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

Experts predict artificial intelligence (AI) and machine learning will usher in a golden age in 2021, which will solve some difficult business problems.

Machine learning trains computers to learn from data with minimal human intervention. Science is not new, but recent developments have given it fresh impetus, Jin-Wan Jung, Senior Director and Leader, Advanced Analytics Lab at SAS. "The development of technology has really helped us," Jung said. "Due to the increase of data and computational power it is possible to make real-time decisions that support self-driving cars or robotic automation."

Jung stated that the COVID-19 crisis also carried forward this practice. "We're using machine learning for things like predicting the spread of disease or the need for personal protective equipment," he said. Lifestyle changes mean AI is being used more often at home, such as when Netflix makes recommendations on the next show, noted Jung. Also, companies are turning to AI to improve their agility to deal with market disruptions.

Rust observations are supported by the latest IDC forecasts. It is estimated that global AI spending will double to $ 110 billion over the next four years. How will AI and machine learning impact in 2021? The top five trends have been identified by Jung and his team of elite data scientists at the SAS Advanced Analytics Lab:

Machine learning and Internet of things (IoT) combine to transform industries
The Canadian Armed Forces rely on Lockheed Martin's C-130 Hercules aircraft for search and rescue missions. The maintenance of these aircraft has been replaced by the marriage of machine learning and IoT. Six hundred sensors located throughout the aircraft produce 72,000 rows per flight hour, including fault codes on failed parts. By implementing machine learning, the system develops real-time best practices for aircraft maintenance.

"We are cutting edge intelligence, which is fast and smart and is the key to profit," Jung said. In fact, the combination is so powerful that Gartner predicts that by 2022, more than 80% of enterprise IoT projects will incorporate AI in some form, from just 10 percent today.

Computer vision becomes mainstream
Computer vision trains computers to interpret and understand the visual world. Using an in-depth learning model, machines can correctly identify objects in videos or images in documents, and react to what they see.

The practice already has a major impact on industries such as transportation, healthcare, banking and manufacturing. For example, a camera in a self-driving car can identify objects in front of the car, such as a stop signal, a traffic signal, or a pedestrian, and react accordingly, Jung said. Computer vision has also been used to analyze scans to determine whether the tumor is cancerous or benign to avoid the need for biopsy. In banking, computer vision can be used to view counterfeit bills or for processing document images, which increasingly rob cumbersome manual processes. In manufacturing, it can improve defect detection rates by up to 90 percent. And it is also helping to save lives; This allows cameras to monitor and analyze power lines for early detection of wildfires.

Faster customization than darwin
At the core of machine learning is the idea that computers are not trained only on the basis of a stable 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," Jung said. "Business is going to move more and more in this direction."

Currently, adaptive learning is often used in fraud investigations. Machines can use data or feedback from investigators to correct the ability to spot 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. While this is going to work, however, automated business processes can adapt to different situations over time.

Democratization of analytics
To deliver returns for business, AI cannot simply be placed in the hands of data scientists, Jung said. In 2021, organizations want to create more value by putting analytics in the hands of those who can gain 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," Jung said. As an example, Gartner suggests that AI will increasingly become part of the mainstream DevOps process providing a "clear path to value".

More focused on ethical issues
Responsible AI will become a high priority for officers in 2021, Jung said. Ethical issues have been raised by businesses in the past year for the use of AI for surveillance by law enforcement agencies or for marketing campaigns. There is also worldwide discussion of law related to responsible AI.

"There is a possibility of bias in the machine, the data or the way we train the model," Jung said. "We must make every effort to double and triple check procedures and gatekeepers to ensure compliance, confidentiality and impartiality." 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 using Chief Analytics Officers (CAOs) and resources, which are the best way to advance analytics. However, organizations of any size can benefit from AI and machine learning, even if they lack in-house expertise.

Jung suggests that if organizations do not have experience in analytics, they should consider assessing how to transform data into a competitive advantage. For example, the Advanced Analytics Lab on SAS provides an innovation and advisory service that provides guidance on value-driven analytics strategies; Organizations helped define a roadmap that aligns with business priorities starting with data collection and maintenance and for the deployment of analytics through implementation and monitoring to meet the vision of the organization. "As we move into 2021, organizations will discover the value of analytics to solve business problems."

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