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Seminar abstract

Artificial Collective Intelligence

Jun Wang
University College London, UK

Abstract: The last decade has witnessed massive progresses in the field of Artificial Intelligence (AI). With supervision from labelled data, machines have, to some extent, exceeded human-level perception on visual recognitions, while fed with feedback reward, single AI units (aka agents) defeat humans in various games including Atari video games, Go game, and card game. Yet, true human intelligence embraces social and collective wisdom and many real-world AI applications often require multiple AI agents to work in a collaborative effort. A next grand challenge is to answer how large-scale multiple AI agents could learn human-level collaborations, or competitions, from their experiences with the environment where both of their incentives and economic constraints co-exist. In this talk, I shall sample some of our recent research on what is called artificial collective intelligence, ranging from machine bidders competing against each other in an auction environment for buying advertising placements, to image/text/music generation with minimax adversarial games, to coordinating multiple AI agents as a team to defeat their enemies in StarCraft combat games. I will finally conclude the talk by pointing out the future direction on this exciting field.

Bio: 汪军,伦敦大学学院(UCL)计算机系教授、互联网科学与大数据分析专业主任,国际公认的计算广告学和智能推荐系统专家。主要研究智能信息系统,主要包括数据挖掘、计算广告学、推荐系统、机器学习、强化学习、生成模型等,已发表了100多篇学术论文。2007年,在美国获得了由微软 “超越搜索——语义计算和互联网经济学奖”。此外,他还是2014年Yahoo! FREP的获奖者之一,ACM SIGIR/CIKM的领域主席,多次获得最佳论文奖。同时也是北京优路公司的创始人,这是一家基于人工智能和数据挖掘领域技术的创业公司,主要服务于在线媒体、自媒体,金融、电商、互联网娱乐等领域。
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