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  叶荫宇

  斯坦福大学教授、冯·诺依曼理论奖得主

  Optimization and Operations Research in Mitigation of a Pandemic

  嘉宾先容

  演讲摘要

  We present several Optimization, Statistics and Operations Research models and methods in mitigation the ongoing Covid-19 pandemic. In particular, we describe in details of following topics:
          ● Inventory and Risk Pooling of Medical Equipment/Resources in a Pandemic 
          ● New Norm: Operation/Optimization helps to maintain Social Distancing
          ● Indoor GPS and Tracking by Sensor Network Localization for Contact-Tracing
          ● Dynamic and Equitable Region Partitioning for Hospital/Health-Care Services
          ● Efficient Public Good Allocating under Tight Capacity Restriction via Market Equilibrium Mechanisms/Platforms

• 

  Oussama Khatib

  斯坦福大学教授、斯坦福；；；；；等耸笛槭抑魅巍EEE Fellow

  The Era of Human-Robot Collaboration

  嘉宾先容

  演讲摘要

  Robotics is undergoing a major transformation in scope and dimension with accelerating impact on the economy, production, and culture of our global society. The generations of robots now being developed will increasingly touch people and their lives. They will explore, work, and interact with humans in their homes, workplaces, in new production systems, and in challenging field domains. The emerging robots will provide increased support in mining, underwater, hostile environments, as well as in domestic, health, industry, and service applications. Combining the experience and cognitive abilities of the human with the strength, dependability, reach, and endurance of robots will fuel a wide range of new robotic applications. The discussion focuses on design concepts, control architectures, task primitives and strategies that bring human modeling and skill understanding to the development of this new generation of collaborative robots.

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  Benjamin Van Roy

  斯坦福大学教授、IEEE Fellow

  Hypermodels for Exploration

  嘉宾先容

  演讲摘要

  We study the use of hypermodels to represent epistemic uncertainty and guide exploration. This generalizes and extends the use of ensembles to approximate Thompson sampling. The computational cost of training an ensemble grows with its size, and as such, prior work has typically been limited to ensembles with tens of elements. We show that alternative hypermodels can enjoy dramatic efficiency gains, enabling behavior that would otherwise require hundreds or thousands of elements, and even succeed in situations where ensemble methods fail to learn regardless of size. This allows more accurate approximation of Thompson sampling as well as use of more sophisticated exploration schemes. In particular, we consider an approximate form of information-directed sampling and demonstrate performance gains relative to Thompson sampling. As alternatives to ensembles, we consider linear and neural network hypermodels, also known as hypernetworks. We prove that, with neural network base models, a linear hypermodel can represent essentially any distribution over functions, and as such, hypernetworks are no more expressive.

• 

  陈小平

  中国科学手艺大学教授、机械人研究中心主任

  人工智能希望与挑战：真相解读

  嘉宾先容

  演讲摘要

          1950年图灵测试提出后，，，，，，人工智能一直生长，，，，，，取得了重大希望。。。。。。图灵测试背后的科学假说我称之为“图灵智能假说”——在人机交互规模内，，，，，，智能可以还原为盘算。。。。。。阿法狗是证实图灵智能假说的一个乐成实例，，，，，，批注AI不必围棋规则以外的人类知识就能远超人类的围棋能力。。。。。？？？？？？捎诺鹿偻饰龇⒚鳎，，，，，阿法狗包括的AI手艺仅在关闭性场景中才华抵达云云效果，，，，，，而现实天下的大部分场景都不是关闭的。。。。。。讲座将诠释什么是关闭性以及与之相关的科技挑战和重大机缘。。。。。。

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  宋乐

  佐治亚理工学院副教授，，，，，，机械学习中心副主任

  Deep Learning for Algorithm Design

  嘉宾先容

  演讲摘要

  Algorithms are step-by-step instructions designed by human experts to solve a problem. Effective algorithms play central roles in modern computing, and have impacted many industrial applications, such as recommendation and advertisement in internet, resource allocation in cloud computing, robot and route planning, disease understanding and drug design.  

  However, designing effective algorithms is a time-consuming and difficult task. It often requires lots of intuition and expertise to tailor algorithmic choices in particular applications. Furthermore, when complex application data are involved, it becomes even more challenging for human experts to reason about algorithm behavior.  

  Can we use deep learning and AI to help algorithm design? There have been a number of recent advancements that have allowed algorithms to designed from specific algorithmic families automatically using data, often leading to either state-of-the-art empirical performance or provable performance guarantees on observed instance distributions. In this talk, I will provide an introduction to this area, and explain a few pieces of work along this direction.

• 

  沈向洋

  美国国家工程院外籍院士、英国皇家工程院外籍院士、前微软公司执行副总裁

  From Deep Learning to Deep Understanding

  嘉宾先容

  演讲摘要

    

• 

  蒙美玲

  香港中文大学讲座教授、IEEE Fellow

  Communication with Speech and Language – A Hallmark of Artificial Intelligence

  嘉宾先容

  演讲摘要

  The ability to communicate in speech and language has long been regarded as a hallmark of human intelligence.  Recent technological advancements have made great strides in enabling machines to simulate the human ability to communicate verbally and create a hallmark of Artificial Intelligence (AI).  This talk presents an overview of ongoing research at CUHK that enables AI to not only speak and listen, but also to enhance learning of a new language, to serve users with communicative impairments, as well as to combat dementia.

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  熊友军

  优必选首席手艺官

  仿人机械人的运动控制研究

  嘉宾先容

  演讲摘要

          先容仿人机械人生长历程、研究目的、应用场景，，，，，，重点探讨仿人机械人运动控制研究研究现状和保存的挑战问题。。。。。。

• 

  张立

  香港中文大学副教授

  医用微纳机械人：梦想、现实和挑战

  嘉宾先容

  演讲摘要

  People have envisioned tiny machines and robots that can explore the human body, find and treat diseases since Richard Feynman’s famous speech, “There's plenty of room at the bottom,” in which the idea of a “swallowable surgeon” was proposed in the 1950s. Even though we are at a state of infancy to achieve this vision, recent intense progress on nanotechnology, MEMS/NEMS technology and micro-/nanorobotics has accelerated the pace toward the goal. A number of research efforts have been recently published regarding the development of tiny swimming machines/robots from the basic principles and fabrication methods to practical applications. 

  I will present the past and recent research progress on medical micro-/nanorobots. The challenges and opportunities of using these tiny agents for biomedical applications will be discussed. 

• 

  周明

  中国盘算机学会副理事长、微软亚洲研究院副院长、国际盘算语言学会前任会长

  预训练模子在多语言、多模态使命的应用

  嘉宾先容

  演讲摘要

          最近几年神经网络自然语言处置惩罚取得了很大的希望，，，，，，其中预训练模子是最近引起普遍关注的立异手艺。。。。。。使用险些无限的文本数据，，，，，，可以自监视的方法训练一个大型的语言模子，，，，，，实现对文本的词汇的上下文相关的语义体现。。。。。。在学习一个特定使命时，，，，，，基于预训练模子举行细调获得了很大的性能提升。。。。。。预训练模子进一步延伸到多语言、多模态的使命中，，，，，，也取得了令人鼓舞的前进。。。。。。

          本讲座先容多语言、多模态预训练模子手艺，，，，，，探讨自然语言处置惩罚现在新的时机。。。。。。我们也将先容我们最近的研究效果包括支持语言明确和语言天生的统一的预训练模子（UniLM）和支持跨语言使命的预训练模子（Unicoder）。。。。。。

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  李世鹏

  优德官网执行院长、国际欧亚科学院院士，，，，，，IEEE Fellow

  Aggregating Intelligence with the Internet of Intelligent Things (IoIT)

  嘉宾先容

  演讲摘要

    

• 

  黄建伟

  香港中文大学(深圳)校长讲座教授、理工学院副院长，，，，，，优德官网副院长、IEEE Fellow

  Incentive Mechanism Design for Crowd Systems

  嘉宾先容

  演讲摘要

  Crowd systems can help solve complicated problems through the collective efforts of many non-expert agents. A key to success is to incentivize enough agents to participate and exert efforts. We will introduce the challenges and opportunities of incentive mechanism designs in diverse types of crowd systems.

• 

  杨强

  IJCAI国际人工智能大会理事会主席、香港科技大学讲席教授、微众银行首席人工智能官

  人工智能和智慧金融

  嘉宾先容

  演讲摘要

  我们将先容人工智能和金融行业深度团结的新理念和落地实践。。。。。。详细先容怎样系统解决小数据和用户隐私带来的挑战。。。。。。针对金融应用领域中标注数据的严重缺乏，，，，，，导致许多优异算法模子无法获得有用训练的问题，，，，，，微众银行AI团队创立性地提出了，，，，，，使用联邦学习的手艺框架来毗连数据孤岛的数据，，，，，，以获得可以保；；；；；ひ私的的机械学习模子训练和应用，，，，，，以及使用迁徙学习来解决小数据的问题，，，，，，解决行业应用的痛点。。。。。。演讲将详细形貌微众AI团队，，，，，，针对这些问题在算法研究方面做出的奇异孝顺,  以及在此基础上打造的开源，，，，，，共生，，，，，，合规的行业生态系统和一系列现实应用。。。。。。

• 

  张潼

  香港科技大学教授、IEEE Fellow

  神经网络理论研究希望

  嘉宾先容

  演讲摘要

          深度神经网络虽然已经成为人工智能的基础模子，，，，，，但一直以来缺乏理论基础。。。。。。我简朴先容一下关于神经网络理论研究的近期希望，，，，，，包括非凸优化重大性问题和过参数化理论。。。。。。

• 

  邢国良

  香港中文大学教授、IEEE fellow

  面向下一代物联网的边沿AI系统

  嘉宾先容

  演讲摘要

          物联网（IoT）通详尽麋集成传感、通讯和盘算来与物理天下举行交互。。。。。。下一代物联网应用是数据麋集型和使命要害型的，，，，，，会天生大宗必需在严酷的时延限制内举行处置惩罚的数据。。。。。。据预计，，，，，，自动驾驶汽车每秒可爆发0.75 GB的数据。。。。。。由于不可展望的高延迟以及对数据的隐私保；；；；；と狈Γ，，，，，现有的云盘算模式应用下一代物联网时面临一系列问题。。。。。。

          我将先容我们最近在Edge AI方面的研究。。。。。。通过智能地漫衍和调理从云到物联网端的盘算，，，，，，存储，，，，，，控制和网络资源，，，，，，边沿智能盘算手艺可以应对下一代物联网的挑战。。。。。。首先，，，，，，我将先容优德官网基于实时边沿中心件(real-time Edge middleware)的智能路边设施RSI (smart roadside infrastructure)系统，，，，，，通过对边沿系统举行编程并在网络层之间划分盘算使命，，，，，，优德官网实时边沿中心件可以在知足应用程序时延要求的同时最洪流平地降低系统功耗。。。。。。在此框架上我们举行了智能多传感器融合和实时多深度学习使命调理等事情。。。。。。最后我将简要先容我们在移动康健、联邦学习、火山地动监测、NB-IoT等偏向的事情。。。。。。我们研发的系统已经举行了大规模的现场安排，，，，，，包括在厄瓜多尔和智利的两个活火山上装置的地动传感器网络。。。。。。

• 

  曹建农

  香港理工大学电子盘算学系讲座教授、IEEE Fellow、ACM Distinguished Member

  Distributed Intelligence at the Edge

  嘉宾先容

  演讲摘要

  The emerging IoT applications in connected healthcare, industrial internet, multi-robot systems, and other areas demand higher intelligence of the connected devices, larger scale of the systems, and better decision making leveraged by analyzing the data being continuously generated. In this context, centralized cloud computing would face high data transmission cost, high response time, and data privacy issues. The edge cloud paradigm seeks to alleviate these inefficiencies by moving the computation and analytics tasks closer to the end devices. It facilitates the evolution of IoT from instrumentation and interconnection to distributed intelligence. This talk focuses on collaborative edge computing where edge nodes share data and computation resources and perform tasks by leveraging distributed intelligence. It covers the major problems in distributed collaboration we are currently studying, namely collaborative task execution, distributed machine learning, and distributed cooperation in autonomous multi-robot systems. Solutions need to address the challenging issues such as distributed data sources, conflicting network flows, heterogeneous devices, consistency, and mutual influence during the training.

• 

  张大鹏

  香港中文大学（深圳）校长讲席教授、优德官网盘算机视觉研究中心主任、IEEE Fellow

  Medical Biometrics- A Computerized TCM Data Analysis Approach

  嘉宾先容

  演讲摘要

  Traditional Chinese Medicine (TCM) diagnosis methods are mainly relied on Doctor's experience and not quantified. In this presentation, we will try to develop a novel approach by using Medical Biometrics technology to solve these problems. By some TCM-orient diagnosis acquisition devices, we could collect many kinds of date like tongue/pulse/odor with a priori knowledge from healthy/sub-healthy in Body Checking Station or from different diseases in Hospitals. Then, we use a statistical pattern recognition method to extract all possible features from these images/waveforms, including color, texture, shape, and so on. After matching between our training data and testing data, some decision rules will be made. Finally, we apply our results to the practical diseases diagnosis to illustrate the effectiveness of our approach.

• 

  欧国威

  香港中文大学副教授、医疗机械人立异手艺中心主任

  Embracing Mechanical Intelligence for Agile Locomotion

  嘉宾先容

  演讲摘要

  Understanding the locomotion principle behind animals is crucial in developing next generation of agile robotic platform. Over the past decades, a wide range of bio-inspired legged robots have been developed that can run, jump, and climb over a variety of challenging surfaces.  However, in terms of maneuverability they still lag far behind animals.  Animals have instinct to use their mechanical body and external appendages (such as tails) effectively to achieve spectacular maneuverability, energy efficient locomotion, and robust stabilization to large perturbations which may not be easily attained in the existing legged robots. 
  In this talk, we will present our efforts on the development of innovative legged robots with greater mobility/efficiency/robustness, comparable to its biological counterpart.  We will discuss the fundamental challenges for legged robots and show our initial results to demonstrate the feasibility of developing such systems through the use of external appendages and advanced intelligent algorithms.  We believe our solutions could potentially lead to more efficient legged robot design and give the legged robot greater mobility and robustness for moving through complex real-world environments, comparable to its biological counterpart. 

“全球人工智能与机械人前沿钻研会2020”乐成举行

FAIR2020 | 叶荫宇：Optimization and Operations Research in Mitigation of a Pandemic

FAIR2020 | 周明：预训练模子在多语言、多模态使命的应用

FAIR2020 | 黄建伟：Incentive Mechanism Design for Crowd Systems

FAIR2020 | 张大鹏：Medical Biometrics- A Computerized TCM Data Analysis Approach

FAIR2020 | 陈小平：人工智能希望与挑战：真相解读

FAIR2020 | 沈向洋：From Deep Learning to Deep Understanding

FAIR2020 | 欧国威：Embracing Mechanical Intelligence for Agile Locomotion

FAIR2020 | 宋乐：Deep Learning for Algorithm Design

FAIR2020 | 李世鹏：Aggregating Intelligence with the Internet of Intelligent Things

FAIR2020 | 熊友军：仿人机械人的运动控制研究

FAIR2020 | 张立：医用微纳机械人：梦想、现实和挑战

FAIR2020 | 杨强：人工智能与智慧金融

FAIR2020 | 邢国良：面向下一代物联网的边沿AI系统

FAIR2020 | 蒙美玲：Communication with Speech and Language – A Hallmark of Artificial Intelligence