Amii’s monthly meetup brings together the brightest minds in Edmonton’s AI community.
Discuss the latest topics in AI and machine learning, learn about the latest tools and techniques in machine learning, discover how companies are using AI to drive value, and network with thought leaders from Amii, local AI companies, service providers, and corporate labs.
Presenters for this month include:
Jason Morris – LLM student at UAlberta's Faculty of Lawand CS Dept./Innovation Fellowship recipient from the American Bar Association Center for Innovation
Jason Morris is an interdisciplinary Master of Laws Amii student at the UAlberta's Faculty of Law and Department of Computing Science. His research dives into the potential of declarative logic programming, one type of rules-based AI, as a tool for enhancing access to justice.
He is a former programmer, database analyst, and founder of Round Table Law – a virtual, cloud-based law firm which specializes in computational law, civil litigation, wills and the laws surrounding mental health.
As a recipient of the prestigious Innovation Fellowship, he developed an extension to an open source legal expert system tool called Docassemble, and he is currently developing an open source visual programming environment for declarative logic programming called Blawx.
Graham Kawulka – President at Maapera
Big data and machine learning are transforming a number of industries, and the environmental sector is no exception. Improvements in data processing and sensor technology have enabled monitoring and high-resolution data acquisition options previously thought to be unachievable for the environmental soil assessment sector. Specifically, machine learning and new sensor technology can be used to support land stewardship and by reducing costs for remediation activities and ensuring a sound basis of data from which to make decisions.
Obtaining analytical data during remediation activities represents a significant cost for most projects, and high-resolution site characterization is, typically, cost prohibitive without innovative quantitative field screening technology. A technological solution to this problem is the use of short-wave infrared (SWIR) reflectance spectroscopy combined with machine learning to identify distinct spectral signatures for petroleum hydrocarbons (PHCs) and clay content in soil. As a result, a detailed three-dimensional site model can be generated rapidly; this supports field decision making during assessment, reduces the risk of missing key features when building a site conceptual model and reduces the risk of residual contamination left on site.
It is now possible to go from a handful of data points for a site, to a cost-effective three-dimensional model with vertical resolutions as low as 10 cm. The foundation of handling any problem is data/ information. Land stewardship can only benefit from improvements in data quantity, quality, and speed. Examples of these benefits have now been seen in the work Maapera Analytics has done to date.
Join us for Amii’s monthly AI Meetup and be a part of building Edmonton’s growing AI community!