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Video
The PolyU Academy for Interdisciplinary Research (PAIR) of The Hong Kong Polytechnic University (PolyU) today hosted its inaugural Public Forum for Research and Innovation. Titled “DeepSeek and Beyond”, the keynote speech was delivered by Prof. YANG Hongxia, Associate Dean (Global Engagement) of the PolyU Faculty of Computer and Mathematical Sciences and Professor of the Department of Computing, who highlighted the latest developments in artificial intelligence (AI). The event attracted over a thousand participants, including faculty members, students, alumni, and leaders from the innovation and technology sector, as well as academics and the public. Additionally, over 390,000 viewers tuned in through the live streaming platforms.
The Forum began with a welcoming speech delivered by Prof. CHEN Qingyan, Director of PAIR and Chair Professor of Building Thermal Science of the PolyU Department of Building Environment and Energy Engineering. This was followed by Prof. ZHANG Chenqi, Chair Professor of Artificial Intelligence of the PolyU Department of Data Science and Artificial Intelligence, and Director of the PolyU Shenzhen Research Institute introducing the speaker.
Prof. Zhang said, “The development of large models is at the core of competition in the AI wave. DeepSeek has demonstrated that high-performance AI models can be achieved using fewer and less advanced graphics processing units (GPUs), demonstrating that cutting-edge AI technology can be realised through the optimisation of algorithms.”
The large AI model developed by the mainland Chinese startup DeepSeek has garnered wide acclaim around the world for its low-cost, high-performance, and open-source framework, disrupting the traditional “computing power-first” logic of AI model training. At the Forum, Prof. Yang highlighted the potential of generative AI (GenAI), adding that it presents abundant opportunities for various sectors, including healthcare, finance, manufacturing, retail, media and fashion, and for applications in medical imaging analysis, fraud detection, predictive maintenance, retail inventory management, content creation, and design and marketing.
Prof. Yang also recounted the evolution of AI and shared her professional milestones with the audience, notably the development of the M6 large model, which trained a 10-trillion-parameters model using just 512 GPUs. Prof. Yang further elaborated on how her GenAI project, Co-GenAI, improves the accessibility of AI technology while minimising dependence on large-scale centralised computing resources, thereby transforming the trajectory of AI progress. This ground-breaking effort has positioned Hong Kong and the Mainland at the forefront of global advancement in GenAI.
Moderated by Prof. Zhang Chenqi, a panel discussion was also held, featuring esteemed panellists Prof. Yang Hongxia and Prof. LI Qing, Head and Chair Professor of Data Science of the PolyU Department of Computing, and Co-Director of the Research Centre for Digital Transformation of Tourism. The scholars discussed the opportunities and challenges that advancements in AI present for higher education and research. They also engaged in fruitful discussion with participants during the question-and-answer session. The topics included the application of AI in industry, the regulation of information, its impact on the employment environment and economic development, and the integration of AI technologies.
PolyU is committed to advancing AI education and research. In January 2025, the University established the Faculty of Computer and Mathematical Sciences with a vision to lead global advancements in digital transformation and AI through distinguished education, research, and knowledge transfer.
Event date: 11/03/2025
Speaker: Prof. YANG Hongxia
Hosted by: PolyU Academy for Interdisciplinary Research
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Video
The seminar began with a warm welcome by Prof. CHEN Qingyan, Director of PAIR, followed by a brief introduction of the speaker by Mr Gavin NGAI, Deputy Director of the Global Engagement Office. Dr Tzezana commenced his presentation by identifying the common myths about AI and discussing how these myths hinder public awareness of AI’s rapid development in various fields. He then projected AI’s capabilities towards the end of the decade, suggesting that AI would soon outperform humans in many areas. Dr Tzezanna also examined the broader implications of these advancements, particularly their impact on the future of work, and offered insights into the skills and knowledge areas essential for staying competitive and successful in this rapidly evolving landscape.
Event date: 24/9/2024
Speaker: Dr Roey TZEZANA
Hosted by: PolyU Academy for Interdisciplinary Research
- Subjects:
- Mechanical Engineering and Computing, Data Science and Artificial Intelligence
- Keywords:
- Artificial intelligence
- Resource Type:
- Video
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Video
The seminar began with a warm welcome by Prof. ZHANG Weixiong, Associate Director of PAIR, followed by a brief introduction of the speaker by Prof. ZHANG Chengqi, Chair Professor of Artificial Intelligence. Prof. Liu kick-started his presentation by outlining the key milestones in the evolution of robotics, and pointed out that human-centred intelligent robots should be able to co-exist, cooperate and collaborate with humans. He stated that robotics is a truly interdisciplinary field that combines engineering, science and humanities. Next, through a series of case studies, Prof. Liu examined how intelligent robots have been designed to work alongside humans in various applications, including civil infrastructure maintenance, construction, and manufacturing. He then discussed the dynamics of collaboration between humans and robots, and examined issues such as trust, computational modelling, physical and cognitive workload, brain-robot interface and human-centred design. By reflecting on the lessons learnt from these case studies, Prof. Liu highlighted both successes and challenges. At the end of his presentation, Prof. Liu emphasised that human-robot teaming is an interdisciplinary field. He also pointed out some areas for further development in the field, highlighting the many opportunities in robotics.
Event date: 10/10/2024
Speaker: Prof. LIU Dikai
Hosted by: PolyU Academy for Interdisciplinary Research
- Subjects:
- Mechanical Engineering and Computing, Data Science and Artificial Intelligence
- Keywords:
- Artificial intelligence Robotics Human-robot interaction
- Resource Type:
- Video
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Video
Prof. Alex MIHAILIDIS, Associate Vice President of International Partnerships at the University of Toronto delivered the 19th PAIR Distinguished Lecture titled “The Future of Elder Care: Integrating Large Language Models” on 26 April 2024. The lecture attracted about 100 participants to join in person and captivated an online viewing audience of over 14,100 from different countries and regions to watch the live broadcast on multiple social media platforms, including Bilibili, WeChat, Weibo, YouTube, etc.
The lecture commenced with a welcome speech and speaker introduction by Prof. ZHENG Yongping, Director of the Research Institute for Smart Ageing (RISA), followed by an engaging presentation by Prof. Mihailidis. The content materials used in Prof. Mihailidis’s presentation were generated by ChatGPT, while he added the narrative.
In his presentation, Prof. Mihailidis first played several videos created by artificial intelligence (AI) to introduce what LLM is, how it can support elderly care services, some drawbacks of using LLM, and its future development. He supplemented that LLMs are advanced AI systems capable of understanding and generating human-like texts, as well as visual outputs and models that can respond to or interact with users. Next, Prof. Mihailidis outlined the specific benefits of using LLMs in elderly care, including the support tools and resources they offer to caregivers. He then delved into the ethical considerations and challenges in LLM design, such as privacy concerns, the risk of over-dependence on technology, and the barriers to technology adoption by older adults and their caregivers. To conclude, Prof. Mihailidis emphasised the role of empathetic and emotionally-intuitive AI in enhancing the quality of life for the elderly and supporting the caregiving ecosystem.
Following the presentation was a lively and insightful question-and-answer session moderated by Ir Prof. Zheng. The audience had a fruitful discussion with Prof. Mihailidis. A souvenir was presented by Prof. CHEN Qingyan, Director of PAIR, to thank Prof. Mihailidis for his excellent presentation and support to PAIR.
Event date: 26/04/2024
Speaker: Prof. Alex MIHAILIDIS (University of Toronto)
Hosted by: PolyU Academy for Interdisciplinary Research
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Video
This talk will survey the intersection of artificial intelligence (AI), ethics, and the Humanities in the UK. It integrates insights from bibliometric analyses, interviews with various stakeholders, and reviews of existing research infrastructure and policies. The talk examines the current state of AI ethics research in the UK, identifying the contributions of the Arts and Humanities, the obstacles researchers face, and the potential impacts of their work. It also considers the international research environment and strategic investments made by other countries in AI and ethics, drawing comparisons with the UK's approach. Opportunities and threats are identified within the context of academia, public perception, and commerce, including the impacts of AI on diverse populations and industries. The talk will conclude by considering how the situation in the UK may compare with that in Hong Kong.
Event date: 30/04/2024
Speaker: Prof. Tony MCENERY (Lancaster University and Shanghai International Studies University)
Hosted by: Faculty of Humanities
- Subjects:
- Computing, Data Science and Artificial Intelligence and Philosophy
- Keywords:
- Artificial intelligence -- Moral ethical aspects Artificial intelligence -- Philosophy Great Britain
- Resource Type:
- Video
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Video
PAIR distinguished lecture series: an overview of high performance computing and future requirements
In this talk, we examine how high performance computing has changed over the last ten years and look toward the future in terms of trends. These changes have had and will continue to impact our numerical scientific software significantly. A new generation of software libraries and algorithms are needed for the effective and reliable use of (wide area) dynamic, distributed, and parallel environments. Some of the software and algorithm challenges have already been encountered, such as the management of communication and memory hierarchies through a combination of compile-time and run-time techniques, but the increased scale of computation, depth of memory hierarchies, range of latencies, and increased run-time environment variability will make these problems much harder.
Event date: 6/12/2023
Speaker: Prof. Jack Dongarra
Hosted by: PolyU Academy for Interdisciplinary Research
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- High performance computing
- Resource Type:
- Video
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Video
In this lecture, Prof. Sifakis will discuss the relevance of existing criteria for comparing human and machine intelligence and show some notable analogies and differences between scientific knowledge and that produced by neural networks. Emphasising that autonomy is an important step towards Artificial General Intelligence (AGI), he will present a characterisation of autonomous systems, and showing key differences with mental systems equipped with common sense knowledge and reasoning, and advocate challenging work directions, including the development of a new foundation for systems engineering and scientific knowledge, and the joint exploration of physical and mental phenomena that embody human intelligence.
Event date: 3/3/2023
Speaker: Prof. Joseph Sifakis
Hosted by: PolyU Academy for Interdisciplinary Research
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Artificial intelligence
- Resource Type:
- Video
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MOOC
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
- Course related:
- AAE5103 Artificial Intelligence in Aviation Industry
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Artificial intelligence Machine learning
- Resource Type:
- MOOC
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Others
4RealSim serves its customers by providing Dassault Systemes’ SIMULIA products (Abaqus, Isight, Tosca, fe-safe, Simpack, XFLOW), VCollab and HyperSizer.
4RealSim offers high-quality Finite Element engineering services to assess, validate and optimize the response of products, assemblies and processes.
4RealSim offers training and mentoring to learn the analysis tools and improve FEA methodologies. Trainings are available online or organized in class rooms.
4RealSim offers customer-tailored and regulatory compliant Computer Modelling & Simulation services for the life-science and medical-device industry.
- Subjects:
- Computing, Data Science and Artificial Intelligence and Business Information Technology
- Keywords:
- Abaqus (Electronic resource) Finite element method
- Resource Type:
- Others
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MOOC
Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.
This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand.
Accordingly, in this course, you will learn:
- The major steps involved in tackling a data science problem.
- The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
- How data scientists think!
- Course related:
- LGT6202: Stochastic Models and Decision under Uncertainty, LGT6802 Guided Study in Logistics II, LGT6803: Guided Study in Logistics III, and LGT6801 Guided Study in Logistics I
- Subjects:
- Business Information Technology and Computing, Data Science and Artificial Intelligence
- Keywords:
- Data mining Problem solving Electronic data processing
- Resource Type:
- MOOC