Data

New Vocabulary: Cognitive Computing

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Cognitive Computing: A Definition Cognitive computing refers to systems that learn at scale, reason with purpose, and interact with humans naturally.

What it means

Cognitive computing systems aren’t programmed; they’re trained to sense, predict, infer and, in some ways, think, using artificial intelligence and machine learning algorithms that are exposed to massive data sets. These systems improve over time as they build knowledge and acquire depth in specialty areas or “domains.” Current computing systems require that rules be hard-coded into a system by a human expert. However, cognitive computers program themselves; they process natural language, make sense of unstructured data and learn by experience much in the same way humans do. These systems not only bring massive parallel processing capabilities to churn through enormous volumes of often fluid data, but also use image and speech recognition as their eyes and ears, making interaction with human teams more natural. The dynamic learning inherent in these systems provides a feedback loop for machines and humans to refine insights and teach one another.

Why it’s important

Cognitive computing will enable new business models and change the way entire industries work, allowing business and government leaders to take on projects of previously insoluble size and complexity. It combines massive data sets with sophisticated analytics, natural language processing, and machine learning to help human experts synthesize findings and improve decision-making. In healthcare, for instance, cognitive computing systems are helping oncologists at Memorial Sloan Kettering Cancer Center determine the best care for individual patients in a manner that would otherwise be impossible. The system combines millions of pages of medical journals with radiology images, electronic medical records, and patient DNA information to help diagnose and recommend personalized treatment options. World-renowned oncologist Dr. Larry Norton, of Sloan-Kettering, says in the book Smart Machines, “Computer science is going to evolve rapidly, and medicine will evolve with it. This is co-evolution. We’ll help each other. I envision situations where myself, the patient, the computer, my nurse, and my graduate fellow are all in the examination room interacting with one another.”

And it’s not just healthcare. The same cognitive techniques can be applied to any industry challenge involving big data. Oil and gas companies can combine seismic data with current events, economic trends, and other sources of information to pinpoint the most promising locations to mine for natural resources. Educators can combine test scores with attendance records and information about learning styles to craft lesson plans that are customized to an individual. That same approach can be used to target marketing campaigns to individuals.

What will change

The applications of cognitive computing to business are endless. Some experts believe that this technology represents our best — perhaps our only — chance to tackle some of the most enduring systemic issues facing our planet, from understanding climate change to identifying risk in our increasingly complex economy.

But this new form of computing will require collaboration and different types of partnerships, ones that extend across the public and private sector and into academic and research organizations. Independent software and services companies will be in demand to design applications that run on new cognitive computing platforms and create specialized offerings to meet the needs of different users and organizations.

The capabilities enabled by cognitive computing will force business leaders to rethink their operating models. While some processes may be refined, others will need to be reinvented, and still others built from scratch. New skills and training will be required, such as developing the ability to design and frame appropriate challenges for cognitive systems.

New ways of thinking, working and collaborating will invariably lead to cultural and organizational change, some of which may be challenging, particularly for managers accustomed to relying on their own judgment and experience to form decisions rather than working in a data-driven partnership. But these issues, like any transformation, can be resolved through an effective change management program.

Conclusion

We are entering an era of cognitive computing, marked by the marriage of robust computing horsepower with a “brain-like” interface capable of synthesizing vast amounts of data and continuous “learning.” Those capabilities will enable organizations to tackle problems of enormous complexity and help researchers, diagnosticians, and businesses form insights and derive solutions that would previously take generations. In the near-term, cognitive computing will make it possible for businesses to transform systems and processes ranging from supply chain to marketing. Longer term, cognitive computing will lead to whole new markets and business models. New types of partnerships are needed to take advantage of these opportunities, ones that transect business, academia and technology. Laying the groundwork now will give forerunners a distinct advantage.

Key Questions to Ask

1. What are the most valuable data sets — both structured and unstructured — that your organization can access?

2. What long-term challenges could your organization address by combining and analyzing those data sets?

3. What skills will be needed to cultivate internally to take full advantage of cognitive capabilities?

4. With whom should you partner on a business and technological front to pilot initiatives and optimize investment?

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