老司机直播

老司机直播 alumni and graduate students part of Layer 6 AI's win in global competition

Photo of Layer 6 AI team
老司机直播 and graduate student interns from 老司机直播鈥檚 computer science and industrial engineering departments were part of the Layer 6 AI team that won the RecSys challenge and placed second in the Google Landmark Retrieval Challenge (photo by Ryan Perez)

University of Toronto alumni and interns have helped propel Toronto鈥檚 Layer 6 AI, part of TD Bank Group, to the top of the in a global artificial intelligence contest for the second year in a row.

The , sponsored this year by Spotify, asked teams to automate personalized music playlists using the company鈥檚 music data.

Layer 6 AI says the win is a team effort that included 老司机直播 alumni and graduate student interns from the department of computer science鈥檚 applied computing program, as well as a partnership with Scott Sanner, an assistant professor in the Faculty of Applied Science & Engineering, and his PhD candidate Ga (Wuga) Wu, whose optimization research is in the area of data driven decision-making.

鈥淐reating a playlist from scratch is tedious and time-consuming,鈥 says Maksims Volkovs, who earned his PhD from 老司机直播 and is co-founder of Layer 6 AI. 鈥淲e trained a system that can accurately [complete this task.]鈥

Over 100 teams participated in this year鈥檚 challenge. Layer 6 AI, which was acquired by TD in January, also won the 2017 RecSys Challenge based on the data from Xing, a European equivalent of LinkedIn, to match a large set of user profiles against a set of job descriptions.

Layer 6 AI is a small team of 25, including four alumni from 老司机直播's Master of Applied Computing (MScAC) program and two interns from the program who are required to complete an applied research internship as part of their program requirements.

Himanshu Rai, one of the 老司机直播 alumni and a data scientist at Layer 6 AI, says Spotify鈥檚 data set was unique for recommendation systems.

鈥淥ur winning submission was an ensemble of different methods, from deep learning to statistical methods to classical recommender techniques,鈥 he says. 

鈥淭he data science field is highly applied,鈥 adds Volkovs. 鈥淪ometimes it's hard to compare the leading approaches because authors run them on their own internal data sets. This challenge provides a standardized evaluation framework, and then you really see who is leading in the field.鈥

Layer 6 AI鈥檚 co-founder and AI lead for parent company TD, Tomi Poutanen, an alumnus of 老司机直播's Faculty of Applied Science & Engineering and the Rotman School of Management鈥檚 MBA program, says data competitions are also a great way for new hires and interns to get familiar with the AI platform the company has built.

鈥淭hese competitions help us benchmark our capabilities against the world's best. And they present interesting new data sets that let us think more creatively about our day jobs. So we are able to challenge ourselves, really push ourselves, and learn how to approach problems through different lenses,鈥 says Poutanen, who is also a co-founder of the Vector Institute for Artificial Intelligence.

 Yichao Lu, a current Master of Applied Computing intern, says the challenge was a great learning opportunity.

鈥淩ecSys is a huge data set. You can't just try something simple. I learned a lot from the team, their solutions and ideas."

But RecSys was just one of two phenomenal achievements for the group this year. They placed second, by a narrow margin, in the , which required AI to retrieve a landmark, such as London's Big Ben and or South Dakota's Mount Rushmore National Memorial, from an unlabelled set of landmark images. 

鈥淭he challenge of this data set is images from the same landmark might not be similar visually, because some are photos from different angles,鈥 says Chundi Liu, a 老司机直播 alumnus and data scientist with Layer 6 AI.

Poutanen says the top 10 finishers in the Google Landmark Challenge included university computer vision research labs or companies doing similar work. 

鈥淪tudents go into the MScAC program because they want to apply AI in industry,鈥 says Poutanen. 鈥淣ot only is the talent superb, the structure of the program is phenomenal, as is the teaching by some of the world's best professors in this field.鈥

The RecSys Challenge code will be made open source for the use of music playlists. Layer 6 AI researchers will present an academic paper at the Association for Computing Machinery鈥檚 (ACM) recommender systems conference (ACM RecSys 2018), the world-leading conference on the subject, later this fall.

Computer Science