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Perspectives

Cognitive Computing: The 'Human' Soul of a New Machine

2017-05-23


As the impact of cognitive computing grows exponentially, an increasingly symbiotic relationship between people and machines is emerging — one that will forever change business decision-making, productivity and operations.

Endowed with the abilities to sense and comprehend enormous amounts of data, apply reason, extract insights and continuously learn while interacting with people and fellow machines, cognitive computing technologies represent a new stage of human/machine coevolution. These technologies are helping replicate human capabilities across the spectrum of sensory perception, deduction, reasoning, learning and knowledge. In essence, they are augmenting and accelerating human capabilities by mimicking how people learn, think and adapt. Forward-looking enterprises are finding ways to leverage these advancements for competitive advantage in the growing digital economy. 

A Converging Spectrum

Cognitive computing technologies, in theory, replicate the human capabilities of sensory perception, deduction, learning, thinking and decision-making. The ability to harness vast amounts of computing power will take this beyond human replication both in terms of speed and ability to distinguish patterns and provide potential solutions that individuals may not be equipped to perceive. This jump in machine capacity not only promises to augment human potential but will spark and increase individual creativity and create new waves of innovation. The three key areas of converging capability are:   

  • Sensory perception, where machines are enabled to simulate human senses of sight, hearing, smell, touch and taste. Of these senses, the most developed in terms of machine simulation are visual and auditory perception — via computer vision and speech processing, respectively. Advancements in the fields of haptics that simulates the sense of touch, machine olfaction that simulates the sense of smell, and gustatory technology that simulates the sense of taste are expected to take virtual experiences to the next level.

  • Deduction, reasoning and learning, where machines simulate human thinking to make decisions. Machine learning, deep learning and neural networks are the most prominent among these technology disciplines and are already being deployed as systems of intelligence to derive meaning from information and apply judgment to arrive at effective, informed decisions.

  • Data processing, where huge datasets are processed to facilitate accelerated business decisions and provide smarter suggestions. Hyperscale computing, knowledge representation and ontologies, high-performance search and natural language processing (NLP) are the leading technologies here and provide the required processing power to ensure systems of engagement work in real time.

Figure 1

Where Silicon Meets Synapse

Use cases where cognitive computing technologies are making an impact can be viewed through two lenses: which industries are deploying the technologies, and which cognitive technologies are being deployed across industries.

From an industry-specific perspective, banking and financial services companies are using cognitive computing to automate business processes, end-to-end. In healthcare, cognitive applications are helping doctors screen and diagnose patients faster, while allowing companies to broaden their reach across large and dispersed populations. Insurance companies are exploring the utility of chatbots and smart advisors to provide the right suggestions to their customers. That is only a small selection, as such applications are cascading across industries — including retail, manufacturing, and travel and hospitality — and disrupting traditional ways of doing business. 

Among a range of technology-driven scenarios, the following are prominent: 

  • Optical character recognition (OCR) for automated data extraction: OCR technology has been applied across the spectrum of industries specifically to extract information in images and optimize document management processes.  

  • Chatbots, robots and intelligent virtual assistants: From general purpose assistants like Siri, Google Assistant or Amazon Echo, to specialized assistants trained in specific fields, bots are improving customer experiences and building efficiencies at scale.  

  • Video analytics: This technology analyzes streaming video feeds from cameras to provide real-time analysis about user-predefined behaviors of people, vehicles or objects.  

  • Image analytics: This technology includes medical image processing in healthcare but extends far beyond, including the analysis of terrestrial characteristics and the analysis of real-time satellite or drone imagery.  

Taking a Heuristic Approach

Enterprises need to carefully assess how and where to adopt cognitive computing technologies based on their maturity. We recommend the following four-step heuristic approach that will help enterprises not only see immediate ROI but also prepare the business to transform for a completely disrupted future landscape.

Step One: Categorize the technology landscape across a maturity continuum.

Enterprises should look at the broad swath of technologies and categorize them across a maturity continuum. The closest for adoption are those that are highly mature, while those that are in experimental stages are at the far end of the spectrum.

Step Two: Identify immediate opportunities for adoption.

Take a look at immediate opportunities that can showcase ROI and help free resources and funding for longer-term projects such as robotic process automation and infrastructure automation. Apply mature technologies (e.g., visual and audio processing) to these opportunities to more quickly realize value and business success.

Step Three: Pilot and prototype emerging areas.

Using a combination of mature and evolving technologies (e.g., handwritten document analysis), identify use cases to deploy as pilots for business transformation. Prime among these are the development and deployment of virtual assistants — using deep learning for creating learning systems that can scale. 

Step Four: Assess moonshot projects.

Think radically and identify disruptive technologies; the truly disruptive ones are those that are on a three-year trajectory and should involve research exploration. While mature technologies can provide a strong foundation to these big reach projects, what will make them successful is the application by design of embryonic technologies that have not yet fully bloomed.

To find out more about cognitive computing technologies and capabilities, read our white paper, “Cognitive Computing: The Next Stage in Human/Machine Coevolution” or visit the Digital Business section of our website. 

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Cognitive Computing: The 'Human' Soul of a New Machine