On May 30, Tencent announced a comprehensive upgrade of its Hunyuan model. The App "Tencent Yuanbao" based on the Hunyuan model was officially launched and can be downloaded from Apple and Android app stores. Compared with the Hunyuan applet version in the previous testing stage, Tencent Yuanbao provides core capabilities such as AI search, AI summary, and AI writing for work efficiency scenarios; for daily life scenarios, Yuanbao's gameplay is also richer and provides multiple features. AI application, and new gameplay methods such as creating personal agents are added. "Tencent does not strive to be the first to make large models." Liu Yuhong, vice president of Tencent Cloud and head of Tencent Hunyuan large model, said: "In the past year, we continued to promote the capabilities of Tencent Hunyuan large model. In the rich and massive Polish technology in business scenarios while gaining insights into users’ real needs

Tan Dai, President of Volcano Engine, said that companies that want to implement large models well face three key challenges: model effectiveness, inference costs, and implementation difficulty: they must have good basic large models as support to solve complex problems, and they must also have low-cost inference. Services allow large models to be widely used, and more tools, platforms and applications are needed to help companies implement scenarios. ——Tan Dai, President of Huoshan Engine 01. The large bean bag model makes its debut and is heavily used. Polishing the model effect is the most critical challenge for the implementation of AI. Tan Dai pointed out that only through extensive use can a good model be polished. Currently, the Doubao model processes 120 billion tokens of text and generates 30 million images every day. In order to help enterprises implement large-scale model scenarios, the beanbao large-scale model independently developed by ByteDance will be launched through the volcano

Pay attention, this man has connected more than 1,000 large models, allowing you to plug in and switch seamlessly. Recently, a visual AI workflow has been launched: giving you an intuitive drag-and-drop interface, you can drag, pull, and drag to arrange your own workflow on an infinite canvas. As the saying goes, war costs speed, and Qubit heard that within 48 hours of this AIWorkflow going online, users had already configured personal workflows with more than 100 nodes. Without further ado, what I want to talk about today is Dify, an LLMOps company, and its CEO Zhang Luyu. Zhang Luyu is also the founder of Dify. Before joining the business, he had 11 years of experience in the Internet industry. I am engaged in product design, understand project management, and have some unique insights into SaaS. Later he

Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A

According to news on April 4, the Cyberspace Administration of China recently released a list of registered large models, and China Mobile’s “Jiutian Natural Language Interaction Large Model” was included in it, marking that China Mobile’s Jiutian AI large model can officially provide generative artificial intelligence services to the outside world. . China Mobile stated that this is the first large-scale model developed by a central enterprise to have passed both the national "Generative Artificial Intelligence Service Registration" and the "Domestic Deep Synthetic Service Algorithm Registration" dual registrations. According to reports, Jiutian’s natural language interaction large model has the characteristics of enhanced industry capabilities, security and credibility, and supports full-stack localization. It has formed various parameter versions such as 9 billion, 13.9 billion, 57 billion, and 100 billion, and can be flexibly deployed in Cloud, edge and end are different situations

1. Background Introduction First, let’s introduce the development history of Yunwen Technology. Yunwen Technology Company...2023 is the period when large models are prevalent. Many companies believe that the importance of graphs has been greatly reduced after large models, and the preset information systems studied previously are no longer important. However, with the promotion of RAG and the prevalence of data governance, we have found that more efficient data governance and high-quality data are important prerequisites for improving the effectiveness of privatized large models. Therefore, more and more companies are beginning to pay attention to knowledge construction related content. This also promotes the construction and processing of knowledge to a higher level, where there are many techniques and methods that can be explored. It can be seen that the emergence of a new technology does not necessarily defeat all old technologies. It is also possible that the new technology and the old technology will be integrated with each other.

If 2023 is recognized as the first year of AI, then 2024 is likely to be a key year for the popularization of large AI models. In the past year, a large number of large AI models and a large number of AI applications have emerged. Manufacturers such as Meta and Google have also begun to launch their own online/local large models to the public, similar to "AI artificial intelligence" that is out of reach. The concept suddenly came to people. Nowadays, people are increasingly exposed to artificial intelligence in their lives. If you look carefully, you will find that almost all of the various AI applications you have access to are deployed on the "cloud". If you want to build a device that can run large models locally, then the hardware is a brand-new AIPC priced at more than 5,000 yuan. For ordinary people,

If the test questions are too simple, both top students and poor students can get 90 points, and the gap cannot be widened... With the release of stronger models such as Claude3, Llama3 and even GPT-5 later, the industry is in urgent need of a more difficult and differentiated model Benchmarks. LMSYS, the organization behind the large model arena, launched the next generation benchmark, Arena-Hard, which attracted widespread attention. There is also the latest reference for the strength of the two fine-tuned versions of Llama3 instructions. Compared with MTBench, which had similar scores before, the Arena-Hard discrimination increased from 22.6% to 87.4%, which is stronger and weaker at a glance. Arena-Hard is built using real-time human data from the arena and has a consistency rate of 89.1% with human preferences.