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From its early roots in artificial intelligence, cognitive computing has matured exponentially in just the last few years, fueling an influx of investment and innovation, and setting up the next wave of business disruption.

In this article, the first of a two-part series, venture capitalist Manoj Saxena, who until recently led IBM’s Watson software division, and Paul Roma and Rajeev Ronanki, principals with Deloitte Innovation with Deloitte Consulting LLP, discuss the rise of cognitive computing, where the smart money is investing, and why health care is at the front end of the adoption curve.

CIO Journal: Artificial intelligence has been around since the 1950s. What’s changed to give rise to cognitive computing as we are now coming to know it?

Saxena: I attribute the rise of cognitive computing to the culmination of five factors. First, we have an abundance of data. Corporate leaders increasingly recognize there are a lot of insights embedded in that data, which they can use to drive better decisions for their businesses. Second, we have sufficient processing power available at scale and at a reasonable cost—primarily through the cloud. Third, as Watson aptly demonstrated when it won Jeopardy in early 2011, we now have technologies that can process and understand information on a massive scale. When playing Jeopardy, Watson was processing 200 million pages in three seconds and understood the meaning of every page. Fourth, the Internet gave rise to an entirely new set of consumer companies that have forever changed the way we work and live: Amazon, Google, and eBay to name a few. As technology evolved, and people’s interactions with it changed, specifically when it comes to social and data, the landscape shifted yet again as companies today are looking to adopt social. Fifth, we have the consumerization of IT. People increasingly rely on their mobile devices to access data through powerful, intuitive, targeted consumer-based apps. They expect that same level of power in the workplace with business apps, and that is what’s also driving interest in cognitive technologies.

Roma: You can’t overstate the impact of the cloud because it fundamentally changes the cost equation. Rather than making a massive capital investment in computing infrastructure, companies can now pay to access the computing resources they need when they need them.

Application integration is also vastly simpler now. If CIOs and their business counterparts want to string together two or three application into a composite cognitive app that includes, say, mobile, sensors, and advanced cognitive techniques, they can now do that literally in a day rather than with a six- or nine-month project.

Ronanki: The components of cognitive computing—artificial intelligence, natural language processing, and machine learning—have been maturing exponentially for decades. Additionally, computing capacity continues to double every two years, following Moore’s Law, and is also available in smaller forms. These developments together bring us to the cusp of the next big wave of computing—the cognitive era.

Saxena: Even in the last year or two, the technology has changed dramatically. At the fundamental level we are seeing the emergence of a new style of IT. Computers have been very good at processing and analyzing structured data—transactions and numbers, but they have not understood natural language, text, images, videos, or tables. With the advent of the Web, social, and mobile, there’s an explosion of data in those forms—everything from product reviews to blog comments to physicians’ notes. Cognitive Scale, one startup company in my portfolio, calls this “dark data”. As much as 80 percent of data being generated today is dark data.

Where is artificial intelligence being used today?

Ronanki: Artificial intelligence is in our phones. It’s in computer systems and in chips. IBM’s Watson is really just another example of an artificial intelligence system. More specifically, it’s a cognitive computer that uses machine learning to “get smarter” over time, not simply by amassing information on a topic, but by establishing context, looking for patterns and distinctions, noticing relationships between data, assessing the veracity of information sources, assigning probabilities to one potential answer over the next, and so on.

Saxena: In all of these ways, Watson and other cognitive technologies are starting to emulate human cognition. They’re perceiving, understanding, and relating, not just to numbers, but dark data as well.

There’s a burgeoning ecosystem growing up around cognitive capabilities. What comprises it? Where is the smart money investing?

Roma: It’s true there is an ecosystem that’s growing exponentially, encompassing not just cognitive, but also content. Content is now being paired with the techniques, algorithms, and agents that know how to make it available and accessible, and interpret it to the business world.

Consider this: IBM is working with the Cleveland Clinic to train Watson to become board certified in medicine. Imagine the specialized data set coupled with the knowledge and insights needed to make that possible. This new ecosystem will include an explosion of companies looking to similarly specialize in various domains. And then to deliver the data and insights to mobile phones and other devices—yet another important component of the ecosystem.

Ronanki: Add to that the notion of contextual computing, in which location, meaning, and purpose all fit together in answering a question. If you’re searching for a restaurant in New York, do you want to compare sites, write a review, or make a reservation? Those are three different contexts.

Can you give an example that illustrates the cognitive ecosystem?

Saxena: Here is an example of a cognitive app that’s just over the horizon. Imagine you’re a farmer in Iowa or in sub-Saharan Africa. You’re out in the field working and your 10-year-old daughter comes running to you and says, “Dad, something bit me but I don’t know what.” You take a picture of the bite on your smartphone, and send it to the cognitive cloud. There it gets processed and tells you there’s a 92 percent chance that it’s a spider bite, and a 47 percent chance it’s a snake bite. It displays the triage information on how to manage a spider bite along with a map to the nearest doctor. It also gives you the doctor’s telephone number, and asks if you’d like the phone to place the call.

This is an example of democratizing medical knowledge at the point of care. To make it possible, you need content in the cloud, you need the cloud itself to process the content and understand it, and you need a mobile device manufacturer.

There’s a role for entities that bring these parties together. Deloitte is doing that with its greenhouses. IBM is also doing this with its BlueMix Garages that were announced in April. Cognitive Scale and other startups are also spearheading a lot of these partnerships.

Are corporate buyers simply looking to procure solutions or are they making direct investments and trying to control and influence the build-out of the ecosystem?

Saxena: A relatively small number of companies are playing an active investment role—including setting up their own funds. Yes, they hope to earn returns on those investments, but the real payout comes from working side-by-side with innovators to develop the technologies they can then adopt, and that can have an outsized impact on their operations—especially on costs. More typically, companies will look to purchase the solutions they need through the ecosystem.

Why is health care on the short list of industries pursuing cognitive analytics aggressively?

Ronanki: It’s true we are seeing a lot of interest across health care sectors—life sciences, care providers, health plans, and medical device manufacturers—and across stakeholders, including consumers, and state and local governments. In the public policy arena, cognitive analytics can provide empirical feedback on a set of policies to determine their effectiveness. Fact-based insights can replace pure ideology in ways that can improve the cost-effectiveness and efficiency of the health care system while also improving patient outcomes.

Roma: In care management, we’re currently seeing more cognitive computing and analytics being adopted outside the U.S. In countries with a single-payer system, for example, or where providers are state-run entities, there’s more likely to be a large data set readily available. Certain countries have already mapped the genome of every citizen, and their medical records are available electronically. Innovations can be rolled out to an entire population simultaneously. That’s not the case in the U.S., where the health care system is extremely fragmented.

Saxena: There is some exciting work being done with cognitive analytics at U.S. health care systems, health plans, and even employer groups, mainly in three areas.

The first is patient engagement. Rather than seeing their doctors only when they show up at the emergency room—the most expensive point of care— patients can continually stay connected with their doctors and receive care proactively. Suppose you’re a physician managing a population of patients with asthma. Your cognitive app alerts you to an expected elevation in pollen levels in certain zip codes the following day. The app combs through patients’ electronic medical records and lab reports, and identifies those patients likely to experience an adverse reaction, who also have low access to care and a history of noncompliance with their asthma meds. From a population of dozens or hundreds of patients, you’ve now zeroed in on four who are most likely to show up at the ER with an acute asthma attack. You then contact them directly on their smartphones with an early warning and recommendations on steps to take to sidestep a potentially lethal medical incident.

The second area for cognitive systems is population health—understanding who’s most at risk of falling sick and proactively addressing health risks before they become too serious, thus most likely to bring additional, avoidable costs into the system. For example, the new Medicare Hospital Readmissions Reduction Program includes a provision that says payments to providers for heart surgery will be denied if a patient is re-admitted to the hospital within 60 days of surgery. Clearly, it’s in everyone’s best interest to avoid re-admissions. By giving the patient a cognitive engagement app and sensors, the hospital can monitor their compliance with meds, fitness activity, and more. The cognitive system can analyze this dark data and alert the doctor if a patient is at risk of re-admission.

The third area is cost control, or gaining visibility into costs—by looking at benefits, claims, patient records, and so on—and identifying improvements that can reduce costs while also improving outcomes.

Roma: Consumers are eager for, and accepting of, devices that help them to better understand and monitor their health, motivate them to take better care of themselves, and engage directly with their care providers. Think of cognitive apps as fitness bands on steroids.

Next up: In the second story in this two-part series, we discuss cognitive analytics vs. business intelligence, the CIO’s role in bringing cognitive analytics into the enterprise, and the security, privacy, and trust issues that may give CIOs pause.