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- ICML 2024 | The new frontier of large language model pre-training: 'Best Adaptation Packaging' reshapes document processing standards
- 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 In the training process of large-scale language models, the way of data processing is crucial. Traditional methods usually work by splicing and splitting a large number of documents into training sequences equal to the context length of the model. Although this improves training efficiency, it often leads to unnecessary truncation of documents.
- AI 621 2024-06-02 21:42:20
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- Tencent Hunyuan large model has been fully reduced in price! Hunyuan-lite is free from now on
- On May 22, Tencent Cloud announced a new large model upgrade plan. One of the main models, Hunyuan-lite model, the total API input and output length is planned to be upgraded from the current 4k to 256k, and the price is adjusted from 0.008 yuan/thousand tokens to fully free. The Hunyuan-standardAPI input price dropped from 0.01 yuan/thousand tokens to 0.0045 yuan/thousand tokens, a decrease of 55%, and the API output price dropped from 0.01 yuan/thousand tokens to 0.005 yuan/thousand tokens, a decrease of 50%. The newly launched Hunyuan-standard-256k has the ability to process ultra-long text of more than 380,000 characters, and the API input price has been reduced to 0.015 yuan/thousand toke.
- AI 418 2024-06-02 20:07:09
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- The latest from Oxford University | Nearly 400 summaries! Talk about the latest review of large language models and the three-dimensional world
- Written above & the author’s personal understanding: With the development of large language models (LLM), rapid progress has been made in the integration between them and 3D spatial data (3DLLM), providing unprecedented capabilities for understanding and interacting with physical space. . This article provides a comprehensive overview of LLM's approach to processing, understanding and generating 3D data. We highlight the unique advantages of LLMs, such as contextual learning, stepwise reasoning, open vocabulary capabilities, and broad world knowledge, and highlight their potential to advance spatial understanding and interaction with embedded artificial intelligence (AI) systems. Our research covers various 3D data representations from point clouds to Neural Rendering Fields (NeRF). and analyzed their integration with LLM for 3D scene understanding, subtitles,
- AI 389 2024-06-02 19:41:32
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- Read this article to understand the AutoGen open source framework for building multi-agent
- Hellofolks, my name is Luga. Today we will talk about technologies related to the artificial intelligence (AI) ecological field - AutoGen - a unified multi-agent dialogue framework. Imagine a scenario where we no longer fight alone, but instead have a highly personalized, cross-domain integrated AI team. Each team member is skilled and professional in their own field, cooperates seamlessly with each other, communicates efficiently, and never gets tired. They are able to work highly collaboratively to address complex and ever-changing challenges. This is the essence of AutoGen - a groundbreaking multi-agent dialogue framework. AutoGen+ gives us unlimited possibilities, allowing us to form our own strategic artificial intelligence team at will. Each member has a unique
- AI 1128 2024-06-02 19:12:02
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- More than just 3D Gaussian! Latest overview of state-of-the-art 3D reconstruction techniques
- Written above & The author’s personal understanding is that image-based 3D reconstruction is a challenging task that involves inferring the 3D shape of an object or scene from a set of input images. Learning-based methods have attracted attention for their ability to directly estimate 3D shapes. This review paper focuses on state-of-the-art 3D reconstruction techniques, including generating novel, unseen views. An overview of recent developments in Gaussian splash methods is provided, including input types, model structures, output representations, and training strategies. Unresolved challenges and future directions are also discussed. Given the rapid progress in this field and the numerous opportunities to enhance 3D reconstruction methods, a thorough examination of the algorithm seems crucial. Therefore, this study provides a comprehensive overview of recent advances in Gaussian scattering. (Swipe your thumb up
- AI 844 2024-06-02 18:57:35
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- Generate dataset with GPT-3.5! New SOTA for image editing by Peking University Tiangong and other teams can accurately simulate physical world scenes
- There are many methods for high-quality image editing, but none of them accurately represent the real physical world. So, give EdittheWorld a try. Peking University, TiamatAI, Tiangong AI, and Mila Labs proposed EditWorld, which introduced a new editing task, namely world-instructed image editing. It defines and categorizes instructions based on various world scenarios. Images are supported by a set of pre-trained models such as GPT-3.5, Video-LLava and SDXL to build a multi-modal dataset with world instructions. A diffusion-based image editing model EditWorld was trained on this data set, and the results in its new task
- AI 825 2024-06-02 17:18:08
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- New SOTA for target detection, real-time recognition on the device and side, Shen Xiangyang rarely forwards and likes
- The field of target detection has ushered in new progress - GroundingDINO1.5, produced by the IDEA Research Institute team, which can achieve real-time recognition on the device side. This progress was forwarded by AI tycoon Shen Xiangyang, who usually makes changes every year. There are two main versions of this release: Pro and Edge. The Pro version is stronger and the Edge version is faster. It still retains the dual encoder-single decoder structure of the previous version GroundingDINO. On this basis, it expands the model size by combining a larger visual backbone, and uses more than 20 million Grounding data to obtain a rich corpus, which greatly improves detection. Accuracy and speed, and are targeted at different applications through Pro and Edge versions.
- AI 1170 2024-06-02 16:41:05
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- DiffMap: the first network to use LDM to enhance high-precision map construction
- Paper title: DiffMap: EnhancingMapSegmentationwithMapPriorUsingDiffusionModel Paper author: PeijinJia, TuopuWen, ZiangLuo, MengmengYang, KunJiang, ZhiquanLei, XueweiTang, ZiyuanLiu, LeCui, KehuaSheng, BoZhang, DiangeYang01 Background Introduction For autonomous vehicles, high-definition (HD) maps can help them Improved accuracy of environmental understanding (perception) and precision of navigation. However, artificially constructed drawing surfaces
- AI 835 2024-06-02 16:26:44
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- Read GPT-4o vs GPT-4 Turbo in one article
- Hellofolks, I am Luga. Today we will talk about technologies related to the artificial intelligence (AI) ecological field - the GPT-4o model. On May 13, 2024, OpenAI innovatively launched its most advanced and cutting-edge model GPT-4o, which marked a major breakthrough in the field of artificial intelligence chatbots and large-scale language models. Heralding a new era of artificial intelligence capabilities, GPT-4o boasts significant performance enhancements that surpass its predecessor, GPT-4, in both speed and versatility. This groundbreaking advancement resolves the latency issues that often plagued its predecessor, ensuring a seamless and responsive user experience. What is GPT-4o? On May 13, 2024, OpenAI released
- AI 718 2024-06-02 16:02:40
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- The first pure visual static reconstruction of autonomous driving
- A purely visual annotation solution mainly uses vision plus some data from GPS, IMU and wheel speed sensors for dynamic annotation. Of course, for mass production scenarios, it doesn’t have to be pure vision. Some mass-produced vehicles will have sensors like solid-state radar (AT128). If we create a data closed loop from the perspective of mass production and use all these sensors, we can effectively solve the problem of labeling dynamic objects. But there is no solid-state radar in our plan. Therefore, we will introduce this most common mass production labeling solution. The core of a purely visual annotation solution lies in high-precision pose reconstruction. We use the pose reconstruction scheme of Structure from Motion (SFM) to ensure reconstruction accuracy. But pass
- AI 758 2024-06-02 15:24:40
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- The thought chain no longer exists? Latest research from New York University: The reasoning step can be omitted
- The popular thinking chain technology may be overthrown! Are you still surprised that large models can actually think step by step using thinking chains? Still struggling with not being able to write thought chain prompt words? Researchers from New York University said: "It doesn't matter, it's all the same." The reasoning steps are not important. You don't have to write the prompt words if you don't want to, just use ellipsis instead. Paper address: https://arxiv.org/pdf/2404.15758 The title of this article even directly uses “Let’sthinkdotbydot” to compare with “Let’sthinkstepbystep” of the thinking chain, showing the power of “ellipsis”. The power of "dots and dots" Researchers have discovered that chain-o
- AI 417 2024-06-02 15:21:41
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- Microsoft releases Copilot agent and also brings native small language model
- According to news on May 22, 2022, on Tuesday local time, Microsoft released the Copilot new agent at the Build developer conference. It is like a virtual employee and can automatically perform tasks. Microsoft believes that this kind of artificial intelligence will eliminate boring work content rather than completely replace employee work. Microsoft's Copilot is developed based on the open source code model GPT-3, which uses a large number of code libraries and algorithms to help developers write code more efficiently. Copilot's ability to analyze context and generate matching code snippets greatly speeds up the speed and quality of development. However, Copilot's functionality is still limited and it cannot completely replace artificial intelligence. In addition, Microsoft also targets Copi
- AI 390 2024-06-02 14:56:12
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- Why artificial intelligence could revolutionize mathematics
- Editor | Cabbage Leaf "Proposing a conjecture—a proposition that is suspected to be true, but requires explicit proof—is like a moment of divine inspiration for mathematicians. Mathematical conjectures are more than just educated guesses. Formulating them It takes a combination of genius, intuition and experience. Even mathematicians have a hard time explaining their own discovery process. Yet, counterintuitively, I think this is the most transformative area of machine intelligence initially." Institute of Mathematical Sciences, London, UK. Chief Thomas Fink said. In 2017, researchers at the Institute of Mathematical Sciences in London began applying machine learning to mathematical data as a hobby. During the COVID-19 pandemic, they discovered that simple artificial intelligence (AI)
- AI 622 2024-06-02 14:47:39
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- How to solve the long tail problem in autonomous driving scenarios?
- Yesterday during the interview, I was asked whether I had done any long-tail related questions, so I thought I would give a brief summary. The long-tail problem of autonomous driving refers to edge cases in autonomous vehicles, that is, possible scenarios with a low probability of occurrence. The perceived long-tail problem is one of the main reasons currently limiting the operational design domain of single-vehicle intelligent autonomous vehicles. The underlying architecture and most technical issues of autonomous driving have been solved, and the remaining 5% of long-tail problems have gradually become the key to restricting the development of autonomous driving. These problems include a variety of fragmented scenarios, extreme situations, and unpredictable human behavior. The "long tail" of edge scenarios in autonomous driving refers to edge cases in autonomous vehicles (AVs). Edge cases are possible scenarios with a low probability of occurrence. these rare events
- AI 1249 2024-06-02 14:44:00
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- IBM releases Granite AI model to open source community
- IBM Research recently announced that its Granite coding basic model is open source, with the goal of democratizing advanced AI tools and promoting comprehensive changes in the way code is written, maintained and developed across industries. This move will enable developers to create, optimize and deploy AI models more efficiently, thereby accelerating the application of artificial intelligence technology. Granite is an advanced AI programming tool developed by IBM Research Institute with powerful functions. What level is it based on the open-standard IBM Granite coding model? Granite originated from IBM's ambition to simplify the coding process. After realizing the complexity and rapid development requirements inherent in software development, IBM used its strong scientific research capabilities to build a set of AI-driven tools aimed at
- AI 784 2024-06-02 13:46:40
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