Gartner Symposium 2017: Deep learning driving AI development

Gartner Symposium 2017: Deep learning driving AI development
Gartner Symposium 2017: Deep learning driving AI development.
Gartner Symposium 2017: Deep learning driving AI development
Gartner Symposium 2017: Deep learning driving AI development.

During a media roundtable at Gartner Symposium 2017, hosted in Cape Town 18-21 September, Alex Linden – Research Vice President at Gartner – revealed that deep learning, a variation of machine learning (ML), is currently the major driver toward artificial intelligence (AI). As deep learning delivers superior data fusion capabilities over other ML approaches, Gartner predicts that, by 2019, 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,” said Linden. “It ultimately solves complex, data-rich business problems. 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.”

Linden pointed to the vast developments which deep learning has undergone in the past 5 years claiming that many of the best and biggest trends around AI all stem from deep learning. This progress added to the fact that deep learning also inherits all the benefits of ML has lead to several breakthroughs in cognitive domains. This is demonstrated by innovations such as Baidu’s speech-to-text services, which is outperforming humans in similar tasks, PayPal’s deep learning system, which is a best-in-class approach to block fraudulent payments and has cut its false-alarm rates in half, and Amazon which 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. 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.

How to Staff for ML
According to Gartner, 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.

“In this situation, IT leaders will be seeking specialists, called data scientists,” said 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,” said Linden. “Even if your team lacks experience with algorithms, it can start with packaged applications or APIs.”

Starting ML and AI Successfully
Gartner believes that using ML and AI to add value to a business is complicated. “Don’t deliberately meet all ML prerequisites exactly — instead find the right problem to solve,” said Linden. “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. “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,” added Linden.

“What’s hard for people is easy for ML, and what’s hard for ML is easy for people,” concluded Linden.

By Dean Workman
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