Using Cognitive Computing computer systems to solve the types of problems that humans are typically tasked with requires vast amounts of structured and unstructured data, fed to machine learning algorithms. Over time, cognitive systems are able to refine the way they identify patterns and the way they process data to become capable of anticipating new problems and model possible solutions.
Cognitive computing systems can synthesize data from various information sources while weighing context and conflicting evidence to suggest the best possible answers. To achieve this, cognitive systems include self-learning technologies that use data mining, pattern recognition and natural language processing (NLP) to mimic the way the human brain works.
Cognitive computing is often used interchangeably with AI, the umbrella term for technologies that rely on data to make decisions. But there are nuances between the two terms, which can be found within their purposes and applications.
AI technologies include but aren’t limited to machine learning, neural networks, NLP and deep learning. With AI systems, data is fed into the algorithm over a long period of time so that the systems learn variables and can predict outcomes. Applications based on AI include intelligent assistants, such as Amazon’s Alexa or Apple’s Siri, and driverless cars are based on AI.
Cognitive computing systems redefine the nature of the relationship between people and their increasingly pervasive digital environment. They may play the role of assistant or coach for the user, and they may act virtually autonomously in many problem-solving situations. The boundaries of the processes and domains these systems will affect are still elastic and emergent.
Their output may be prescriptive, suggestive, instructive, or simply entertaining. The cognitive computing process uses a blend of artificial intelligence, neural networks, machine learning, natural language processing, sentiment analysis and contextual awareness to solve day-to-day problems just like humans. IBM defines cognitive computing as an advanced system that learns at scale, reason with purpose and interacts with humans in a natural form.
The personal digital assistants we have on our phones and computers now (Siri and Google among others) are not true cognitive systems; they have a pre-programmed set of responses and can only respond to a preset number of requests. But the time is coming in the near future when we will be able to address our phones, our computers, our cars, or our smart houses and get a real, thoughtful response rather than a pre-programmed one.
In terms of Education and Learning, It stands out among many application areas of cognitive computing due to their practical appeal as well as its research challenge. One can easily gauge the level of research challenge once we recognize the broad spectrum of human learning, the complex and not fully understood human learning process, and various influencing factors of learning, such as pedagogy, technology, and social elements.
Cognitive computing or cognitive assistants will support many of the day-to-day routine tasks or jobs that students need to do as they progress with their studies. For instance, the cognitive assistant will support students as they make routine appointments with members of staff, help manage assignment deadline dates and the assistant will help the student to manage and schedule payments to support their studies. The cognitive assistant can also provide information to students about the services that are available around the campus to support their applications for further study or when applying for university. The cognitive assistant is context-aware which means that the service is aware of the student’s place in the student life cycle and it knows what to offer the student at each point in the student life cycle.
Imagine this scenario. A student asks the cognitive assistant, “I want to go to university and I need 450 UCAS points to get accepted by a well-known university. What grades do I need to achieve on my remaining assignments to achieve the required number of points to get to university?” Even before the student has asked the question, the cognitive assistant may provide the answer. For example, the cognitive assistant will know that the student needs 450 UCAS points to get his or her chosen university and will advise the student on the grades to be obtained when the assignments are posted on the learning management system of the institution by the teachers on the course. The point is that the technology will ease the burden and workload of teachers as they can refer the query to their cognitive assistant. The technology helps students to find out which course to pursue after completing the current course or any other information regarding current or further studies. It can also act as a personal tutor, guiding students through their course work, explaining problematic sections.
In the Health Care Sector too, Artificial intelligence (AI) and cognitive technologies have gained significant traction and have been more widely adopted in recent years. Increased adoption of cognitive and AI platforms can be attributed to their wider application potential across the health care industry, including patients and hospitals. It uses data mining, pattern recognition, and natural language processing, to imitate the way the human brain works and thinks. The more data is fed, the better understanding it has to make a decision.
Technological headways in the healthcare industry have empowered doctors and other human services suppliers to better diagnose and treat their patients. The healthcare area has experienced different technological progressions in the course of recent years with EHR usage, voice recognition technology and so forth that have upgraded the work processes in each healthcare unit. Alongside other healthcare entities, medical transcription organizations are additionally outfitting the potential of innovative technology to guarantee auspicious and precise documentation. The focus is currently on improving quality while diminishing documentation time. With trend-setting innovation, the transcription, transport, workflow, delivery and safe storage of clinical records can be completed with no block; it likewise encourages ceaseless workflow.
Cognitive computing is right now being utilized at various leading oncology centres across the country, incorporating Memorial Sloan Kettering in New York City and MD Anderson in Houston. Its utilization with unstructured data (information that is for the most part text-heavy and not organized in a predefined manner, with more than 80% of medical information being characterized thusly), best practice information, published clinical studies, and clinical trial data permit it to look at boundless amounts of data in assisting to make diagnosis and treatment decisions on these patients. It has become a significant clinical help device for clinicians in their decision making.
Cognitive computing falls under the category of artificial intelligence, but it is a separate sub-category of this broader umbrella. In a nutshell, the term describes a computer that can “think” like the human brain. Using deep learning, natural language processing, interactivity, and context, supercomputers with cognitive systems can learn and adapt, solving complex problems that computers have historically been incapable of processing. IBM is the leader in cognitive computing with its Watson technology, and the company is working hard to make cognitive computing accessible to organizations in a variety of industries. This technology has massive potential in many different industries, but IBM has been putting the focus on the health-care industry since there are so many areas for improvement.
Let us also have a look at the application of cognitive computing in day to day life:
Chatbots are programs that can simulate a human conversation by understanding communication in a contextual sense. To make this possible a machine learning technique called natural language processing is used. Natural language processing allows programs to take inputs from humans (voice or text), analyze it and then provide logical answers.
Sentiment analysis is the science of understanding emotions conveyed in a communication. While it easy for humans to understand tone, intent etc. in a conversation, it is far more complicated for machines. To enable machines to understand human communication you need to feed training data of human conversations and then analyze the accuracy of the analysis.
Face detection is the advanced level of image analysis. A cognitive system uses data like structure, contours, eye colour etc. of the face to differentiate it from others. Once a facial image is generated, it can be used to identify the face from an image or video.
Risk management in financial services involves the analyst going through market trends, historical data etc. to predict the uncertainty involved in an investment. But this is analysis is not only related to data but also to trends, gut feel, behaviour analytics etc. Thus it is both an art and a science. Big data analysis (i.e. analysis of past trends alone) is not sufficient to do a risk assessment.
Fraud detection is another application of cognitive computing in finance. It is basically a type of anomaly detection. The goal of fraud detection is to identify transactions that don’t seem to be normal (anomalies). This also requires programs to analyze past data.
The cognitive process can be comprehended in a much simple way as “the mechanism which uses the existing knowledge for generating new knowledge”. The main theme of cognition is closely related to abstract concepts such as mind, perception, and intelligence. It is like understanding the obligation of a human brain and working on human kinds of issues. Such systems and set-up continually gain knowledge from the data. The cognitive computing system consolidates data from diverse and miscellaneous information sources while considering context and conflicting evidence to suggest the best feasible answers. It is one of the classifications of technologies that uses machine learning and Natural Languages Processing(NLP) to enable people and machines to interact and gain understanding more naturally for magnification of human expertise, perception, and cognition.