Yesterday, the Government of Alberta announced a $100 million investment towards the province’s Artificial Intelligence (AI) sector. This investment will be used for training, research, fostering company growth, and encouraging foreign AI businesses to move here.
Coupled with last year’s investment of $50 million to create 3,000 new high-tech training seats at post-secondary institutions across the province, yesterday’s announcement also follows the 2017 federal commitment of $125 million towards the Pan-Canadian Artificial Intelligence Strategy, which provided funding for the Alberta Machine Intelligence Institute (Amii). [As part of yesterday’s announcement, Amii will also be getting $27 million from the provincial government to set up an office in Calgary].
In our last post, we spoke to Amii president John Shillington about AI research developments and business potential in Alberta. In this post, we want to slow things down and provide a little more background on these technologies, and why even the smallest (or least technical) companies in Alberta should be paying attention to AI.
“We have a lot of fertile ground here for an enormous amount of growth.” — John Shillington, CEO, Amii
Artificial Intelligence essentially refers to a computer that can perform tasks that normally require human intelligence (i.e. it can do a task on its own).
Other tools / methodologies that are frequently mentioned when discussing AI are Machine Learning and, to a certain extent, Data Science.
Machine Learning is exactly what it sounds like: the ability for a machine to learn something (without being explicitly programmed to do so).
Diving deeper, there are different ways that such machines are programmed to learn and process information. These can involve text, sound and image analysis, pattern recognition, game play, etc. The terms you will hear referencing these systems include Deep Learning and Neural Networks, Reinforcement Learning, Natural Language Processing, and Machine / Computer Vision. (For more details on these systems and how they work, Wondr has published a great breakdown on Medium).
Data Science is a means of gaining descriptive and prescriptive insights from data, i.e. looking at a set of information and not only determining what that data is and what it means, but also what it could mean. Take Netflix for example. The streaming service noticed that people liked watching suspenseful movies / movies with a diverse cast / movies starring Sandra Bullock. This led to the production of Bird Box, a Netflix-original film that was streamed 45 million times in its first seven days of release.
These technologies have been in the works for decades, but it wasn’t until the relatively recent proliferation of affordable yet powerful computing (either through physical hardware or via the cloud), and high-speed networking, that millions of people could start using these tools.
Every company is. If you use QuickBooks, Google, Facebook ads, Mailchimp, etc you’re already benefiting from the AI-assisted services, help bots, and recommendations that come from these tech giants.
Other large companies in Canada, such as Shopify, Linamar (a manufacturing firm), and most major banks have developed in-house AI capabilities. The most common uses of AI right now are to: 1) Improve fraud and other security threat detection, 2) Increase the efficiency of internal equipment and operations, and 3) Develop personalized recommendation tools (and help bots to respond to common problems) for customers.
There are also interesting examples of machine vision being used in agriculture and energy companies. Blue River Technology has developed a tool that can be used on farm tractors to detect weeds and harmful plants and strategically direct herbicides towards them, and fertilizers towards the plants that farmers want to grow.
Other companies are developing machines that monitor oil pipelines to detect early signs of cracks.
The answer, of course, is: it depends. The key to utilizing any new digital tool — including data science and machine learning — is to approach it with a very specific goal in mind. You can’t look to these technologies as a magical oracle that can quickly assess your entire company to determine any issues / potential you may have. They need specific (as specific as possible!) questions and goals.
Also think about what information you have: are you keeping extensive records of your operations, and is it organized and easy to go through? All digital projects start with the data, and the more data you have (and the more organized it is), the better the outcome of your projects.
You may want to work with a tech consultancy in Alberta to get your project off the ground. Some (but certainly not all) of these organizations include:
There also may be opportunities for you to develop an AI or data science team in-house, utilizing your existing talent. You can attend an upcoming Amii session to find out more about AI and how to get started. There will likely be more learning opportunities popping up in the next year, as Alberta begins its big push to drive more companies (and Albertans) to dive into this game-changing technology!