Big data development has been a major driver in the advancement of artificial intelligence. While the term "big data" predates "artificial intelligence," it encompasses both hardware infrastructure and software analytics. However, to truly understand the relationship between big data and AI, we must start from their core: big data refers to massive, multi-dimensional, and multi-format datasets.
The evolution of artificial intelligence often involves a learning process. Today’s breakthroughs in AI are largely due to the rapid growth of big data over the years. With the emergence of various sensors and data collection technologies, we now have access to unprecedented volumes of data. This allows for detailed analysis in specific domains, creating the foundation for achieving "intelligence" in those areas.
Professor Huang Yihua from the Department of Computer Science at Nanjing University delivered a keynote titled “Large-Data Machine Learning: From Algorithms to Systems.†He discussed the current state of big data analysis technology, both domestically and internationally, as well as key technical challenges, major platform tools, and future trends in the field.
As a member of the Big Data Expert Committee of the Chinese Computer Society, Professor Huang leads a big data lab that has contributed significantly to open-source projects. His work underscores the growing importance of big data in shaping the AI landscape.
Artificial Intelligence and Robotics
In recent years, AI and robotics have become prominent topics in everyday life. Many people confuse the two, but the relationship can be seen as follows: AI provides robots with cognitive abilities, while robots serve as the physical embodiment of AI. Although they are not inherently linked, the synergy between them has grown stronger over time.
Today, leading robot companies integrate AI as a core strategic goal. Whether in industrial or service settings, next-generation robots equipped with AI offer significant advantages over traditional models.
At the conference, Professor Xi Ning from the University of Hong Kong and Chair of IEEE RAS shared insights on the connection between AI and robotics. With extensive experience in robotics, automation, and intelligent control, he has made substantial contributions to AI research.
Intelligent Computing Technology in the Age of AI
Historically, intelligent computing has been closely tied to AI, especially neural network development. However, symbolic reasoning-based AI is rule-dependent, whereas modern AI systems learn and adapt autonomously. Neural networks, though powerful, struggle with small sample problems, leading to some skepticism.
In recent years, innovations like SVM, kernel methods, and deep learning have emerged, enabling intelligent computing to handle both large-scale and small-sample data effectively. These advancements have made the technology more 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-driven intelligent computing is essential for unlocking the value of big data.
At the conference, Professor Wang will present a talk on “Multi-granular Big Data Intelligent Computing,†introducing new approaches in multi-granularity computation, recognition, clustering, decision-making, and problem-solving.
Visual Search and Recognition System
A 9-second surveillance video recently went viral, showcasing advanced capabilities. Unlike traditional monitoring, this system accurately identifies vehicle types, pedestrians’ age, gender, and clothing, offering clear and detailed information.
This system is part of China's “Skynet†project and uses computer vision to detect and locate pedestrians in images or videos. Combined with AI, it is widely used in smart devices, behavior analysis, and intelligent transportation.
Professor Ji Rongji from Xiamen University presented a keynote on “The Compactness of Visual Search and Recognition System.†He discussed recent progress in compact feature representation for visual applications, highlighting the university’s work in big data-driven multimedia content retrieval and visual understanding.
Intelligent System Cognition and Reasoning
The ultimate goal of AI is to make computers function as intelligent systems capable of human-like cognition and reasoning. Achieving this in real-world systems is crucial for practical AI applications.
To realize this, AI systems must integrate neural networks, computer science, and decision-making technologies. This multidisciplinary approach enables effective data analysis and processing.
Professor Lin Fangzhen, a professor at the Hong Kong University of Science and Technology and an AAAI Fellow, focuses on AI in cognition and reasoning. His keynote, “Cognition and Reasoning of Intelligent Systems,†explored how AI systems can incorporate common sense, domain knowledge, and normative reasoning.
With a Ph.D. from Stanford and numerous awards, Professor Lin is a leading voice in AI research.
In addition to an impressive lineup of speakers, the conference will highlight key academic and R&D advancements in AI this year. It will also explore future market opportunities through in-depth professional forums, bringing together thousands of AI experts, industry leaders, and analysts. Alongside these discussions, the event will host a prestigious annual award ceremony, recognizing outstanding achievements and contributions to the AI industry.
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