLMs tuning and design, TorchTune is highly praised for its scientific nature and practicality. This article will introduce in detail the functions, features and application of TorchTune in LLMs tuning, hoping to provide readers with a comprehensive and in-depth understanding. 1. The birth background and significance of TorchTune, the development of deep learning technology and the deep learning model (LLM)
- AI 698 2024-04-26 09:20:02
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- Jiyue once again joins forces with NVIDIA, and the high-performance computing platform Thor will be launched in 2026
- After an absence of four years, the 18th Beijing International Automobile Exhibition returns. As a pioneer in intelligence, Jiyue, a high-end smart car robot brand, uses the theme of "Jiyue is smarter and more beautiful" to interpret future AI technology, making its first appearance at the Beijing Auto Show. On April 25, 2024, Jiyue Auto Robot debuted at the Beijing Auto Show with a new product lineup. Its second model, the AI intelligent pure electric drive car Jiyue 07, made its debut at the auto show, with an original Chinese design with a strong artistic aesthetic. , winning the title of "The Most Beautiful 7 Series". Jiyue and NVIDIA have joined forces again, and the 1000TFLOPS high-performance computing platform Thor will be mass-produced in 2026. At the same time, Jiyue 01 will also be upgraded to the latest V1.5.0 version, PPA Smart
- AI 1064 2024-04-26 08:28:01
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- Changan Qiyuan E07's 'one car with multiple states' subverts traditional perceptions. Will future cars be transforming robots?
- On April 25, 2024, the Beijing International Auto Show (referred to as the Beijing Auto Show) opened successfully at the China International Exhibition Center in Beijing, with the Changan Qiyuan E07 making its grand debut. It has variable form, variable functions and variable software. It is the world's first mass-produced variable new car and a milestone in the advancement of Changan Automobile's intelligence. It is also known as the "Chinese version of Cybertruck" because of its styling design and software architecture. The Changan Qiyuan E07 that met consumers this time brought special surprise benefits. Users who download and register the official exclusive APP - Topspace can receive 1,000 yuan of optional equipment funds. Participating in activities can obtain at least 120 yuan/day of optional equipment funds. In addition, Changan Qiyuan E07 has also launched an early access rights and interests activity. Users only need to
- AI 504 2024-04-25 21:04:45
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- Yan Shuicheng took charge and established the ultimate form of the 'universal visual multi-modal large model'! Unified understanding/generation/segmentation/editing
- Recently, Professor Yan Shuicheng's team jointly released and open sourced Vitron's universal pixel-level visual multi-modal large language model. Project homepage & Demo: https://vitron-llm.github.io/ Paper link: https://is.gd/aGu0VV Open source code: https://github.com/SkyworkAI/Vitron This is a heavy-duty general vision The multi-modal large model supports a series of visual tasks from visual understanding to visual generation, from low level to high level. It solves the image/video model separation problem that has plagued the large language model industry for a long time, and provides a comprehensive unified static image. Understanding, generating, segmenting, and editing dynamic video content
- AI 867 2024-04-25 20:04:15
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