Continual Skills Development for Canadian Competitiveness in AI

What are the main forces shaping the future of work and innovation? 

Over the long-term, the large trend is to move away from a natural resources economy in which we pull things from the ground and pluck them from the earth into a knowledge-based economy, in which we use our understanding of the systems and societal patterns to provide enhanced value, productivity, and effectiveness for our industries. In that context, the dominant players are no longer manual labour, they are now technology, data, and the ability to apply data to technology. 

What impact will those forces have on Canada’s future economy and workforce? 

Most Canadians tend to think that we must be behind; it is just a natural place to be. In fact, we are much better off than we might think. We are already a highly educated society and we have a lot of knowledge workers today. CBRE reports will point to how the Toronto-Waterloo corridor is rich in tech skills. We have one of the largest tech workforces and in some years we have one of the fastest growing regional skill sets growth in the tech workforce. We are actually pretty well-positioned for this with respect to our raw skillset and the population that is flooding into Canada because of our positive immigration policies and our respect for people of all types.  

“Canada leads in particular areas, like AI, and must utilize those areas to get out in front internationally.” 

That means we have to utilize that and that requires a certain amount of initiative and desire to compete and perform on not only a national stage but on an international stage. That is an area where we have not always tried to lead and part of what we need to do here is to recognize that Canada leads in particular areas, like AI, and must utilize those areas to get out in front internationally. 

What are the main impacts of AI in Canada and on which sectors of the economy will it experience the most disruption?  

Artificial intelligence is a broad field. It is a science of prediction, based on statistical models and on aggressive computer science. As a result, we have tools that, with enough training, can be surprisingly precise in their predictions of information. That is the frontline of the knowledge economy today; allowing an organization to understand itself and its processes, become more efficient, and study customers to understand what services they really want. Artificial intelligence enables us to study a population’s health in order to identify which techniques are failing and which might work, and it generally provides a very broad empowerment of organizations that can take advantage of resources with recordable technology. They can improve human processes by recording the information, pointing out the efficiencies and inefficiencies, and allowing the training processes for onboarding employees to be more effective. 

“The future of work is dominated by where society goes, and that is dominated by its economies, and they are dominated by outcompeting.” 

With that all said, how does this affect the future of work? The key thing is that the future of work is dominated by where society goes, and that is dominated by its economies, and they are dominated by outcompeting. In a world in which we compete with one another in order to determine the technologies that are going to be more widely adopted and are going to return better results, how does an economy like Canada’s exploit the tech coming down the pipeline and deliver it to a workforce that is ready to make the best use of it? In that sense, I think this is a question of going to your strengths, outcompeting your strengths, delivering a richer economy, and using that richer economy to fill in the gaps and bring everyone along. That is basically the formula for governing in the country we live in today, and the strengths are coming from our well-trained workforce, our diverse societies, and our willingness to be mathematically educated and take advantage of that.  

The places in which we we want to double down and grow are in the financial services, manufacturing bases, precision medicine, and precision agriculture, to address the ways in which we run our society, and that will allow us to compete. 

“Continual learning is the main thing we want to achieve in a society that is going to be agile and competitive in a knowledge economy.” 

The future of work is frequently seen as a dog-whistle to indicate some people might lose their jobs. I do not want to suggest that that is not a possibility, because economies and the skills you value change over time. What we need to do most in our society is become more adept at continual learnng, which means improving our skills not just in the coming years but throughout our lives. We need to find ways where we can be learning what is changing, where the new technologies are, how to adapt our skills to take advantage of those technologies, and how to be the organization that is recommending innovation and competing.  Continual learning is the main thing we want to achieve in a society that is going to be agile and competitive in a knowledge economy. 

What skills are needed for Canada to continue to innovate and to commercialize our AI innovations globally? 

People frequently talk about how data is the new oil or the new gold, but that leads people to be highly complacent to say we keep records so therefore we have gold. There are two massive problems with that, the first one is garbage in, garbage out. If you do not keep good records and if you do not have a path of evaluating your own records and improving the information content that they capture, then you do not have an asset.  

“If you are not trying to use your data, you are not improving its value.” 

Generally, if you have recorded information and you are not using it, it is probably not useful and it is certainly not increasing in value. A large number of individuals believe their data is massively valuable, even companies, but they do not really know how to use it so they are going to sit on it because they are protecting its value. If you are not trying to use your data, you are not improving its value. There is a need to not just collect data but to actively do something with it. 

“The big challenge is how we will push continual education through our organizations and develop stronger skills for staying on top of technology.” 

The second part of this is the tools to act on it. Too many organizations and companies treat this like an opportunity to bring in a specialist for a short period of time to convert their assets and data into something that they can then just turn a crank on and reliably get improved value out of it. That is not the correct way to view it. The correct way to view it is that your data will need to continually be improved through exploration and the use of data is a process of modeling, testing, and reacting. It is an agile process, so you are going to need to be continually developing and improving your data as well as continually developing and improving your use of data to underpin your organization. That is not something in which you call in an export for a short period of time so they can give you back an artifact and you can just turn it on and it generates assets. What this means is that companies need to change themselves, instead of hiring a ringer for a short period of time. Changing yourself is hard, so the big challenge is how we will push continual education through our organizations and develop stronger skills for staying on top of technology. 

What would be your pitch—and to who—to improve skills development for Canada’s future workforce and innovators? 

I would say that everyone needs practice. We need to create experiential opportunities for practitioners in companies, executives, and professors to see modern technology working. In my experience, it is deep learning. We recently did a project involving a dozen companies and 30 practitioners who experimented with modern natural language processing techniques to see how a natural language model ad fine-tuning can create a very powerful question and answer tool. That experience has already affected multiple companies in the way their products are developed, the way their employees are developed, and the training exercises they do internally. Experiential opportunities are important.  

“We need to create experiential opportunities for practitioners in companies, executives, and professors to see modern technology working.” 

Government should not simply fund academic research by professors in isolation, but ensure that deployment is a funded agenda item in the project to enable companies to become practitioners of the technology. There are academic practioners, companies like Vector, or the National Research Council of Canada Industrial Research Assistance Program (NRC IRAP) who want to show them the technology. Get your employees spending some time tracking the new technologies, getting their hands dirty, and bringing the technology back into the company to try at home.