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The most popular trends in cognitive computing

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Republished from the Watson Blog


With over 500 companies developing cognitive systems, we’re seeing patterns emerge around the creation of cognitive systems at the business unit, business process, and application levels. By selecting a subset of these companies to compare, we can see a few of the leading business units and processes that are going cognitive. We also discover how various Watson services are combined to create to address these business needs.

These topics and more were covered in the recent Emerging Cognitive Patterns webinar.

A few highlights are discussed briefly below; refer to the full webinar slide deck for complete details.

Over 500+ Cognitive Applications in 30+ Industries

The graphic below shows a small sample of the 500+ companies developing cognitive applications. Their solutions cover a range of industries and business units, but their goal is the same: business process transformation. Call centers are a natural place for cognitive because they have high volumes of natural language queries for which answers are often buried deep within internal knowledge bases. But you might not have expected to see entertainment in this mix as well. Edge Up Sports embedded Personality Insights into their fantasy sports league app to provide profiles of players based on their social media activities.

Within call centers, we see four IBM Watson services being combined to more quickly address customer needs by improving call intelligence. This happens through language translation (Chat Lingual) to allow human agents to communicate with a broader range of customers and call analytics that ensures agents are adhering to defined scripts and complying with business guidelines: Speech IQ and Yactraq. In some cases, humans are now allowed to focus on more complex customer issues by delegating simpler problems to virtual call agents. This has inspired companies like Macaw Speech to provide cognitive speech tooling so call center managers can rapidly deploy more powerful virtual agents across their call centers.

Analyzing Personal Decision Making

We could write a book on where cognitive is being applied, but let’s shift to a short example of how to apply cognitive to a specific use case: understanding an individual’s personal decision-making process? To start, we need a framework that captures the decision patterns that guide a person to decide on a specific action. The diagram below shows such a framework that IBM has established based on psychology, consumer behavior, and decision theory.

By overlaying the IBM Watson services on top of the framework, we see how cognitive technologies provide the ability to understand and support individual decision-making. On the left, Personality Insights and Tone Analyzer help us understand who a person ‘is” and how they’re feeling by analyzing what a person writes about and the emotions hidden in the words written. When combined with the context of a person’s writing (Alchemy Language), we can help them retrieve the most relevant information and explore the tradeoffs in their actions. This can be best expressed by looking at how this is being done in practice as shown in the next graphic.

Here we see patterns in solving the matchmaking problem that is a common theme when interacting with people. This can occur when connecting similar people, finding the right person to satisfy a company’s need, or monitoring a person’s behavior to understand and influence how they behave. In each case, the challenge is to extract useful personal characteristics that can be analyzed for known similarities. As we see above, cognitive solutions have been built to do this for diverse challenges such as matching people to prospective life partners, determining who in social media is most influential in affecting a brand’s image, and helping athletes avoid head injuries.

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