Five real world AI and machine learning trends that will make an impact in 2021

Experts predict artificial intelligence (AI) and machine learning will enter a golden age in 2021, solving some of the hardest business problems.

Machine learning trains computers to learn from data with minimal human intervention. The science isn’t new, but recent developments have given it fresh momentum, said Jin-Whan Jung, Senior Director & Leader, Advanced Analytics Lab at SAS. “The evolution of technology has really helped us,” said Jung. “The real-time decision making that supports self-driving cars or robotic automation is possible because of the growth of data and computational power.”

The COVID-19 crisis has also pushed the practice forward, said Jung. “We’re using machine learning more for things like predicting the spread of the disease or the need for personal protective equipment,” he said. Lifestyle changes mean that AI is being used more often at home, such as when Netflix makes recommendations on the next show to watch, noted Jung. As well, companies are increasingly turning to AI to improve their agility to help them cope with market disruption.

Jung’s observations are backed by the latest IDC forecast. It estimates that global AI spending will double to $110 billion over the next four years. How will AI and machine learning make an impact in 2021? Here are the top five trends identified by Jung and his team of elite data scientists at the SAS Advanced Analytics Lab:

Machine learning and the Internet of Things (IoT) combine to change industries

Canada’s Armed Forces rely on Lockheed Martin’s C-130 Hercules aircraft for search and rescue missions. Maintenance of these aircraft has been transformed by the marriage of machine learning and IoT. Six hundred sensors located throughout the aircraft produce 72,000 rows of data per flight hour, including fault codes on failing parts. By applying machine learning, the system develops real-time best practices for the maintenance of the aircraft.

“We are embedding the intelligence at the edge, which is faster and smarter and that’s the key to the benefits,” said Jung. Indeed, the combination is so powerful that Gartner predicts that by 2022, more than 80 per cent of enterprise IoT projects will incorporate AI in some form, up from just 10 per cent today.

Computer vision goes mainstream

Computer vision trains computers to interpret and understand the visual world. Using deep learning models, machines can accurately identify objects in videos, or images in documents, and react to what they see.

The practice is already having a big impact on industries like transportation, healthcare, banking and manufacturing. For example, a camera in a self-driving car can identify objects in front of the car, such as stop signs, traffic signals or pedestrians, and react accordingly, said Jung. Computer vision has also been used to analyze scans to determine whether tumors are cancerous or benign, avoiding the need for a biopsy. In banking, computer vision can be used to spot counterfeit bills or for processing document images, rapidly robotizing cumbersome manual processes. In manufacturing, it can improve defect detection rates by up to 90 per cent. And it is even helping to save lives; whereby cameras monitor and analye power lines to enable early detection of wildfires.

Adapting faster than Darwin

At the core of machine learning is the idea that computers are not simply trained based on a static set of rules but can learn to adapt to changing circumstances. “It’s similar to the way you learn from your own successes and failures,” said Jung. “Business is going to be moving more and more in this direction.”

Currently, adaptive learning is often used fraud investigations. Machines can use feedback from the data or investigators to fine-tune their ability to spot the fraudsters. It will also play a key role in hyper-automation, a top technology trend identified by Gartner. The idea is that businesses should automate processes wherever possible. If it’s going to work, however, automated business processes must be able to adapt to different situations over time, Jung said.

Democratization of analytics

To deliver a return for the business, AI cannot be kept solely in the hands of data scientists, said Jung. In 2021, organizations will want to build greater value by putting analytics in the hands of the people who can derive insights to improve the business. “We have to make sure that we not only make a good product, we want to make sure that people use those things,” said Jung. As an example, Gartner suggests that AI will increasingly become part of the mainstream DevOps process to provide a “clearer path to value.”

Greater focus on ethical issues

Responsible AI will become a high priority for executives in 2021, said Jung. In the past year, ethical issues have been raised in relation to the use of AI for surveillance by law enforcement agencies, or by businesses for marketing campaigns. There is also talk around the world of legislation related to responsible AI.

“There is a possibility for bias in the machine, the data or the way we train the model,” said Jung. “We have to make every effort to have processes and gatekeepers to double and triple check to ensure compliance, privacy and fairness.” Gartner also recommends the creation of an external AI ethics board to advise on the potential impact of AI projects.

Where to start

Large companies are increasingly hiring Chief Analytics Officers (CAO) and the resources to determine the best way to leverage analytics, said Jung. However, organizations of any size can benefit from AI and machine learning, even if they lack in-house expertise.

Jung recommends that if organizations don’t have experience in analytics, they should consider getting an assessment on how to turn data into a competitive advantage. For example, the Advanced Analytics Lab at SAS offers an innovation and advisory service that provides guidance on value-driven analytics strategies; by helping organizations define a roadmap that aligns with business priorities starting from data collection and maintenance to analytics deployment through to execution and monitoring to fulfill the organization’s vision, said Jung. “As we progress into 2021, organizations will increasingly discover the value of analytics to solve business problems.”

SAS highlights a few top trends in AI and machine learning in this video.

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Jim Love, Chief Content Officer, IT World Canada

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Cindy Baker
Cindy Baker
Cindy Baker has over 20 years of experience in IT-related fields in the public and private sectors, as a lawyer and strategic advisor. She is a former broadcast journalist, currently working as a consultant, freelance writer and editor.

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