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What you need to know about ‘machine learning’

Get up to speed on the next technological revolution that will change our world.

Even if you don’t know much about machine learning, you’ve probably heard about the coolest example of the technology today — self-driving cars.

Made by manufacturers like Google and Tesla, these cutting edge cars use technology like automation, sensors, cameras, algorithms and analytics to assess risks and avoid collisions on the road.

Just as the term ‘machine learning’ implies, these vehicles actually ‘learn’ over time. They continually amass data from each driving situation they encounter, giving them more data patterns on which to base future driving decisions.

That’s the crux of machine learning; the ability of computerized systems to learn over time like the human brain, yet perform automated tasks and make decisions without requiring any human intervention. As summarized in a white paper by SAS, machine learning uses “computational methods that learn from experience to improve performance and make accurate predictions.”

While self-driving cars are just starting to legally hit the roads in some jurisdictions, machine learning has already hit the ground running to help businesses in several industries.

Machine learning in action           

Canadian-based Array Systems Computing is a pioneer of machine learning. In the 1990s, the company used early machine learning technology to detect weapons hidden in luggage at airports. Now Array applies machine learning for clients in various sectors.

In finance, Array’s software can identify high-risk customers and predict the best times to make trades. In real estate, it analyzes behaviour from website visitors to determine if they’re looking to buy, sell, lease or rent a home. It also helps agents show clients homes they’re most likely to buy based on relevant variables. Hotels use Array systems to optimize room prices based on characteristics of individual customers.

The common thread starts with data being analyzed to identify patterns and outcomes. Machine learning takes those data patterns, predicts probable outcomes and suggests the best decisions or steps to take in order to get an outcome desired by the business or its client.

What are the business benefits?

How can all of this help businesses? Potential benefits include fewer labour requirements, greater efficiency and lower costs, especially because automated analysis and decision-making require little or no human supervision. Machine learning software is also capable of quickly analyzing huge volumes of data that humans could never process.

Accuracy is another upside. According to research by McKinsey Global Institute, machine learning algorithms have allowed one UK bank to achieve an accuracy rate of more than 90 per cent in spotting fraudulent transactions.  

Get up close & personal

One of the most promising applications of machine learning is in marketing and sales. Although machine learning can analyze data from millions of customers, it can also hone in on data from just one individual client as well.

By analyzing each customer’s past purchases, website searches, payment transactions and delivery history, the software learns the shopping preferences for that specific individual. Instead of blanketing all customers with the same message, marketers can use data from each shopper to tailor product recommendations and offers created specifically for them as individuals. This is how machine learning has made the era of personalized marketing possible.

Where your business should start

It all starts with data. To lay the groundwork for machine learning in your business, examine all the internal and external data sources available to your operations, such as smartphones, equipment sensors, wearable devices, payment systems, cameras and websites. Consider which types of data would be most useful to your operations and your customers.

You may discover some data is unavailable to you or that you need to digitize parts of your operations in order to deploy machine learning systems. Data quality and integration may also be issues. Then think about how the data will be used in your day-to-day operations. Will sensor data automatically shut down your production line to avoid equipment failures? Or will website data identify potential buyers for your products?

Find out if there are any regulatory constraints around data privacy or security in your industry that could affect how your business adopts machine learning.

Finally, consider the impact of machine learning on jobs at your company. McKinsey researchers say 80 per cent of all current work activities could be automated with machine learning. It won’t happen overnight, but this technology will revolutionize work forever. Do what you can to be ready for the transformation. 

Up next: See the enormous impacts of machine learning on our roadways and beyond. How self-driving cars will change every one of these industries.

How self-driving cars will change every one of these industries

Christine Wong

Christine Wong is a journalist based in Toronto who has covered a wide range of startups and technology issues. A former staff writer with, she has also worked as a reporter for the Canadian Economic Press and in broadcast roles at SliceTV and the CBC.

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