All posts by Administrator

AI Lecture Series III

Mastering AlphaGo Zero

This event was organized on Nov 18, 2017.

Al lecture Series II

Intelligent transportation, smart cities and autonomous driving

By Yifei Wang
Oct 29, 2017

The “intelligent transportation, smart cities and autonomous driving” lecture was successfully held at the University of Alberta at NRE 2-001 on Oct 21, 2017.

About 80 participants attended this event. Zhijun Qiu (University of Alberta tenure professor), Lihang Ying (AlbertaAI President), Lixin Liu (TusStar Alberta COO) and Yuxi Li (reinforcement learning experts) gave the fabulous talks and answer the questions from the audiences. AlbertaAI VP-External Yifei Wang hosted this event. University of Alberta Chinese Graduate Students’ Club (UA – CGSC), Edmonton IT Club (EITC) and Tsinghua Alumni Association of Edmonton (TAAE) are the co-organizer of this event.

Prof. Zhijun Qiu shared his insight of the status quo and progress of artificial intelligence, intelligent car and transportation. He also touched on the commercialization and Alibaba’s “City brain” project during the speech.
Prof. Qiu pointed out that artificial intelligence (computer technology) and the Internet of things (sensor technology, communication technology and microelectronic technology), are similar to the relationship between the brain and body. Currently, the vast majority of commercialized applications are “Pseudo artificial intelligence”, because the quantity and quality of data is not enough, the ratio of IT giants and startups focusing on AI is too low. The biggest advantage of artificial intelligence is “iteration”, the core value is derived from the data iteration, the algorithmic iteration, the iteration of application.

Then Dr. Ying gave the talk on “autonomous driving, implementation and open sources resources”, including Level 0 to 5 automatic grading system and the basic software and hardware of realizing autonomous driving. Dr Ying illustrated the Tesla Autopilot, which is based on radar, ultrasonic, camera sensor hardware, and the method and code implementation of using all kinds of data from sensor trained with deep learning model. He particularly demonstrated open source project OpenPilot, which has been implemented in cars from Acura, Civic. In the end, he touched on the StreetDrone (a self-driving car that develop the algorithm automatically) and the online self-driving certificate(can be obtained through the course in Udacity).

Mr. Liu, the Chief Operating Officer of TusStar Alberta, introduced the profile of TusStar. Liu described the business layout and achievement in expanding incubation network in China and the globe of the Tusholdings Co. Ltd. (a large integrated enterprise established in reliance on Tsinghua University) and its subsidiary corporation TusStar (an advanced Incubation Service Institution). The vision of TusStar Alberta which was established recently in May, 2017 is becoming a professional incubator focus on Alberta’s startup and investment.

Finally, Dr. Yuxi Li joined the panel discussion with the other three guest speakers. All the questions asked by enthusiastic audiences were answered. Noticeably, two of the audiences came all the way from Calgary after about 400 kilometers of driving only for this event!

AI Lecture Series I

Post-alphaGo Era of Artificial Intellegence:
Brief Introduction and Application of Deep Reinforcement Learning

Date: July 22, 2017

In this lecture, AI expert Dr. Yuxi Li introduced and demonstrated the technology behind AlphaGo — deep reinforcement learning from three aspects:
1)    Background of Artificial intelligence, machine learning, Deep learning
2)    Key concepts and algorithms of reinforcement learning 
3)    The application of deep reinforcement learning, including game, robot, natural language processing, computer vision, neural network, structural design, business management, finance, healthcare, Industry 4.0, smart power grid, intelligent transportation, computer system etc.

Bio of Guest speaker
Dr. Yuxi Li systematically summarized six core element, six key technologies and twelve fields of application of deep reinforcement learning in his publication “Deep Reinforcement Learning: An Overview” in Arxiv.