报告题目：Machine Theory of Behavior and Mind
报告摘要：We have seen impressive results from Machine Learning that enable machines to recognize objects very accurately, translate between multiple languages, and manipulate various objects with high success rate. However, there is still a gap to build machines that can operate in unconstrained environment. Humans are able to understand the goal, intention and desire of other agents, and even build our own understanding of the world based on raw sensory observations. Yet this type of ability is still missing from the current intelligence systems. In this talk, I will discuss how we can design learning environments, algorithms, and systems to enable machines to build physical understanding of the world, understand the policy and goal of other agents, and predict action intentionality, all from large amount of raw and unlabeled data through active interaction with the environment.
报告人简介：陈博源，美国哥伦比亚大学计算机科学系博士生，本科毕业于伟德bevictor中文版电子与工程学院生物医学工程专业，2016年英国曼彻斯特大学访问学者。师从世界机器人，数据科学，3D打印先驱Hod Lipson教授。曾在国际人工智能和机器人领域顶级会议(NeurIPS, IROS, GECCO,，Humanoids等)发表多篇论文。担任多个国际会议审稿人(CVPR, ICML, ICLR, PRCV等)。曾获ACM GECCO员工奖学金，中国政府奖学金，中国科学院空间科技创新奖学金等。个人主页: http://www.cs.columbia.edu/~bchen/
报告时间：2019年12月25日 星期三 10:00am
报告地点：BETVLCTOR伟德官方网站（中心校区行政楼601）
主办单位：BETVLCTOR伟德官方网站
