Why this is important to Wisconsin businesses: The number of projects using this technology was expected to double from 2017 to 2018, and double again by 2020.

Canada has emerged as a leader in artificial intelligence, with over $1.3 billion in funding announced for artificial intelligence research and development in 2016-2017. Machine learning is an artificial intelligence or cognitive technology that allows systems to learn and improve from experience through exposure to data without being programmed explicitly. When it comes to machine learning, the industry can expect big changes to the machines (and chips). In 2018, large and midsize enterprises are expected to intensify their use of machine learning. The number of implementations and pilot projects using this technology is expected to double compared to 2017, and that number is expected to double again by 2020. International Data Corporation (IDC) forecasts that spending on artificial intelligence and machine learning will grow from $12 billion in 2017 to $57.6 billion by 2021. The Canadian Institute for Advanced Research (CIFAR) is also leading the Canadian government’s $125 million Pan-Canadian Artificial Intelligence Strategy. Through the Vector Institute, an independent nonprofit based in Ontario that was created as a response to this initiative, their strategy is to:

  • increase the number of artificial intelligence researchers;
  • develop global leadership on economic, ethical, policy and legal implications of advances in artificial intelligence; and
  • support a national research community on artificial intelligence.

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 machine learning in data centers will be FPGAs and ASICs. These new kinds of chips should increase the use of machine learning, thereby enabling applications to consume less power while at the same time become more responsive, flexible and capable.

Opportunities exist as companies look to capitalize on machine learning technologies. These five key areas should make it easier and faster to develop machine learning solutions:

  1. automating data science;
  2. reducing the need for training data;
  3. accelerating training;
  4. better explaining the results of machine learning; and
  5. deploying local machine learning.