Big data development has become a major driving force behind the advancement of artificial intelligence. While the term "big data" predates "artificial intelligence," it encompasses both hardware platforms and software analysis technologies. To better understand their relationship, we must look at the core nature of big data: it refers to massive, multi-dimensional, and multi-format datasets.
The evolution of artificial intelligence often involves a learning process. Major breakthroughs in AI have largely been fueled by the rapid growth of big data over the years. With the emergence of various sensors and data collection technologies, we now have access to vast amounts of data that were once unimaginable. This data allows for detailed analysis in specific areas, laying the foundation for achieving intelligence in those domains.
Professor Huang Yihua from the Department of Computer Science at Nanjing University will present a talk titled “Large-Data Machine Learning: From Algorithms to Systems.†He will discuss big data analysis technologies, current domestic and international developments, key technical challenges, major tool platforms, and future trends in the field.
Professor Huang is also a member of the Big Data Expert Committee of the Chinese Computer Society. His big data lab has contributed significantly to several open-source projects, highlighting his leadership in the field.
Artificial Intelligence and Robotics
In recent years, artificial intelligence and robotics have become central topics in everyday life. Many people confuse AI with robots, but the relationship between them can be seen as follows: AI provides robots with the ability to think, while robots demonstrate the external performance of AI. Although they are not inherently connected, their mutual development has created a strong, intertwined relationship.
Today, many robot companies focus on AI as a key strategic goal. Whether in industrial or service robots, the integration of AI technology offers significant advantages over traditional models.
At the conference, Professor Xi Ning from the University of Hong Kong and Chair of IEEE RAS will share insights on the connection between AI and robotics. With extensive research in robotics, automation, and intelligent control, Professor Xi has made notable contributions to the field of AI.
Intelligent Computing Technology in the Age of Artificial Intelligence
Historically, intelligent computing has been closely tied to AI, especially neural network development. However, symbolic reasoning-based AI is highly rule-dependent, whereas self-learning and adaptive neural networks offer more flexibility. Yet, these methods struggle with small sample datasets, leading to some criticism.
Recent advancements like SVM, kernel methods, and deep learning have addressed these limitations. These techniques now enable intelligent computing to handle both large-scale data and small samples, making them increasingly appealing.
Professor Wang Guojun, chief scientist of the National Key R&D Project and Dean of the Graduate School at Chongqing University of Posts and Telecommunications, emphasizes that big data intelligent computing is crucial for unlocking the value of big data.
Professor Wang will deliver a presentation on “Multi-granular Big Data Intelligent Computing,†introducing innovative approaches in multi-granularity computation, recognition, clustering, decision-making, and joint problem-solving.
Visual Search and Recognition System
Recently, a 9-second surveillance video gained widespread attention. Unlike traditional monitoring systems, this video accurately identifies vehicle types, pedestrians' age, gender, and clothing. The clarity of the information highlights the power of modern AI-driven systems.
This system is part of China’s “Skynet†project, utilizing computer vision to detect and locate pedestrians in images and videos. Combined with AI, it's widely used in smart devices, behavior analysis, and intelligent transportation.
Professor Ji Rongji from Xiamen University will present a keynote speech titled “The Compactness of Visual Search and Recognition System.†He will discuss visual feature compression and its applications in media analysis and computational research.
Professor Ji has focused on big data-driven intelligent processing systems for multimedia retrieval, social media analysis, and visual understanding, achieving significant results in the field.
Intelligent System Cognition and Reasoning
The ultimate goal of AI is to make computers function as intelligent systems akin to humans. Cognition and reasoning are considered the most concentrated expressions of AI. Achieving cognitive and reasoning capabilities in intelligent systems is vital for real-world applications.
To realize this, systems must integrate neural networks, computer technology, and intelligent decision-making. This multidisciplinary approach enables effective data analysis and processing.
Professor Lin Fangzhen, from the Hong Kong University of Science and Technology and a Fellow of AAAI, will deliver a keynote on “Cognition and Reasoning of Intelligent Systems.†He will explore how AI systems can incorporate common sense, domain knowledge, and normative knowledge.
Professor Lin holds a Ph.D. from Stanford University and has received numerous awards, including the Hong Kong Yankee Foundation Researcher Award and best paper honors at top AI conferences.
In addition to an impressive lineup of speakers, the conference will highlight the most significant academic and R&D achievements in AI this year. It will also provide insights into future market opportunities. Parallel professional forums will bring together thousands of AI professionals from leading global companies, industry experts, and research organizations. These events will showcase successful AI applications, promote collaboration between industry and academia, and recognize outstanding contributions through an annual award ceremony.
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