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- The $1 million prize from the Clay Institute will go to AI. The rules of the mathematics world have changed drastically. How will mathematicians deal with 'massive conjectures' in the future?
- In the world of mathematics, a complete proof of "an unproven conjecture" often requires a combination of talent, intuition and experience. Even mathematicians find it difficult to explain their discovery process. However, with the rise of large models in recent years, we have witnessed a new force of change. AI not only surpasses humans in predicting the complexity of elliptic curves, but also makes breakthroughs in exploring new formulas for fundamental constants. Recently, Thomas Fink, Director of the Institute of Mathematical Sciences in London, published an article in Nature’s worldview column, exploring how AI plays its unique role in the field of mathematics and how it can help mathematicians move from conjecture to proof. . In this article, Fink mentioned the role of AI in
- AI 556 2024-06-01 11:02:46
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- This article will take you to understand SHAP: model explanation for machine learning
- In the fields of machine learning and data science, model interpretability has always been a focus of researchers and practitioners. With the widespread application of complex models such as deep learning and ensemble methods, understanding the model's decision-making process has become particularly important. Explainable AI|XAI helps build trust and confidence in machine learning models by increasing the transparency of the model. Improving model transparency can be achieved through methods such as the widespread use of multiple complex models, as well as the decision-making processes used to explain the models. These methods include feature importance analysis, model prediction interval estimation, local interpretability algorithms, etc. Feature importance analysis can explain the decision-making process of a model by evaluating the degree of influence of the model on the input features. Model prediction interval estimate
- AI 1172 2024-06-01 10:58:13
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- At CCIG2024, Hehe Information document analysis technology solves the 'famine' problem of large model corpus
- In 2024, the China Image and Graphics Conference will grandly open in the ancient capital of Xi'an. This conference was hosted by the Chinese Image and Graphics Society and hosted by the Air Force Medical University, Xi'an Jiaotong University, and Northwestern Polytechnical University. Through more than 20 forums and more than 100 achievements, it focused on displaying production artificial intelligence, large models, and machine learning. , brain-inspired computing and other areas of image graphics. Large model technology is being widely used with technological innovation to meet the image processing needs of many industries. During the conference, the CSIG Document Image Analysis and Recognition Special Committee and Shanghai Hehe Information Technology Co., Ltd. (referred to as "Hehe Xinheng") jointly hosted the forum "Large Model Technology and Its Frontier Applications", with representatives from South China University of Technology and Shanghai Jiao Tong University, Tsinghua University, Fudan University, Shanghai Artificial Intelligence Research Institute
- AI 873 2024-05-31 22:28:49
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- Tencent Cloud AI Code Assistant is fully open to the public
- On May 22, Tencent Cloud officially announced that its AI code assistant is fully open to the public. Currently, developers, development teams, and enterprise customers can experience it online through Tencent Cloud’s official website. Tencent Cloud AI Code Assistant is a digital tool for intelligent positioning code completion and generation. It is based on Tencent's mixed code model and can provide developers, development teams and enterprise customers with safe, compliant, and high-viscosity experience coding scenario AIGC services. , with functions such as agent code completion, technical dialogue, code diagnosis, unit testing, etc., supports ten programming languages or frameworks such as Python, Java, C/C++, and Go, and integrates with mainstream integrated development environments. In terms of code completion, the AI code assistant can provide good automatic completion based on multi-file understanding and context.
- AI 1024 2024-05-31 20:08:24
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- Is Flash Attention stable? Meta and Harvard found that their model weight deviations fluctuated by orders of magnitude
- MetaFAIR teamed up with Harvard to provide a new research framework for optimizing the data bias generated when large-scale machine learning is performed. It is known that the training of large language models often takes months and uses hundreds or even thousands of GPUs. Taking the LLaMA270B model as an example, its training requires a total of 1,720,320 GPU hours. Training large models presents unique systemic challenges due to the scale and complexity of these workloads. Recently, many institutions have reported instability in the training process when training SOTA generative AI models. They usually appear in the form of loss spikes. For example, Google's PaLM model experienced up to 20 loss spikes during the training process. Numerical bias is the root cause of this training inaccuracy,
- AI 888 2024-05-30 13:24:53
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- One article takes you through data models: conceptual model, logical model and physical model
- Data models are the cornerstone of organizational data management and a key component in building information infrastructure. Data models provide organizations with a clear data structure and logical framework, making data management more efficient and sustainable. In the digital era, data has become one of the most valuable assets of an enterprise, and the design and implementation of data models determine the effectiveness and credibility of data in enterprise operations and decision-making. A good data model can not only simplify complex data landscapes and improve data quality and consistency, but also optimize database performance and support data analysis and decision-making. Therefore, the significance of data models is to provide data-driven decision support for enterprises and promote business innovation and competitiveness improvement. Provides data-driven decision support for enterprises and promotes
- AI 1018 2024-05-30 12:00:35
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- The Turing giant appeared at ICLR and went crazy for stars LeCun and Bengio at the summit! Three major technology trends of Chinese teams set off new imagination of AGI
- In the past few days, the grand conference of AI - ICLR was held in Vienna. OpenAI, Meta, Google, Zhipu AI and other world-leading AI technology companies gathered together. Celebrities gathered at the venue, the stars were dazzling, and you could just walk a few steps and bump into a celebrity who had published a subversive paper. Not surprisingly, the ICLR2024 exhibition hall also became a star-chasing scene. The lively atmosphere almost blew the roof off. LeCun, the famous "e-man" among the Turing Big Three, who was a star-chasing Turing giant at the event, generously announced his itinerary on X in advance, and was waiting with great anticipation to meet fans. In the comment area, not only were fans excited to check in, but some were even ready to submit their resumes on the spot. The fans' trip was indeed worthwhile. At the scene, LeCun explained eloquently and the audience watched enthusiastically.
- AI 756 2024-05-30 11:17:17
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- Multimodal AI is the future of medicine. Google launches three new models, and Med-Gemini welcomes a major upgrade
- Editor | Cabbage Leaf Many clinical tasks require understanding of specialized data, such as medical images, genomics, etc. This kind of professional knowledge information usually does not exist in the training of general multi-modal large models... As described in the previous paper, Med-Gemini surpassed the GPT-4 series models in various medical imaging tasks to achieve SOTA! Here, GoogleDeepMind authored a second paper on Med-Gemini. Building on Gemini's multimodal model, the team developed multiple models for the Med-Gemini series. These models inherit the core functionality of Gemini and are fine-tuned for 2D and 3D radiology, histopathology, ophthalmology, dermatology and genomics data.
- AI 1332 2024-05-30 10:13:19
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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 872 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 883 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 902 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 709 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 825 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 1033 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 597 2024-05-09 16:55:01
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