Artificial intelligence has been a far-flung goal of computing since the conception of the computer, but we may be getting closer than ever with new cognitive computing models. Cognitive computing comes from a mashup of cognitive science – the study of the human brain and how it functions – and computer science, and the results will have far-reaching impacts on our private lives, healthcare, business, and more.
What is Cognitive Computing?
Cognitive computing, a subfield of artificial intelligence, simulates human thought processes in machines using self-learning algorithms through data mining, pattern recognition, and natural language processing. These artificial environments rely on deep learning algorithms and neural networks to process information by comparing it to a teaching set of data. By mimicking human thought processes, computers help people make better and easier decisions. Given these machine-human interactions, cognitive computing can also be described as Augmented Intelligence.
How does it work?
The way cognitive computing works differs greatly from other, related forms of technology and computational sciences. Typically, other types of systems are deterministic or prescriptive; humans program these systems to learn and behave in a certain way. Cognitive systems, on the other hand, are designed to learn, reason, and behave as humans do. They are adaptive and able to respond to new information; interact with data, other systems, and humans; and understand contextual elements and clues to make hypotheses, recommendations, and decisions.
However, because these systems are so different and complex, developers face unique challenges in creating and refining cognitive systems. Researchers have identified several major hurdles in deploying cognitive computing, particularly when it comes to integrating software-based cognitive systems with the appropriate computer hardware and cultivating the connection between feeling, knowing, and thinking within systems. As cognitive systems become more prevalent and assimilated in peoples’ lives and work, researchers will have to determine how to overcome these hurdles and explore new facets of what these systems can do.
What are the business benefits of Cognitive Computing?
- Improved data collection and interpretation: Cognitive computing applications analyze patterns and apply machine learning to replicate human capabilities such as deduction, learning, perception, and reasoning. Both structured and unstructured data can be collected from diverse sources, and in-depth cognitive analytics are applied to interpret the data. That information can then be used to improve visibility into internal processes, how your products and services are being received, what your customers’ preferences are, and how best to build their loyalty.
- Troubleshooting and error detection: By applying cognitive concepts to a robust technological environment, cognitive computing can help you more quickly and accurately identify issues in business processes and uncover opportunities for solutions.
- More informed decision-making: Through its data collection and analysis capabilities, cognitive computing allows for more informed, strategic decision-making and business intelligence. This can lead to more efficient business processes, smarter financial decisions, and overall improved efficiency and cost savings.
- Improved customer retention: Cognitive computing sets the stage for a more helpful, informed customer-to-technology experience, improving customer interactions. Its ability to interact with and understand and learn from humans greatly improves overall customer retention and satisfaction.
Disadvantages of Cognitive Computing
Cognitive Computing also has downsides, including the following:
- Security challenges. Cognitive systems need large amounts of data to learn from. Organizations using the systems must properly protect that data – especially if it is health, customer, or any type of personal data.
- Long development cycle length. These systems require skilled development teams and a considerable amount of time to develop software for them. The systems themselves need extensive and detailed training with large data sets to understand given tasks and processes.
- Slow adoption. The slow development lifecycle is one reason for slow adoption rates. Smaller organizations may have more difficulty implementing cognitive systems and therefore avoid them.
- Negative environmental impact. The process of training cognitive systems and neural networks consumes a lot of power and has a sizable carbon footprint.
There’s no question that cognitive computing will transform the world in dramatic ways in the coming years. The possibilities and applications are immense, from improving the customer experience to making manufacturing processes more efficient to make our streets safer. Harnessing the power of cognitive computing can mean huge steps forward for businesses, schools, the environment, and the quality of life for everyone.