In a span of just 30 days, the AI Strategy Task Force sought public input - which I happily participated in.
I gave each of the 26 questions serious thought and published all my answers in a series of posts.
Given what was asked -and just as importantly - what wasn’t, I have three of my own questions for the task force. Here is the first.
- What is AI?
There is a running joke that AI is what we call new algorithms that don’t yet work reliably - or that hasn’t found use cases beyond demos. Old but established algorithms are not exciting, despite being powerful and useful tools. In the 2010s, image classification using deep learning experienced tremendous progress. Exciting and bold predictions were made about how it would replace radiologists and truck drivers. This has not happened, but these algorithms have found widespread adoption in innumerable use cases. The excitement has waned despite such algorithms being an integral part of any modem computer vision toolkit.
Machine learning was AI in the 2010s after its AlexNet moment, where it matched humans in a broad image recognition benchmark. But these methods are not part of the current AI conversation - a conversation purely focused on generative AI methods. Will Canada’s new AI strategy take into account machine learning and other older methods that are seeing less progress, less attention but can still have tremendous impact? What about even older and simpler methods, such as support vector machines? Looking forward, how will the strategy consider today’s’ bleeding edge methods once they become routine tools and new, unproven methods are in the spotlight instead?