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- Comprehensively surpassing DPO: Chen Danqi's team proposed simple preference optimization SimPO, and also refined the strongest 8B open source model
- In order to align large language models (LLMs) with human values and intentions, it is critical to learn human feedback to ensure that they are useful, honest, and harmless. In terms of aligning LLM, an effective method is reinforcement learning based on human feedback (RLHF). Although the results of the RLHF method are excellent, there are some optimization challenges involved. This involves training a reward model and then optimizing a policy model to maximize that reward. Recently, some researchers have explored simpler offline algorithms, one of which is direct preference optimization (DPO). DPO learns the policy model directly based on preference data by parameterizing the reward function in RLHF, thus eliminating the need for an explicit reward model. This method is simple and stable
- AI 416 2024-06-01 16:41:36
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- The author of ControlNet's new work is a hit: P photos can be changed into backgrounds without asking for help, and AI lighting is perfectly integrated
- The new work by the author of ControlNet is so fun to play that it has received 1.2k stars since it was open sourced. IC-Light, which is used to manipulate image lighting effects, is called lmposingConsistentLight. The gameplay is very simple: Uploading means that the system will automatically separate the characters and other subjects, select the light source position, fill in the prompt words, and you can integrate into the new environment without any flaws! Hurry up and do some Wong Kar-wai style lighting: Don’t like it? It doesn't matter, it only takes a few minutes to switch to the natural light coming in from the window. Currently, IC-Light provides two types of models: text conditional relighting models and background conditional models. Both models require a foreground image as input. Since Controlnet was so fun before, this time IC
- AI 1080 2024-06-01 16:23:10
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- A new milestone in controllable nuclear fusion, AI achieves fully automatic optimization of dual tokamak 3D field for the first time, published in Nature sub-issue
- Editor | Now, at the Princeton Plasma Physics Laboratory (PPPL), scientists are turning to artificial intelligence to solve a pressing challenge facing humanity: generating clean, reliable energy from fusion plasma. Unlike traditional computer code, machine learning is more than just a list of instructions. It can analyze data, infer relationships between features, and learn and adapt from new knowledge. PPPL+ researchers believe this ability to learn and adapt could improve their control of fusion reactions in a variety of ways. This includes perfecting the design of the vessel surrounding the superheated plasma, optimizing heating methods, and maintaining stable control of the reaction over increasingly longer periods of time. close
- AI 674 2024-06-01 15:57:53
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- Palm Reading Technology joins hands with Amazon Cloud Technology to reshape the reading experience with the power of generative AI
- In the digital age, although obtaining information has become easy, readers still face many challenges in enjoying reading. From the confusion of choosing books, to the distraction during reading, to the absorption and organization of knowledge after reading, these constitute a series of difficulties in the reader's experience. As a leading company in the field of digital reading, Zhangyue Technology has a deep understanding of users' different reading needs and explores the use of generative AI technology to reshape the reading experience. With the help of relevant technologies of Amazon Cloud Technology, we can enrich diversified reading scenarios such as Vincent pictures and Vincent videos, and create a digital and immersive innovative reading experience for users. Reaching the core of the essence of reading and reshaping the reading experience. Sun Kai pointed out that from the past development trends of the reading market, we have seen that with the continuous upgrading and iteration of technology and equipment,
- AI 720 2024-06-01 15:02:07
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- Li Feifei reveals the entrepreneurial direction of 'spatial intelligence': visualization turns into insight, seeing becomes understanding, and understanding leads to action
- After Stanford's Feifei Li started his business, he unveiled the new concept "spatial intelligence" for the first time. This is not only her entrepreneurial direction, but also the "North Star" that guides her. She considers it "the key puzzle piece to solve the artificial intelligence problem." Visualization leads to insight; seeing leads to understanding; understanding leads to action. Based on Li Feifei's 15-minute TED talk, which is fully open to the public, it starts from the origin of life evolution hundreds of millions of years ago, to how humans are not satisfied with what nature has given them and develops artificial intelligence, to how to build spatial intelligence in the next step. Nine years ago, Li Feifei introduced the newly born ImageNet to the world on the same stage - one of the starting points for this round of deep learning explosion. She herself also encouraged netizens: If you watch both videos, you will be able to understand the computer vision of the past 10 years.
- AI 964 2024-06-01 14:55:34
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- Tencent Hunyuan upgrades model matrix, launching 256k long text model on the cloud
- The implementation of large models is accelerating, and "industrial practicality" has become a development consensus. On May 17, 2024, the Tencent Cloud Generative AI Industry Application Summit was held in Beijing, announcing a series of progress in large model development and application products. Tencent's Hunyuan large model capabilities continue to upgrade. Multiple versions of models hunyuan-pro, hunyuan-standard, and hunyuan-lite are open to the public through Tencent Cloud to meet the model needs of enterprise customers and developers in different scenarios, and to implement the most cost-effective model solutions. . Tencent Cloud releases three major tools: knowledge engine for large models, image creation engine, and video creation engine, creating a native tool chain for the era of large models, simplifying data access, model fine-tuning, and application development processes through PaaS services to help enterprises
- AI 525 2024-06-01 13:46:36
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- Working with Amazon Cloud Technology, Beijing Lingao Technology helps enterprises seamlessly combine large models and data
- As the era of generative AI continues to develop, more and more companies are beginning to pay attention to, apply and even train large models, hoping to create greater business value. This idea is certainly correct. The emergence of large models will definitely bring innovation and breakthroughs to enterprises. However, the basic model relies on large-scale high-quality data sets. The key to using generative AI to bring differentiated advantages to enterprises is the enterprise's proprietary data. Data is one of the core competitiveness of enterprises. Recently, Amazon Cloud Technology held a media communication meeting with the theme of "No Data, No Model - Data Base in the Generative AI Era". At the meeting, Li Qipeng, CEO of Beijing Lingao Technology, introduced how Vanus can help enterprises solve data problems and thus seamlessly integrate with large models. Beijing Lingao Technology is a fast-growing start-up company
- AI 1074 2024-06-01 12:48:21
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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 403 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 641 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 740 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 902 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 614 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 723 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 552 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 1115 2024-05-30 10:13:19
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