A machine learning system can make the best possible decision if it has enough data to learn from, but it cannot judge whether any of the resulting decisions are OK ethically.
Deep learning, a variation of machine learning (ML), represents the major driver toward artificial intelligence(AI), reports Gartner.
As deep learning delivers superior data fusion capabilities over other ML approaches, the analyst firm predicts that in two years, deep learning will be a critical driver for best-in-class performance for demand, fraud and failure predictions.
"Deep learning is here to stay and expands ML by allowing intermediate representations of the data," says Alexander Linden, research vice president at Gartner.
"It ultimately solves complex, data-rich business problems.”
Gartner’s 2017 Hype Cycle for Emerging Technologies notes deep learning is receiving additional attention because it harnesses cognitive domains that were previously the exclusive territory of humans, mainly image and voice recognition and text understanding.
“Deep learning can, for example, give promising results when interpreting medical images in order to diagnose cancer early. It can also help improve the sight of visually impaired people, control self-driving vehicles, or recognise and understand a specific person's speech."
Gartner says deep learning also inherits all the benefits of ML. Several breakthroughs in cognitive domains demonstrate this.
Baidu's speech-to-text services are outperforming humans in similar tasks; PayPal is using deep learning as a best-in-class approach to block fraudulent payments and has cut its false-alarm rate in half, and Amazon is also applying deep learning for best-in-class product recommendations.
Today, most common use cases of ML through deep learning are in image, text and audio processing — but increasingly also in predicting demand, determining deficiencies around service and product quality, detecting new types of fraud, streaming analytics on data in motion, and providing predictive or even prescriptive maintenance.
Gartner points out however, ML and AI initiatives require more than just data and algorithms to be successful. They need a blend of skills, infrastructure and business buy-in.
Gartner’s advice for harnessing deep learning and related technologies around machine learning include starting with simple business problems for which there is consensus about the expected outcomes, and gradually moving toward complex business scenarios.
“Focus on data as the fuel for machine learning by adjusting your data management and information governance for machine learning,” says Gartner. “Data is your unique competitive differentiator. Although the choice of machine-learning algorithms is fairly limited, data sources are abundant and a good long-term investment.”
What's hard for people is easy for machine learning, and what's hard for machine learning is easy for people
Prepare to staff for machine learning
It is also important to nurture the required talent for machine learning, and partner with universities and thought leaders to keep up to date with the rapidly changing pace of advances in data science.
Most organisations lack the necessary data science skills for simple ML solutions, let alone deep learning. If ML projects cannot be addressed with easy-to-use applications, IT leaders will require ML expertise, says Gartner
"In this situation, IT leaders will be seeking specialists, called data scientists," says Linden. "Data scientists can extract a wide range of knowledge from data, can see an overview of the end-to-end process, and can solve data science problems."
Gartner predicts that 80 percent of data scientists will have deep learning in their toolkits by 2018.
"If one of your teams possesses a good understanding of data, has business domain expertise and can interpret outputs, it is ready to start ML experiments," says Linden. "Even if your team lacks experience with algorithms, it can start with packaged applications or APIs."
Using ML and AI to add value to a business is complicated, notes Linden.
"Don't deliberately meet all ML prerequisites exactly — instead find the right problem to solve," he advises.
"It is a good idea to start ML by using the same data you use in your popular reports, such as orders by a region. Then you can apply ML to make forward-looking predictions, for example a forecast for the same orders by a region for the next month. This way it extends on the after-the-fact reports to show business stakeholders the art of the possible with ML."
Nevertheless, ML has limitations, says Linden.
"An ML system can make the best possible decision if it has enough data to learn from — such as millions of priced items and their availability — but it cannot judge whether any of the resulting decisions are OK ethically.”
A combination of data scientists' current experience and skills with new ML capabilities will be required for successful ML and AI adoption, he adds.
"What's hard for people is easy for ML, and what's hard for ML is easy for people."
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