In midsummer and August, the sun is like fire and the vegetation is lush, everything shows the wild and poetic vitality. Summer is a season of exploration, growth, and innovation. In this season that belongs to practitioners, 51CTO brought an AI event with the theme of "Drive·Innovation·Digital Intelligence".
AI technology was born less than a century ago. After several ups and downs, it has ushered in a golden period of comprehensive development and implementation in the past 20 years. What are the current cutting-edge technological achievements and practical innovation breakthroughs in the field of AI? How do you view the next decade of AI? This is an issue lingering in the minds of many technicians.
On August 6, the AISummit Global Artificial Intelligence Technology Conference opened as scheduled as an online live broadcast. On the first day, nearly a hundred experts, scholars, technical experts, and management elites gathered together to discuss the wave of "digital intelligence" in the era of artificial intelligence with tens of thousands of participants.
In the opening speech, Mr. Cui Kang, Vice President and Editor-in-Chief of 51CTO, expressed his expectations for the conference as the main planner of this conference. He believes that with the development of artificial intelligence, it is no longer like a segmented technology in the IT field, but more like a general technology. Although its development is improving all the way, it still needs to face many current contradictions. This conference hopes to provide some enlightening clues for these contradictions and find some solutions. At the same time, I also hope that “this conference can tell participants that AI technology and AI developers are making the world a better place.”
In the main forum in the morning, six important guests interpreted the current status and trends of artificial intelligence technology from the perspective of technology leaders, the research findings that practitioners need to pay attention to, and the impact of cutting-edge technology in the industry.
Dou Zhicheng, deputy dean of the Hillhouse School of Artificial Intelligence at Renmin University of China, shared his prospects for the next generation of intelligent search technology. He interpreted the development trends and core features of the new generation of intelligent search technology, and also made a detailed analysis of technologies such as interactive, multi-modal, interpretable search, and large model-centered de-indexed search.
Tian Yuandong, researcher and senior manager of Meta/Facebook Artificial Intelligence Research Institute, focused on analyzing the opportunities and challenges of decision-making in real-world scenarios. Currently, how to use deep neural networks to process structured data and find neural network solutions that replace human heuristic strategies for some discrete optimization problems is still an unsolved problem. Tian Yuandong introduced how to use reinforcement learning and search methods with neural networks to find heuristic algorithms for complex optimization problems.
Pan Qinghua, deputy director of iFlytek Research Institute of HKUST, gave his own judgment on the development trend of artificial intelligence technology using the industry implementation of AI as an entry point. In-depth answers were given around how to form an interactive method that embodies emotions and combines reality and reality, how to create a complex intelligent system that integrates multi-modal modes and integrates software and hardware, and how to develop better solutions for the industry.
Zhou Zongwei, a senior engineer and senior manager at Google and the leader of the MLPerf team, summarized ten points of understanding that affect the design of Google's artificial intelligence chips from multiple perspectives based on the explosive growth in computing power demand in recent years. In terms of his views on the new generation The impact of the architecture and design of the quasi-structured chip was sorted out, and the collaborative evolution of Google's chips and software was interpreted.
Xiang Liang, head of the Volcano Engine machine learning system, mainly introduced the technological evolution of large-scale machine learning computing power. In the keynote speech, he analyzed the technical difficulties and diminishing returns encountered in the implementation of large models, conducted an in-depth analysis of the promotion and impact between the computing power and machine learning industries, and also analyzed the future development trends of computing power. Predictions were made.
Liu Yi, head of Microsoft's Bing advertising text generation algorithm, took Microsoft's Bing DeepGen project as an example to explain the issues of diversified search advertising text generation and online real-time matching. The advertising text generation technology is introduced in detail, including system brief introduction, basic and diversified generation models, algorithm model, model characteristics and commercial impact of real-time matching of advertising online text.
The four sub-forums held in the afternoon were also exciting and climaxed one after another. Its content covers four major topics: "AI-driven search and recommendation", "MLOps best practices", "machine learning performance optimization road", and "computer vision application and innovation" and 16 subdivided topic directions.
The wave of digital transformation has given rise to a new evolution of search recommendation technology, and recommendation technology has also ushered in a stage of deep integration with intelligent algorithms. In the "AI-driven search and recommendation" sub-forum, senior experts in the field of search and recommendation shared forward-looking thinking in the field of intelligent search and recommendation from the perspective of business practice.
Wang Liang, senior technical expert at Alimama and head of external advertising technology, explained the concept of federated learning and its application in Alibaba advertising; Ma Jianqiang, senior researcher at Tencent and head of online video knowledge graph, explained Tencent’s With the vertical search of video search as the background, it focused on the main technical scenarios, algorithm architecture and progress of video search, short video vector recall, the application of long video IP knowledge graph, etc.; Chen Sheng, head of Meituan search ranking, said "Meituan search" With the theme of "Construction and Practice of Sorting Platform", it explained in detail the technical architecture of Meituan Search, the construction of the sorting platform, and the optimization of the sorting algorithm; Kuaishou senior recommendation algorithm expert Zang Xiaoxue brought Kuaishou's experience in causal inference and Latest research on graph neural network algorithms.
In the large-scale application of artificial intelligence in enterprises, there are challenges such as long R&D rollout cycles and difficulty in matching data and models. MLOps came into being. In the "MLOps Best Practices" sub-forum, experts discussed the practical effects and effectiveness of MLOps around hot topics such as the R&D operation and maintenance cycle, continuous training and continuous monitoring, model version and lineage, online and offline data consistency, and efficient data supply. Cutting edge trends.
Tan Zhongyi, Vice Chairman of TOC of Open Atomic Foundation and member of LF AI & Data TAC, introduced the concept, positioning, main content, common projects of MLOps, and the criteria for evaluating the ability and level of MLOps of an AI team; Chapter 1 Lu Mian, Four Paradigm system architect and head of OpenMLDB R&D, focused on the open source machine learning database OpenMLDB and analyzed how it can achieve the goal of immediately launching machine learning feature development and how to ensure the correctness and efficiency of feature calculation; NetEase Cloud Music Artificial Intelligence Researcher and Technical Director Wu Guanlin started from the background of cloud music business and explained the real-time implementation plan of the model, and the thinking and practice of further radiating to more scenarios by combining FeatureStore; Huang Bingkey, Deputy Director of the Big Data and Artificial Intelligence Laboratory of the Software Development Center of the Industrial and Commercial Bank of China This article introduces the MLOps practice of ICBC, covering the construction process and technical practice of the full life cycle management system of model development, model delivery, model management, and model iterative operation.
In the field of machine learning, enterprises have continuously increasing performance requirements for algorithms: How to ensure the stability of the system? How to use engineering means to solve the problem of insufficient samples and real-time model? How to improve ease of use? In the "Road to Machine Learning Performance Optimization" sub-forum, experts shared their opinions on this.
Yang Yang, chief engineer of Didi, explained the innovative application of personalized recommendations in data operations based on the pain points of traditional data operations, and looked forward to the technologies and businesses that need to be improved in future data operations; 100 people from Tongji University Program Distinguished Researcher and Doctoral Supervisor Wang Haofen explained the key technologies and commonly used data sets involved in multi-strategy question answering of text knowledge and two forms of multi-modal question answering; Cai Qingpeng, a senior algorithm expert at Kuaishou, used reinforcement learning in Kuaishou short video recommendation The system's technical implementation is used as a case to explain Kuaishou's practical experience in online parameter search based on reinforcement learning, two-stage constrained reinforcement learning algorithm, and how to optimize and achieve APP activity; NetEase Cloud Music Algorithm Platform R&D expert Huang Bin introduced NetEase Cloud Music Online The practice and thinking of prediction systems include how to build a high-performance and easy-to-use prediction system, and how to solve problems such as real-time samples and models through engineering means.
Computer vision (CV), as the pioneer of AI technology, is the foundation of many innovative key technologies. In the "Computer Vision Application and Innovation" sub-forum, experts in the CV field conducted in-depth analysis on multiple scenarios such as video quality monitoring, intelligent video creation, and autonomous driving.
Li Jing, director of Alibaba Youku Technology Center and head of Moku Lab, proposed using AI to improve the short video creation process in response to existing problems in the field of short video creation, and shared the exploration and development of Youku's AI video intelligent production system. Practice; SSIMWAVE co-founder and chief researcher Zeng Kai explained how to solve possible problems in the end-to-end video quality monitoring system and the AI-based objective video quality evaluation algorithm; Tang Dong, head of Ant Group's large security image similarity traceability algorithm Qi used Ant Security Technology's "Siyuan" similar traceability engine as a practical case to conduct a technical dismantling of how to identify and trace in open scenarios; if Ma Zhiguo, an expert in smart mobility perception technology, analyzed the lidar solution in autonomous driving and explained the automatic Perception technology involved in driving, and an in-depth analysis of the relationship between data and autonomous driving.
Taking this AISummit conference as an opportunity, 51CTO will continue to explore and try with ecological partners in the future to build an artificial intelligence for the majority of technical personnel A platform for in-depth communication and sharing in the field. On August 7, the conference will also usher in special sessions on intelligent voice, smart finance, and the metaverse. Friends who follow this conference can look forward to the exciting continuation.
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