The Faculty of Applied Science & Engineering’s new Artificial Intelligence (AI) minor and certificate programs will be available for enrolment by students in the Core-8 and Engineering Science programs in January.

Students are required to fulfil three full course equivalents (FCE) to complete the minor, while students enrolled in the certificate program must complete 1.5 FCEs. Since a few of the courses required for the program fall out of the scope of students’ main discipline, some students may need to take extra courses to complete the requirements.

Students who complete the minor or certificate will receive a notation on their transcript.

Professor Jason Anderson from the Department of Electrical & Computer Engineering, a key figure behind the program, explains that all students will be required to take one foundational course, as well as courses in data structures and algorithms relevant to AI and machine learning.

Students enrolled in the certificate program can choose between traditional AI or machine learning for specialization. Students in the minor will learn about both and choose an additional area of interest to specialize in, such as computer vision or natural language processing.

Anderson explains that machine learning is one aspect of AI. In traditional AI, computers can make decisions on their own. In machine learning, computers use and learn from data to make decisions.

“The computer is actually trained to recognize images in different categories. In traditional AI, that’s more encoded in rules.”

“Students who take the certificate or minor will have hands on experience applying AI and machine learning techniques to real engineering problems,” says Anderson. In addition, students will be exposed to the ethical questions surrounding AI technology.

While there is currently no specific Professional Experience Year Co-op (PEY) opportunity for the AI minor and certificate, Anderson says that many students are already working with AI to some extent during their PEY.

Anderson also notes that AI ties in with other engineering disciplines in several ways. For instance, AI technologies can be used by civil engineers to understand traffic patterns or by chemical engineers in drug discovery.

In his own field, Anderson notes that AI technology is being used in computer-aided design tools that “create complicated digital circuits” in order “to produce higher quality designs, for example, that use less silicon area, that use less power, operate faster, to make predictions.”

“We want students who can research in this area but also have applied AI techniques,” says Anderson. Through this program, he hopes to foster engineering talent that will lead students to create startups, develop new AI technology, or further their education through graduate studies.

A U of T team took home a silver medal at this year’s International Genetically Engineered Machine (iGEM) Giant Jamboree competition for their project on an “environmentally friendly and economically feasible” way to clean wastewater using genetically engineered bacteria.

The competition, held on October 24–28 in Boston, Massachusetts, brought together more than 6,000 students and professionals of all levels and countries to showcase achievements in synthetic biology.

In trying to find an alternative wastewater cleaning solution, the U of T team used biomass flotation for bioremediation processes.

“We hypothesized that one could genetically engineer E. coli to bind to waste particles, and then float to the surface of the reactor, allowing for easy E. coli removal,” wrote the team in their drylab model.

“We have engineered bacteria to float by producing gas vesicles, and now we are trying to get these bacteria to bind pollutants so that they can float up with them and separate them from wastewater,” wrote Vice-President Internal Jack Castelli to The Varsity. The team has also mathematically modelled a bioreactor platform that makes use of their system.

According to President Amy Yeung, the group came up with the idea by talking to graduate students at their lab and reading scientific literature, and then searching for novel tools to find solutions to the problems they encountered.

“Coming across the use of gas vesicle in ultrasound imaging we also thought that it could be expressed in bacteria and used as a novel wastewater cleaning solution. So naturally, we decided to combine the two,” wrote Yeung.

When asked about any challenges they faced during their project, Lab Manager Tashi Rastogi said that the group had problems troubleshooting their genetically engineered constructs in the lab.

“With time we realised the importance of exercising reflexivity in our scientific process,” wrote Rastogi.

She also credited advice from graduate mentors in helping the group understand “both the biological systems [they] worked to engineer and the needs of the industrial systems [they] were designing [their] solution for.”

“The overarching goal of our project is to construct an efficient and inexpensive bio-remediation system, capable of separating pollutants from wastewater using bacteria,” wrote Castelli, who added that current processes are very costly and not environmentally friendly.

“If you have a substrate of interest that cells can bind or uptake, say heavy metals, and that’s present in a liquid medium, say mining effluent, then you could adapt the system to bind to or uptake the substrate and remove it from the medium,” he continued.

“This same logic can then be applied to microplastics, hormones, pharmaceuticals and other molecules of interest in other industries.”

The team included over 30 U of T students from across the Faculty of Arts & Science and the Faculty of Applied Science & Engineering. This is the third medal that U of T has won since the club was founded in 2007.

The competition is run by the iGEM Foundation, which has its roots in an independent study course at the Massachusetts Institute of Technology. Since its founding in 2003, it has grown into the non-profit organization that runs this international competition.