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Recently, Professor Zhai Jidong's team from the Institute of High Performance Computing at Tsinghua University, in collaboration with Tsinghua-affiliated innovative enterprise, Qingcheng Extreme Intelligence, announced the open-sourcing of Chitu, a groundbreaking large model inference engine. This innovative technology marks another significant breakthrough for China in the AI field, particularly in inference engine development. A core highlight of the Chitu engine is its ability to natively run FP8 precision models on non-Nvidia Hopper architecture GPUs and various domestic chips.

On the morning of February 17, Tencent Holdings' stock price surged in the Hong Kong market, rising over 7.5% at one point, leading to an increase in market value of approximately 320 billion Hong Kong dollars (about 298.1 billion yuan). Related concept stocks also showed strong performance, with Weimob Group (02013.HK) up 15%, Yuanshen Group (00772.HK) up 11.97%, and Youzan (08083.HK) up 10.16%. The significant stock price increase may be related to the latest developments in WeChat Search. On February 16, Tencent announced that WeChat Search has integrated the mixed large model to provide AI search functionality.

Recently, research teams from Tsinghua University, Fudan University, and Stanford University jointly released an Agent development framework called 'Eko'. This framework aims to help developers quickly build production-ready 'virtual employees' using simple code and natural language. The Eko framework can take over a user's computer and browser, performing various tedious tasks on behalf of humans. With Eko, users can achieve automated data collection, testing, and file management, among other functionalities. For instance, users can set Eko to automatically gather information from Yahoo Finance.

With the development of digital art, automated image processing technology has received increasing attention. Recently, a research team from Tsinghua University and Tencent's ARC Lab proposed a novel image sequence coloring model called ColorFlow. This model aims to address the challenge of maintaining consistency in character and object identity while coloring black and white image sequences, fulfilling the practical needs of industries such as comics and animation. ColorFlow is a three-stage diffusion base framework that leverages contextual information by utilizing a reference image pool for black and white.

In recent years, large language models represented by ChatGPT have sparked a new wave in the AI field. These powerful language models can not only understand human instructions but also formulate plans, explore environments, and utilize tools to solve complex tasks, demonstrating immense potential in areas such as robotics, personal assistants, and process automation. However, most existing AI entity systems are passive, requiring explicit human instructions to perform tasks. To schedule a meeting, one still has to manually input the time, location, and even list out all the participants, which is quite cumbersome.

Tencent Docs has recently launched version 3.9.0, featuring a comprehensive upgrade of the AI Document Assistant to enhance user experience and work efficiency. The new version deeply integrates the three core office functions of 'reading, writing, and searching', aiming to provide a one-stop solution for users' work challenges. In terms of 'reading', the AI Document Assistant's deep reading capability has significantly improved, supporting intelligent close reading for niche scenarios. Users can upload research reports, lengthy documents, and more, and the AI assistant will quickly extract key information. The upgraded assistant supports the upload of up to 50 files at once.

Kimi Technology Co., Ltd. and Tsinghua University's MADSys laboratory have jointly released an open-source project called Mooncake, aimed at collaboratively building a large model inference architecture centered around KVCache. In June 2024, both parties released the design plan for the Mooncake inference system based on Kimi, which significantly enhances inference throughput through PD separation and a storage-computation architecture, garnering widespread attention in the industry.

In an era of rapid development in artificial intelligence, the intelligence level of large models continues to improve, but the challenges of inference system efficiency are becoming increasingly apparent. Addressing high inference loads, reducing inference costs, and shortening response times has become an important issue faced by the industry. Kimi has collaborated with the MADSys laboratory at Tsinghua University to launch the KVCache-based Mooncake inference system design plan, which will be officially released in June 2024. The Mooncake inference system enhances efficiency through its innovative approach.

Recently, researchers from ByteDance Research Institute and Tsinghua University jointly released a new study, pointing out that current AI video generation models, such as OpenAI's Sora, while capable of creating stunning visual effects, have significant flaws in understanding basic physical laws. This study has sparked widespread discussion on the capabilities of AI in simulating reality. The research team tested AI video generation models under three different scenarios: predictions under known patterns and predictions under unknown patterns.

The rapid development of deep learning relies on the scale of datasets, models, and computational power. In the fields of natural language processing and computer vision, researchers have already discovered a power-law relationship between model performance and data scale. However, in the field of robotics, especially in robot manipulation, such scalable patterns have yet to be established. A research team from Tsinghua University recently published a paper exploring data scaling laws in robot imitation learning and proposed an efficient data collection strategy that collected sufficient data in just one afternoon, enabling...

The research team at Tsinghua University recently released a mobile sound source simulation platform named SonicSim, aiming to address the current data scarcity in the field of voice processing for mobile sound source scenarios. This platform is built on the Habitat-sim simulation platform, capable of realistically simulating real-world acoustic environments, providing higher quality data support for the training and evaluation of voice separation and enhancement models. Existing voice separation and enhancement datasets are mostly based on static sound sources, which are inadequate for the needs of mobile sound source scenarios.

In a recent sensational news in the mathematical community, a group of alumni from Tsinghua University has successfully proven 162 previously unsolved mathematical theorems with the help of AI. Even more impressively, this intelligent agent named LeanAgent has tackled Terence Tao's formal challenge regarding the Polynomial Freiman-Ruzsa Conjecture! This has led us to marvel at how AI has completely transformed the research methods in basic scientific studies. It is well known that current language models (LLMs), while impressive, still have limitations.