Today, I feel it’s safe to say we have arrived at the point in time when simply designing machines to fulfill our tasks and needs is not enough.
This is the time when cognitive computing steps in to facilitate and back these daily interactions. The demand for cognitive computing services has been projected to skyrocket. According to Grand View Research, the global market is expected to reach $49.36 billion by 2025.
These theoretical predictions are not the only proof of the success that cognitive computing promises. According to a survey of early adopters conducted by IBM Cognitive, companies that have embraced the capabilities of cognitive computing have already noticed considerable return on their investment: 65% of respondents said the technology became crucial to the overall success of the business; 58% said it was pivotal in the company-wide digital transformation; and 58% said it would remain an important competitive advantage in the future.
At its core, cognitive computing implies the use of computer models and algorithms to replicate how humans think and reason in complex situations where the answers aren’t simple and straightforward. Cognitive computing systems combine information from a wide variety of sources and evaluate the context in order to find their way to the best answers possible. In this process of data synthesis and evaluation, the systems rely on data mining, pattern recognition and natural language processing to simulate the thought process of a human brain.
With the systems’ extensive reliance on structured and unstructured data, the technology requires large amounts of such information to tackle more complex problems usually left to human judgment and reasoning. The data is fed to the machine learning algorithms that identify patterns and gradually learn to predict problems and design solutions in a faster, more efficient manner.
As a next-generation solution that’s already a reality, cognitive computing stands out with a number of attributes that make it a viable investment for businesses undergoing a digital transformation and seeking more growth, engagement and productivity.
Cognitive systems are at their core, highly adaptive, quickly adjusting to the digital transformation strategy of the company as it evolves and changes over time. This flexibility and agility also ensure that the systems are able to read and process information in real-time and are immune to the adjustments and changes to the data as it is fed into the machine.
A critical component of cognitive computing is human-computer interaction. As the needs of the users evolve and change, the system adjusts to these changes, constantly preserving the ability to recognize and meet those needs in the best possible way – something that the inherent agility of the system makes possible.
A similarity between human reasoning and cognitive computing is how cognitive systems – just like human brains – are able to quickly identify uncertainty and incompleteness, thus asking the right questions or engaging more data to find optimal solutions. One method to approach this is to survey similar situations that have enough information on how to resolve a similar problem. The pattern identification abilities of cognitive computing make it possible for the technology to work its way through vague problems and find definitive solutions through data surveying, pattern recognition and asking the right questions when needed.
Finally, what makes cognitive computing so unique is its ability to understand the context in which the problem occurs. Cognitive systems are able to understand and identify such contextual information as syntax, time, location, domain, requirements, a user’s profile, tasks or goals. The sources for this type of information are both structured and unstructured data, ranging from visual to sensory and even audio.
One important question that usually arises is in regards to the major overlap between cognitive computing and artificial intelligence. While both, indeed, share a large number of capabilities – and AI is the larger umbrella term for such smart technology – there is a key distinction between the two advanced systems. And it’s in the purpose and goals of the technology.
Cognitive computing and AI both include many of the same underlying technologies, such as expert systems, neural networks, robotics and virtual reality (VR). AI technologies largely imply the combination of such technology as machine learning, neural networks, NLP and deep learning. Some of the more prevalent applications of AI technology are intelligent assistants – like Alexa, Siri or Google Assistant – and autonomous vehicles. Typically, AI is trained on data over a period of time to teach the system the ability to learn certain variables and eventually predict outcomes.
When it comes to cognitive computing, the term usually refers to AI solutions that replicate the human thought process. Just like the human brain analyses the environment and understands the larger context it operates in, cognitive computing approaches the task of solving problems in the same way. The types of technology that make this simulation possible are machine learning, deep learning, sentiment analysis, neural networks and NLP.
To put it into the context of business applications, the goal of an AI system is to automate internal processes and reach maximum efficiency and productivity within the organization. In contrast, cognitive systems are tools designed to help humans – such as employees or company leaders or even customers – make better-informed decisions. A perfect example of the support that cognitive systems offer to human professionals is IBM Watson for Oncology. Used by professionals focusing on cancer treatments, the technology has made it possible to augment the medical professionals’ experience in identifying the best solutions for patients by generating a wide range of hypotheses and suggesting various treatments for the doctors to discuss and prescribe.
Cognitive computing is a subcategory of AI that comes closest to mimicking human cognition – and in that, it opens numerous doors for companies to improve customer engagement, internal efficiency and maximize growth. The technology’s intent is, indeed, slightly different from the traditional promise of AI technology – but in that difference, cognitive computing puts emphasis on augmenting human capabilities and helping professionals perform better, rather than acting as a replacement for human skills.
Originally published in Forbes