Cognitive computing systems have been one of the trendiest aspects of modern day technologies. Deploying computerized models to simulate the human cognition process to find solutions is what cognitive computing systems do. A cognitive computing system is used in complex situations for ambiguous and uncertain outcomes. The term cognitive computing is closely associated with IBM’s cognitive computer system, Watson and overlaps with Artificial Intelligence (AI) using the same technologies to power cognitive applications, like neural networks, expert systems, virtual reality (VR) and robotics.
The Technology Behind Cognitive Computing
Cognitive computing systems can synthesize data from multiple information sources, analyzing the context and conflicting evidence to offer the best-suited solutions. For the best solutions, Cognitive computing systems apply self-learning technologies which use data mining, natural language processing (NLP) and pattern recognition to mimic how the human brain works.
Cognitive systems aggregate vast amounts of structured and unstructured data which are fed into machine learning algorithms for further analysis. With technological upgrades, cognitive systems are poised to refine the way they identify patterns and process data to anticipate new problems and give the best solutions on a case to case basis
To achieve the best solutions, cognitive computing systems must employ five key attributes, as pointed by the Cognitive Computing Consortium.
• Adaptive: Cognitive systems must be flexible to learn and relearn information changes as priorities change. These systems must be adaptive to real-time dynamic data adjustments as business environment change.
• Interactive: Human-computer interaction (HCI) is a critical component that is indispensable to cognitive systems. User interaction with cognitive machines, processors, devices and cloud platforms for requirement gathering must be top notch.
• Iterative: Cognitive computing technologies should be able to perform iteration to the maximum levels. They must identify problems by asking questions or pull additional data if a problem is vague or incomplete by historical analysis about similar situations that have previously occurred.
• Contextual: Understanding context is critical to the business problem, thus cognitive systems must understand, mine and identify contextual data like syntax, domain, location, time requirements, user profile, tasks or end goals. This contextual data may be drawn from multiple sources of information, like visual, auditory structured and unstructured data or sensor data.
Harnessing the Power of Cognitive Performance Computing
Cognitive performance computing has taken the leaders in business, management consulting and government around the globe by storm. As the policymakers analyze and debate how they can leverage cognitive computing for their work, cognitive operations are increasingly being adapted in organizations where there is a constant set of unknowns. Senior officials, trusted advisors are setting the best practice for internal users and clients alike. Regulators are working forward to create the laws requiring cognitive compliance from organizational leaders. The next evolution of cognitive computing answers to fulfilling the organizational goals including helping executives and management consultants work through their risk-reward trade-offs matrix also called as cognitive performance. Organisations focus to enhance their cognitive performance as it leads to improved critical thinking, stakeholder communications, advisory collaboration, decision-making, uncertainty monitoring and cognitive compliance.
Mixed Bag Performance
So far the initial results to leverage the gains from cognitive computing have been mixed. Even Watson sometimes gets into a trouble filtering solutions from often conflicting datasets. Cognitive system scores high as it has the ability to learn-relearn and adapt to changing environments. This leads them to improve their results without manual coding. The path to autonomy is leading to a very real possibility that very soon business systems will be largely managed by autonomous, self-learning platforms.
But the path to this development is a two-way street. As cognitive evolves and change to exponential learning and continuous, self-directed optimization, business enterprises must learn to adapt to a radical change in their working to gain an advantage in blockchain, the IoT and advanced 3D printing technologies.
To successfully navigate this transition businesses and organizations need to adopt changes with a clear mind. As industries go digital there will be an opportunity to create service driven lines or entirely new ones for an increasingly connected world.
Digital Footprint in Cognitive Performance
Many organizations have a complex structure involving multiple teams who are responsible for operating processes and digital transformation. These teams are organizations and business enterprises that spend time to embrace the cognitive performance of their teams and will leapfrog their competitors as they leverage the competition. There is still a long, bright road ahead for reaping the maximum gains from cognitive performance.
Cognitive technology may be referred to sometimes as “thinking” computer, but this is not entirely true and correct. The mysteries of the human thought and consciousness are still unfathomed, as cognitive systems make a sincere attempt to mimic the human intellect through highly advanced algorithms. A definite change has been made as cognitive solutions can outperform the human brain, particularly in processing large, complex datasets. But ultimately, the human brain is the winner for its unique and mysterious thinking and capability to achieve the unconquered.