AI & Natural Resources
The natural resource sector is witnessing a major transformation from being data-poor to data-rich, creating a new mining research frontier. The mining sector has traditionally been risk-averse and slow to adopt new technologies. The challenges faced from the scarcity of high-grade mineral deposits, declining productivity and tightened regulatory conditions have created an impetus for change and innovation in this sector. Embedding data analytics, decision making support and autonomous operations into mining operations creates an opportunity for a transformation across the mining value chain. Artificial intelligence and its diverse sub-fields are swiftly integrating into all phases of mine lifecycle, from mineral exploration and discovery, to mine development and production and finally mine reclamation.
AI & Visual Computing
AI and machine learning are providing a timely boost to the fields of computer vision and computer graphics, enabling computers to massively improve their visual understanding of the world, and to become vastly better at creating new virtual worlds and their depictions. Applications within the field include traditional digital media such as VFX and games, as well as computational design & fabrication, VR/AR/MR, visual understanding and reasoning, multi-modal learning, video analytics, controlled image/video generation, training simulators, context-aware devices and robots, sports analytics, and much more.
Visual computing is of significant importance to our local BC digital media eco-system which is experiencing dramatic growth and currently includes over 600 digital media (VFX, games, VR, etc...) companies with $2.3 billion in annual revenues.
At UBC we have developed specific strengths around:
- End-to-end architecture design and training of deep neural networks for variety of visual tasks. Including in large-scale supervised, semi-supervised and weakly-supervised settings, where only limited amount of labeled training data is available.
- Building AI agents capable of leveraging, modeling and learning from multi-modal (visual, lingual, audio, geometric & photometric) data.
- Leveraging AI in support of the design of shapes, motions and imagery, where the algorithms need to integrate a comprehensive mix of available data, human input to the creative process, and functional design criteria.
AI & Manufacturing
Advanced Manufacturing is a strategic sector of the Canadian economy, representing 11% of GDP (larger than the oil & gas sector, the mining sector and the forestry sector) employing some 1.7m Canadians. 1,300 Canadian companies are integrated into a hugely competitive international supply chain for automotive and aerospace production. UBC is Canada’s leading centre of research excellence in advanced manufacturing with deep expertise in metals and composites, virtual machining and clean energy manufacturing. To maintain our national competitive advantage, it is absolutely essential that we navigate the digital transformation of manufacturing: the ubiquitous capture of data to plan, monitor and control factory operations, the use of new predictive models of materials, processes and products, the development of intelligent automation systems.
AI & Optimization
UBC has one of the top optimization groups in the world. We have some of the top people in the world working on optimization for machine learning, focusing on speeding up (and reducing the power consumption) of the machine learning models that are changing our daily life. We also have specialization in solving industrial-strength combinatorial optimization problems. UBC researchers have won prominent awards in both areas.
AI & Economics
AI and Economics have many commonalities, notably sharing a focus on utilitarian models of rational agency and relying upon the statistical analysis of data. Much work at the intersection between the two disciplines focuses on cases where rational agents interact, asking questions about how such agents reason about each other, what behaviors will emerge in systems of such agents, and how such systems can be structured by a designer to elicit socially beneficial outcomes. Another key point of focus goes beyond such rational agent models, asking what biases agents exhibit, investigating the way economic mechanisms should adapt to such biased agents, and learning about these biases from data. A third topic of interest performs statistical analysis of data arising from economic systems; e.g., inferring agents' interests from their behaviors or distinguishing correlation from causation in observational data. In all three of these lines of work, computational tools are used to address traditionally economic problems. A fourth line of work reverses this dynamic, applying economic ideas to the design of computer systems such as peer-to-peer file-sharing systems, cryptocurrencies, crowdsourcing platforms, prediction markets, or peer-grading systems.
AI & Natural Language Processing
The CAIDA NLP group focuses on developing methods to teach machines to understand and generate human language, and applications for improved health, safer social networks, more social personal assistants, and more engaging educational agents. Work from the sub-group includes text summarization, machine translation, affective computing, and representation learning.
AI & Inference
AI is about decision making, and an important component is inferring probabilities, which relies on probabilistic inference. CAIDA researchers work on exact and approximate probabilistic inference, particularly advanced Monte Carlo methods and variational inference for graphical models, lifted inference for relational models. These methods are applied to domains of biology, medicine, and geology.