In the latest issue for Fortune, Erin Griffith examines the investment trends in AI (Artificial Intelligence) technology and poses the question: is AI an overhyped fad or a revolution? She writes, “There’s an easy way to tell when the hype around a technology trend has peaked. 1) Are the smartest venture capitalists complaining about valuations? 2) Are big tech companies snapping up start-ups so young they can barely be considered real businesses? 3) Are Fortune 500 executives talking about their [insert trend here] strategy? If the answer to any of these questions is yes, congratulations! You’ve identified a fad.”
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Of course, most revolutions look like fads in their early days—because they are. Distinguishing between those fads that will fade and those that will become the norm in the long term can be difficult, and as in all things, hindsight has a much higher success rate than foresight when it comes to identifying the winners and the losers. So what data might guide such an evaluation?
The research firm CB Insights recently reported that in 2016 there were 658 venture capital deals in the AI sector. In 2016, that amounted to $5 billion in startup funding deals, a significant increase from $589 million in 2012. International Data Corporation projects worldwide revenue from artificial intelligence and cognitive systems to be $47 billion in 2020, up from $8 billion in 2016.
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CB Insights selected 100 of the most promising artificial intelligence startups globally from a pool of 1,650 candidates based on factors like financing history, investor quality, and momentum. A look at the top 50 shows that AI is surging worldwide with 20% located outside the United States. They cover a wide range of market segments: core AI, FinTech, auto, health care, commerce, CRM, cyber-security, robotics, business intelligence, and text analysis and generation.
Interestingly, the fact that AI does not necessarily intersect with established business cases has not proven to be a hurdle to investment. “These are not businesses,” says John Somorjai, executive vice president of corporate development at Salesforce, which has acquired a handful of AI companies. “These [deals] are about technology and talent.”
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The development of artificial intelligence has inspired both fascination and dread.
In 1955, the term AI represented the concept of autonomous systems modeled on the structure of the human brain. At the same time, other researchers were tackling a different problem: finding patterns in what was then considered great volumes of data and making proper selections, or decisions, based on that data. In 1956 William Ross Ashby wrote in his Introduction to Cybernetics that “…what is commonly referred to as ‘intellectual power’ may be equivalent to ‘power of appropriate selection’.” This was not intended to as “artificial intelligence” in the way we typically understand it, and in fact was labeled as the inverse: IA, or Intelligence Augmentation. If this model sounds suspiciously familiar, it is because today’s AI systems are constructed on the IA paradigm. Our real-world applications, including language processing, machine learning, and human-computer interaction are based on IA—data pattern recognition and appropriate decision making—and as such, they augment our capacity to understand what is happening in the complex world around us. While the term “AI” became the label of choice for such technology, it is an ironic misnomer.
Let’s look at the “Why You Should Let Artificial Intelligence Creep Into Your Business” article in the March, 2017 issue of Inc magazine for some definitions:
How AI works: problem solving: Unlike traditional computing, which delivers precise solutions within defined parameters, AI, sometimes referred to as cognitive computing, teaches itself how to solve problems. “Instead of delivering specificity, AI-centric programming generates millions of solutions, evaluating each for efficacy and then choosing the most viable and optimal ones,” says Amir Husain, CEO and founder of SparkCognition.
What it does better: data diving: Manually finding your target customer, by searching and poring through income-level, interest-based, and geographical data, is labor-intensive and time-consuming. AI cuts to the chase. “For example, using a feed of three key pieces of information that the entrepreneur provides; a brief product description text, images and a price range; an AI system can zip through social media and other online outlets, looking for correlations between product and digital conversations,” says Husain. If you give it the green light, AI’s natural language processing technology then writes and sends a sales pitch, notes transmission times, and analyzes feedback. “You can almost hear an AI system going, Aha! I’ve cracked the code.” says Husain, adding that AI constantly optimizes itself by making slight changes to the message.
Where it works: practical apps: One key reason for AI’s upsurge is entrepreneurs’ free or inexpensive access to libraries such as IBM Watson, Goggle TensorFlow, and Microsoft Azure. These application programming interfaces (APIs) allow coders to build AI apps without starting from scratch. Husain expects to see a proliferation of AI-centric marketing, sales and other service startups focused on small and medium-size businesses.
Let’s look at some specific examples from the same article.
Call Centers: The biggest misconception about AI is that it’s robots with human faces sitting at remote desks. “AI is nothing more than an add-on technology, spice and flair, to an otherwise conventional system, such as a traditional travel-reservation site that, because of AI can now converse with a human,” says Bruce W. Porter, an AI researcher and computer science professor at the University of Texas, Austin. Porter emphasizes that future breakthroughs will not be 100 percent AI. “AI will likely provide a 10 percent product or service performance boost,” he says. That is, in fact, huge. Firms that fail to make the leap, he says, may fail to have customers.
Information Retrieval: Not all searches are as simple as typing a few keywords and having Google take over. Entrepreneurs often need more in-depth and complicated excavations for patent and trademark data, for example and that, in turn, involves an often-hefty legal budget to pay a highly-trained human to do. Porter foresees within five years many companies offering services to consumers who have no experience in AI or specific knowledge fields. They’ll be able to conduct their own AI based data retrieval. Count on industry disruption, he says, as this type of AI application will leapfrog current data-retrieval-service providers.
Contract Generation: Because it’s able to generate natural language, AI is an exceptional tool for helping entrepreneurs assemble contracts, as opposed to buying them off the shelf at, say LegalZoom. AI applications will converse with – by text and, ultimately, voice – and tease information out of humans that will become components of formal agreements, such as details about fee payments and product returns. Porter anticipates users will pay to access cloud-based AI computer systems to produce such documents. AI-centric startups, because they don’t require a human in the loop and won’t need to hire staffers, can offer their services at a very low cost, especially given an anticipated large volume of customers and business competition.
AI can displace humans, but it can’t replace them.
Leaders of every industry and institution are sprinting to become digital. Who will win? The answer is clear: It will be the companies and the products that make the best use of data. And the ones that make the best use of data will likely be the ones that use AI to gain efficiencies in data analysis and decision making.