Reality Check: How Smart Is AI, Anyway? A Look at AI Applications

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May 17, 2018      

For all the talk about artificial intelligence, machine learning, and deep learning, experts said some businesses and consumers may have unrealistic expectations of what an AI can do. It’s time for a reality check on what AI applications are possible, and what people can expect in the future. So, how smart is AI?

Companies handling very large data volumes, such as in digital commerce, customer service, and digital marketing, are finding success from AI applications, said Whit Andrews, vice president and distinguished analyst at Gartner Inc.

Anand Rao, PwC, discusses realistic AI applications

Anand Rao, PwC

“[AI is] helpful in image analysis-related projects that have security and monitoring applications,” said Andrews. “It’s useful in large-data activities such as financial services practices, including automated trading, and expert advising. It can also be used in predictive maintenance.”

Image recognition is another key area of success for AI applications, said Anand Rao, innovation lead for the Analytics Group at consultancy PwC. Rao said he believes the ability of systems to take video or image files, understand them, describe them, search for specific features, and even to generate new images “is opening up a large number of consumer and business applications.”

In addition, Rao said two other current AI applications are key. The first is conversational AI, relating to chatbots and conversational systems. Such systems can help users in their personal lives – for example, travel, finances, health, or search requests.

The second application is natural language processing, including the ability to take large text documents, such as legal documents, invoices, or research papers. The systems can organize and convert them into “topics and the associations of these topics” before analyzing them, Rao said.

A mismatch between expectations, reality

Despite a growing number of AI applications, Andrews said there may be a mismatch between people’s expectations and reality, especially since some people “expect more than they can get from AI reasonably.”

Andrews said his clients sometimes ask about a new business problem that they find intractable. They “hope aloud that AI can fix it, since nothing else could,” he observed. Even if the problems can’t be solved with AI, Andrews said that it might now “very well be possible to try successfully.”

“No business sector is uniquely vulnerable to unreasonable dreams,” he said. “What makes people more likely to be more wishful than realistic is lacking well-defined, clearly described data with measurable outcomes.

“It’s not reasonable to expect AI to immediately improve outcomes that are 10 years in the future unless you have data that is respectably reliable now, and you have points between now and then that will allow you to feel comfortable you’re improving the likelihood of that future improvement,” said Andrews.

Rao said there is currently a lot of hype around AI, machine learning and deep learning. While there have been “significant advances” in very specific tasks, such as recognizing objects in images, better understanding of language and better voice recognition and speech generation, Rao said he believes that a “true general purpose intelligence is still a long way off.”

“The hype has resulted in big expectations​ and ‘myths’ around what AI can do and cannot do,” said Rao. “The mismatch between expectation and reality spans all business sectors.”

The hype has been particularly notable in relation to the ease of access for machine learning and deep learning, Rao said. The process of democratizing AI and making it easier for everyone to access these technologies could become a double-edge sword, he said.

“While it may enable more business users to use these technologies, it also creates the risk of not using them properly, which will result in greater issues of trust and verifiability in the future,” said Rao. “The responsible use of AI is critical for all businesses to understand, as well as how to build and test robust and safe AI.”

AI applications to improve integration between endpoints

Andrews said a key point to understand is that AI “first improves the interface between people and computational resources.” He described a friend of his who is more than 80 years old and who “uses her speech-enabled speaker much more than she uses her PC, although she doesn’t even own a smartphone.”

“Ultimately, AI will be the endpoint of integration,” said Andrews. “We’ll see devices and applications more successfully interact than ever before.”