These are my final thoughts on the Canada AI Survey, in the form of questions for the task force.
This is the final post in “Responses to the Canada AI Survey” - a series containing my responses to the eight themes of the Canadian government’s public consultation on artificial intelligence.
After working my way through the survey, reading what many others wrote and attending some roundtable discussions, here are some critical questions that I believe the task force will need to develop answers for.
What is AI?
There is a running joke that AI is what we call new algorithms that don’t yet work reliably - or that haven’t found use cases beyond demos. Such algorithms are not exciting despite being powerful and useful tools. In the 2010s, image classification using deep learning (a branch of machine learning which itself is a type of artificial intelligence) 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 modern 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 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?
What happens when the AI hype fizzles?
Nearly everyone admits that the current level of interest and investment in AI will not be sustained indefinitely. How will the public and policy conversations change once AI falls out of the news cycle? Or worse yet, once some high profile companies become financial victims of this hype cycle. Let’s imagine that we were tasked with setting internet policy in 2003, after the dotcom crash and the public launch of MySpace - but before Facebook and the advent of mobile Web. How will we handle this window of opportunity with LLMs?
If AI lives on the global marketplace of the internet, how could we ever control it?
The internet is a global marketplace, in particular because a great deal of its currency in the consumer space is based on attention and advertising instead of money. AI in the consumer space, but also in the cloud-enabled enterprise space is also distributed on this global marketplace. What are the realistic expectations in shaping AI on the web if it can freely enter and exit Canadian borders? Does our ideal of a free and open internet come under pressure?
How do we shape the use of AI if incentives drive behaviour and outcomes?
Many of our greatest technological disasters, including those which are still ongoing, are unsurprising as consequences of existing incentives. Is there any goodwill to change such incentives given AI’s potential? Many are drawing attention to what this means in the extreme case - our capitalistic society may destabilized by rapid adoption of AI due to displacement of labour and other effects. Which incentives at the individual, corporate, political and societal level can be subject to modification?
Where does technology not considered AI fit in?
More than 90% of doctors in Ontario still use fax machines. It’s painful to spell it out, but the technology to replace the fax machine has existed for decades now - and none of it required AI. Will Canada’s AI Strategy ignore opportunities to make advances in areas that also need updated policy and incentives but that don’t require actual AI?
Moving Forward
The Canada AI Survey represents a valuable exercise in stakeholder consultation, but it’s only the beginning of the conversation. The 30 day sprint was short and hopefully comes from a recognition that we need to move quickly. I hope that we can implement much needed change with an eye to the past, and continue adapting with an eye to the future. Our current ChatGPT moment is too narrow a scope to dictate far-reaching policy decisions.