Companies involved in the artificial intelligence and machine learning industry have a unique opportunity to provide talent, infrastructure, or data to Canadian companies looking to use these technologies.
Machine Learning (ML) is an Artificial Intelligence (AI), or cognitive, technology that allows systems to learn and improve from experience through exposure to data – and without explicit programming.
When it comes to machine learning, the industry can expect big changes to the machines (and chips). Large and medium-sized enterprises are expected to intensify their use of machine learning in 2018, with the number of implementations and pilot projects using this technology expected to double 2017’s total – and double again by 2020. International Data Corporation (IDC) forecasts that spending on AI and ML will grow from $12 billion in 2017 to $57.6 billion by 2021.
The industry has moved from CPU-only to CPU-and-GPU solutions, which made chips 10 to 50 times better, and now the industry looks to offer similar solutions with field programmable gate arrays (FPGAs) and application-specified integrated circuits (ASICs). By the end of 2018, over 25 percent of all chips used to accelerate ML in the data center will be FPGAs and ASICs. These new kinds of chips should increase the use of ML, thereby enabling applications to consume less power while becoming more responsive, flexible, and capable.
There are five key areas that should make it easier and faster to develop ML solutions:
- automating data science,
- reducing the need for training data,
- accelerating training,
- explaining the results of ML better, and
- deploying local ML.
The Canadian Institute for Advanced Research (CIFAR) is leading the Government of Canada’s $125 million Pan-Canadian Artificial Intelligence Strategy. Through the Vector Institute (an independent not-for-profit institution based in Ontario that was created as a response to this initiative) their strategy is to:
- increase the number of AI researchers,
- develop global leadership on economic, ethical, policy and legal implications of advances in AI, and
- to support a national research community on AI.
Opportunities exist in Canada, as companies look to capitalize on machine learning technologies.
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