January 9, 2017
Three decades in the past, companies were wrestling with the implementation of enterprise-wide transaction systems. Two decades ago, many organizations were anxious to develop electronic commerce capabilities. A decade ago, most firms were just beginning to think about how to embrace advanced analytics for better decision-making. Today, forward-thinking companies want to be cognitive—to master machine and deep learning, natural language processing, and other cognitive technologies.
Of course, cognitive technology is just another word for artificial intelligence, and that is hardly new. There have been several “AI winters” and “AI springs” over the past fifty years, and current technologies are mostly a powerful extension of previous ones. But there is little doubt that the flowering this AI spring is changing the garden permanently.
Leading companies have already begun to employ these capabilities. In a recent survey of executives from fifty large firms by NewVantage Partners, 44% (a plurality) named cognitive technologies the disruptive technology they most expect to impact their firms over the next decade, and 89% felt they would have some impact on their companies. 69% said their companies had already begun to use cognitive technologies.
Why Cognitive Now?
Why has this rapid growth in visibility and interest for cognitive technologies taken place, and what are the implications of it? There are both demand and supply factors underway. On the demand side, there hasn’t been much productivity improvement in advanced economies over the past several years (only 1.3% average annual growth from 2007 to 2015, and decreasing productivity in the first two quarters of 2016), and companies are anxious to learn whether cognitive technologies can finally spur productivity growth. Whatever the strengths of human beings in the workplace (and there are many), labor provided by humans alone remains expensive (even when outsourced) and difficult to manage relative to machines.
Also on the demand side, there are many situations today in which a traditional human approach to analytics and decision-making is simply impossible. These decisions need to be made with too much data and in too short a time for humans to be employed in the process. Digital advertising, medical diagnosis, predictive maintenance for industrial equipment, and many other realms of business today are impossible to execute well without some form of cognitive technologies.
On the supply side, we now have both software and hardware that is well suited to performing cognitive tasks. Both proprietary and open source software is widely available to perform various types of machine cognition. Google, Microsoft, Facebook, and Yahoo have all made available open source machine learning libraries. And some of the world’s largest IT companies are providing proprietary offerings.
Cognitive capabilities are available as standalone software, and increasingly as embedded capabilities within other types of software. IBM has placed a big bet on Watson as a standalone software offering. Salesforce.com recently announced “Einstein,” a set of cognitive capabilities that are embedded within its programs for sales, marketing, and service. Oracle has announced a series of “adaptive intelligence apps” that will interface with the company’s existing cloud-based software and learn from user behavior. We expect that virtually every major software vendor will embed cognitive capabilities in their transactional systems before long.
Data scientists, however, tell us that the supply side factors that really make a difference for this generation of AI are data and processing power. Neural networks, for example, have been available since the 1950s. But current versions of them—some of which are called “deep learning” because they have multiple layers of features or variables to make a decision about something—require massive amounts of data to learn on, and massive amounts of computing power to solve the complex problems they address. In many cases there are data sources at the ready for training purposes. The ImageNet database, for example—a research database used for training cognitive technologies to recognize images—has over 14 million images that a deep learning system can sink its teeth into.
The availability of almost unlimited computing capability in the cloud means that researchers and application developers can readily obtain the horsepower they need to crunch data with cognitive tools. And relatively new types of processors like graphics processing units (GPUs) are particularly well suited to addressing some cognitive problems. GPUs in the cloud provide virtually unlimited processing power for many cognitive applications.
Venture capital, of course, is stoking the cognitive engine. VC funding for various forms of cognitive startups increased from $282 million in 2011 to almost $2.4 billion in 2015. Many of these startups will undoubtedly be acquired by larger IT firms that sell to large enterprises.
How to Get Cognitive
As with many new technologies, the greatest impediment to progress in cognitive is management awareness and understanding. Cognitive technology is actually a constellation of related technologies that can be difficult to understand. Even IBM’s Watson—viewed by many as a monolith that won Jeopardy!—is actually a collection of more than 15 programs (APIs) that are integrated to solve particular business problems. Most organizations will need some sort of management education program to acquaint decision-makers with the possible benefits that cognitive technologies offer.
It’s also important to build on existing strengths. For many organizations, the most direct route to cognitive is through analytics. Many firms today have some analytical capabilities, and machine learning can be viewed as an automated form of analytics—“autonomous analytics.” The easiest route for organizations that already have some analytics activities underway is to start with statistical machine learning.
Also as with other technologies, it’s critical to be business-driven with cognitive technology. After managers gain some initial familiarity with the tools, they should discuss how business strategies and key processes could be aided by more knowledge and better decisions. Some organizations are already beginning to redesign key business processes with cognitive technologies in mind, incorporating the best capabilities of smart humans and smart machines into their work designs.
The world is a big and complex place, and there is increasingly data available that reflects its size and complexity. We can’t deal with it all with human experience or even with traditional, human-crafted analytical methods. Fortunately there are some exciting new technologies to capitalize on the data we have. Cognitive technologies are one of the most promising and disruptive forms of computing that the world has ever known. The time is now to begin the transition to them.
Most organizations will need some sort of management education program to acquaint decision-makers with the possible benefits that cognitive technologies offer. Peter Fingar's 75-page briefing book, Cognitive Computing, is a great place to start.