Artificial intelligence (AI) has been the most far-flung goal of mankind since the birth of the computer. However, we can certainly say that we are closer to that goal than ever with the advent of new cognitive computing models.
In a layman’s terms, cognitive computing is a mashup of cognitive science and computing science, where cognitive science studies the human brain and how it works and computing science deals with the innovative ways of using computers for the betterment of the community.
What is cognitive computing?
Cognitive computing systems are used to find solutions to complex situations where answers are uncertain or ambiguous, using computerised models that simulate the human cognition process. Although the term is often used alongside AI, it is closely related to Watson, IBM’s cognitive computer system. Cognitive computing intersects with AI and includes several fundamental technologies to influence cognitive applications such as virtual reality (VR), neural network, and expert systems.
The prime goal of cognitive computing is simulating human thought processes in computerised models using self-learning algorithms that use data mining, natural language processing, and pattern recognition. This way, the computer could mimic the way human brain works. Even though computers are way faster at calculations than humans, it has not been able to grasp several tasks that we do seamlessly such as natural language and recognising unique objects in an image. Thus, cognitive computing is another try at teaching computers to be more humane.
How does it work?
Cognitive computing models synthesise data from different information sources while considering context and conflicting evidence to find the best suitable answers. For this, cognitive systems including self-learning technologies that use pattern recognition, data mining, and natural language processing. Moreover, computers are then used to solve the types of problems that require a huge amount of structured and unstructured data. Over time, cognitive modules refine the way computers recognise patterns and figure out how to process data to anticipate probable problems and structure possible solutions accordingly. There are five vital attributes to achieve these capabilities, including adaptive, interacting, iterative & stateful, and contextual.
Adaptive: Cognitive computing models must be flexible to learn as the information changes and the goals evolve. In addition, it must be able to comprehend dynamic data in real time and make necessary adjustments accordingly.
Interactive: Human to computer interaction is the most vital aspect to develop a cognitive computing model. In fact, users must be able to convey the information to machine and define their goals and the model also must interact with other processors and cloud platforms.
Iterative and stateful: The technology must identify problems by asking questions if the given problem is vague or unclear. Cognitive computing models do this by maintaining every bit of information it has about similar situations that have happened previously.
Contextual: This could be the most crucial aspect as understanding the context is a complicated process and the cognitive systems must identify, understand, mine contextual data by drawing information from multiple sources and data.
Potential of cognitive computing
As cognitive computing systems are based on multiple technologies including natural language processing and machine learning algorithm, enterprises must be equipped with required digital strategy and technological infrastructure to incorporate these systems in the business processes to improve the value proposition of services.
Apart from this, the increase in the number of software-as-a-service based enterprise management solutions would help more market players to automate their enterprise by offering essential technological framework. What’s more, correct SaaS products can offer the required boost for the emerging companies, SMEs as well as big corporations. Cognitive computing systems help enterprises to stay completive and propel higher revenues in today’s tech-savvy world. This innovative technology has offered an effective tool to improve decision making, elevate employee expertise, and acquire customer view.
The rising volume of complex data, increased demand for big data analytics, and the positive impact of cognitive computing on business applications are expected to boost the demand for cognitive computing. According to a research firm, Allied Market Research, the global cognitive computing market is anticipated to reach $13.8 billion by 2020, registering a CAGR of 33.1% during the period 2015–2020.
The potential use cases of cognitive computing are vast and vibrant. From cognitive materials to cognitive engineering systems and from cognitive cars to cognitive flight systems, there are innumerable opportunities. Wherever there are complex problems or circumstances are everchanging, cognitive computing could help simplify the issue. Since cognitive computing offers an intelligence information tool and is incessantly evolving to be more like human, it will certainly increase human capabilities and knowledge. Over time, humanity would work hand in hand with human-like machines to solve complex problems that trouble humanity.