主講人：林昕璇教授 (成功大學政治學系)
SPEAKER
題　目：AI自主性武器系統之國際規範秩序-以國際人道法為中心
TOPIC
時 間：113年9月27日(五)上午10點至12點
DATE　　
地　點： 清華大學台達館108教室
PLACE

Abstract:
This presentation explores the international regulatory framework governing AI autonomous weapon systems, focusing on the principles of international humanitarian law (IHL). It examines the ethical, legal, and strategic challenges posed by the deployment of AI in military operations, including autonomous decision-making in weapons systems and their implications for human oversight. Through a review of historical examples and recent developments, the study highlights the tension between technological advancements in autonomous weaponry and the need for regulatory measures to ensure compliance with international law. The presentation further discusses the role of major international actors, such as the United States and China, in shaping the global governance of AI in military contexts.

主講人：唐鳳 Audrey Tang (前數位發展部部長)
SPEAKER
題　目：人工智慧時代下的民主 Democracy in the Age of AI
TOPIC
時 間：113年10月11日(五)上午10點至12點
DATE　　
地　點：台積館 1F 孫運璿演講廳
PLACE
Abstract:
因應現代科技發展與人工智慧浪潮，使用這些便利、高效率的技術提升工作與生活的同時，無法忽略其對於倫理、社會和法律層面的影響。當科技進步而保護規範或道德標準停滯不前時，該如何避免侵害、建立更具責任的人工智慧環境，更是現代新興技術發展不可或缺的核心價值。人工智慧的倫理現今變得由為重要，不只是私人企業連政府也逐漸開始使用並透過人工智慧運作協助其工作的進行。然而人工智慧的使用卻衍伸出了倫理和權利上的議題，包含隱私、安全、公平、平等、問責性、人權及民主 等問題。
為確保課程的內容更具備落地性，本課程將在10/11(五) 10:00am-12:00pm盛邀數位政策專家唐鳳至清大演講。透過其專業、實踐的經驗和同學們分享人工智慧 時代對於民主的影響為何態樣。究竟人工智慧的使用是會促進民主的蓬勃發展，還是成為另一種隱性的阻礙，甚至現有的民主呢？
In light of the rapid advancements in modern technology and the surge of artificial intelligence, it is essential to recognize the impact these convenient and efficient technologies have on ethics, society, and legal frameworks. As technology progresses, yet protective re gulations and moral standards lag behind, addressing how to prevent violations and establish a more responsible AI environment has become a critical value in the development of emerging technologies.
Artificial intelligence ethics are increasingly signifi cant today. Not only are private enterprises adopting AI, but governments are also integrating these technologies into their operations. However, the use of AI raises important ethical and rights related issues, including privacy, security, fairness, equal ity, accountability, human rights, and democracy.
To delve into these pressing issues, we are excited to announce a special lecture by renowned digital policy expert Audrey Tang, taking place on October 11 (Friday) at National Tsing Hua University. With her extensive expertise and practical experience, Tang will explore the impact of the artificial intelligence era on democracy, offering insights into whether AI will enhance democratic development or pose new challenges to existing democratic systems.

【報名連結 Registration link 】
https://forms.gle/Gv2YUtit2utzxRFNA

相關網址:https://cvgip2024.csie.ndhu.edu.tw/news/best-paper/

主講人：Prof. Jing-Ming Guo
1.Distinguished Professor, National Taiwan University of Science and Technology
2.Chief Technology Officer, Industrial Technology Research Institute
題　目：Development Trends and Considerations for Practical Applications of Generative AI
時 間：113年05月24日(五)下午1點20分至3點
地 點：台達館104教室

Title of speech
This presentation explores the latest trends in Generative AI, focusing on key topics such as the evolution of GPT, domain-specific optimization in large language models (LLMs), micro-sizing LLMs, and the deployment of Generative AI in Industrial Technology Research Institute (ITRI) and its potential expansion to Taiwan enterprises. The talk delves into the advancements in GPT models, highlights the significance of expert-level optimization techniques in enhancing LLM performance, and discusses strategies for scaling down large models. Moreover, it showcases the current efforts by ITRI in implementing Generative AI and outlines the future prospects of adopting this technology within Taiwan's business landscape.
Biography
Prof. Guo is currently a full Professor with the Department of Electrical Engineering, and Director of Advanced Intelligent Image and Vision Technology Research Center. He was Vice Dean of the College of Electrical Engineering and Computer Science, and Director of the Innovative Business Incubation Center, Office of Research and Development. His research interests include Big data signal processing, artificial intelligence, digital image/video processing, computer vision. Dr. Guo is a Senior Member of the IEEE and Fellow of the IET.
Dr. Guo is Chapter Chair of IEEE Signal Processing Society, Taipei Section, Board of Governor member of Asia Pacific Signal and Information Processing Association, and Chair of IET Taipei Local Network. He was General Chair of many international conferences, e.g., APSIPA 2023, IEEE Life Science Workshop 2020, ISPACS 2019, IEEE ICCE-Berlin 2019, IWAIT 2018, and IEEE ICCE-TW 2015. He was Technical Program Chair of many international conferences as well, e.g., IEEE ICIP 2023, IWAIT 2022, IEEE ICCE-TW 2014, IEEE ISCE 2013, and ISPACS 2012. He is/was Associate Editor of the IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technologies, IEEE Transactions on Multimedia, IEEE Signal Processing Letters, Information Sciences, Signal Processing, and Journal of Information Science and Engineering.

主講人：謝君偉教授（陽明交通大學）
題　目：Pre-trained Models與LLM結合測試
時 間：113年05月21日(二)上午10點10分至12點
地　點：台達館102教室

Abstract:
Large Language Models (LLMs) have demonstrated incredible potential in generating text that resembles human writing, as shown in numerous studies. Despite their impressive abilities, training them from scratch is not easy. Recently, model reuse has become a key technique, allowing developers to utilize pre-trained models, ranging from small ones to large language models, to improve the performance and efficiency of their machine learning systems. This presentation will explore how to establish connections between the widely adopted practice of model reuse across different domains and covers various stages of leveraging Pre-Trained Models (PTMs), including data preparation, architecture design, model training, and model inference. In addition to providing a comprehensive overview of typical methods for model reuse, we'll address the important issue of how to retrain a model using cost-effective GPUs like Nvidia 4090. We'll also discuss solutions and results based on Phison’s aiDAPTIV platform.

主講人：李承緒 副總 (訊連科技)
題　目：電腦視覺在業界的開發實務與應用
時 間：113年05月17日(五)下午1點20分至3點　
地　點：台達館104教室

Abstract:
近幾年來AI與AR技術突飛猛進，許多技術已經應用在日常生活上了，尤其是生成式AI更是帶來新技術的大躍進及應用的大爆發，因此也造就了許多商機。
訊連科技與玩美移動兩家軟體服務公司，分別投入了許多相關的研究與開發，本次演講將以技術與應用的層面介紹這兩家公司在AI與AR的研究開發成果。

Speaker：
李承緒，於1996年台大資訊工程系碩士畢業，目前於訊連科技擔任研發部資深副總，帶領超過兩百位工程師研發AI在多媒體及電腦視覺的技術與應用。

主講人：孔祥重 院士
題　目：Be a Thinker: Prepare for the New Era of Generative AI
時 間：113年04月30日(二)下午1點00分至2點10分
地　點：台達館104教室


Abstract:
We are in the beginning of the generative AI era where much of our society will undergo fundamental changes. There will be many new opportunities for everyone, but also lots of ncertainties that could be difficult to understand. Being able to think through issues deeply would be more important than ever. I would encourage practicing thinking and suggest some techniques.
Speaker：
H. T. Kung is William H. Gates Professor of Computer Science and Electrical Engineering at Harvard University. He conducts research on topics related to the application of artificial intelligence in manufacturing and healthcare, AI accelerators, VLSI design, high-performance computing, parallel and distributed computing, computer architectures, and computer networks.

主講人：Dr. Tsung-Wei Ke (CMU)
題　目：Learning Visual Perception that Foundation Models Haven’t Learned
時 間：113年01月08日(一)下午13點30分至14點30分
地　點：台達館613會議室

Abstract:
Foundation models have achieved significant success in computer vision research. Vision-Language Models (VLMs) seem promising to address recognition challenges. VLMs excel at picking out fine-grained semantics, including those unseen during training. The Segment Anything Model (SAM) is a strong and general image parser, which generates segmentation masks of any given prompt. However, these models have not fully solved visual perception. Recognition models, including VLMs, are not robust to distribution drifts, caused by factors such as occlusion, sensorial noise, and domain gap. Segmentation models, including SAM, fail to discover and localize parts from the whole in the image. Pixel groupings are often inconsistent at different segmentation granularities. In this talk, I will present our recent works that address both challenges. First, we combined the state-of-the-art image generative models and recognition models. By optimizing the recognition models with the generative objectives, we improved the recognition of out-of-distribution images. Lastly, we introduced unsupervised hierarchical image segmentation frameworks, which generate consistent pixel groupings across segmentation hierarchy.

Bio:
Tsung-Wei is a postdoctoral researcher at CMU, working with Katerina Fragkiadaki. He obtained his Ph.D degree from UC Berkeley, working with Stella Yu. He is interested in the field of computer vision and embodied AI.

Topic: Recent results on learning with diffusion models
Speaker: Ming-Hsuan Yang
Time: 112/12/25(Mon.)10:00-11:30
Place: Delta R106
Abstract:
Diffusion models have been successfully applied to text-to-image generation with state-of-the-art performance. In this talk, I will discuss how these models can be used for low-level vision tasks and 3D scenes. First, I will present our findings on exploiting features from diffusion models and transformers for zero-shot semantic correspondence and other applications. Next, I will describe how we exploit diffusion models as effective prior for dense prediction, such as surface normal, depth, and segmentation. I will then discuss how diffusion models can f a c i l i t a t e a r t i c u l a t e d 3 D reconstruction, 3D scene generation, and novel view synthesis. When time allows, I will present other results on fine-grained text-to-image generation and pixel-wise visual grounding of large multimodal models.

Bio:
Ming-Hsuan Yang is a Professor at UC Merced and a Research Scientist with Google. He received the Google Faculty Award in 2009 and CAREER Award from the National Science Foundation in 2012. Yang received paper awards at UIST 2017, CVPR 2018, ACCV 2018, and Longuet-Higgins Prize in CVPR 2023.
He is an Associate Editor-in-Chief of PAMI, Editor-in-Chief of CVIU, and Associate Editor of IJCV. He was the Program Chair for ACCV 2014 and ICCV 2019 and Senior Area Chair/Area Chair for CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, IJCAI, and AAAI. Yang is a Fellow of the IEEE and ACM.

主講人：Dr. Jonnie Penn (Harvard University)
題　目：What the History of AI Says about its Future: Designing with Non-Use in Mind
時 間：112年12月15日(五)上午10點00分至12點00分
地　點：台達館108教室
Abstract:
What the History of AI Says about its Future: Designing with Non-Use in Mind
Since the term ‘AI’ was introduced in the 1950s, it has been used to describe three entirely different schools of thought about the nature of machine intelligence. Why is this? This talk introduces the forgotten forces behind the origins of ‘AI.' These complex histories provide rich evidence with which to calibrate speculation about AI and AI Ethics in the decades ahead. I introduce one overlooked trend around non-use.
Questions for the students to think about in advance of the class:
How would knowing the intention of your user help you to design an AI system?
How could restraint (when a user chooses not to use a technology) help you design better AI systems?

Bio:
Dr Jonnie Penn, FRSA, is an Associate Teaching Professor of AI Ethics and Society at the University of Cambridge. He is a historian of technology, a #1 New York Times bestselling author, and public speaker.
Penn serves as a Faculty Affiliate at the Berkman Klein Center at Harvard University, a Research Fellow and Teaching Associate at the Department of History and Philosophy of Science, a Research Fellow at St. Edmund’s College and as an Associate Fellow at the Leverhulme Centre for the Future of Intelligence.
He was formerly a MIT Media Lab Assembly Fellow, Google Technology Policy Fellow, Fellow of the British National Academy of Writing and popular broadcaster.