What is cognitive computing?
Cognitive computing is the technique of simulating human thought processes in complicated scenarios, where the answers might be vague. It does so by using computerized models.
The computerized models aim to imitate the functioning of the human brain by making use of self-learning algorithms that employ data mining, pattern recognition, and natural language processing.
Essentially, cognitive computing involves understanding and simulating human reasoning and human behavior.
Cognitive computing depends on deep learning algorithms and neural networks to compare data to the training set of data, and then process it. As the cognitive computing system is exposed to more data, it becomes increasingly accurate, making the system more capable of handling complex decision-making processes.
However, cognitive computing focuses more on decision support than decision making. It aims to help human decision-makers come to better, more informed decisions rather than to completely eliminate human decision-making.
What is the purpose of cognitive computing?
The purpose of cognitive computing is essentially to simulate human thought processes in a computerized model. This branch of computing seeks to mimic human thought processes in complex situations in which the answers could be vague and uncertain. Cognitive computing works with artificial intelligence and incorporates a lot of the underlying technologies that are used to make cognitive applications like expert systems, neural networks, robotics, and virtual reality (VR) work.
It involves making use of self-learning algorithms that employ data mining, pattern recognition and natural language processing to imitate the manner in which the human brain functions.
What does cognitive computing include?
Cognitive computing includes technology platforms that combine machine learning, reasoning, natural language processing, speech, vision, human computer interaction (HCI), to simulate the human brain’s functioning and aid in improving human decision making.
Applications that use cognitive computing link data analysis and adaptive page displays for the purpose of adjusting content to suit a specific type of audience.
How does cognitive computing work?
Cognitive computing empowers computers to mimic the manner in which the mind works. The systems synthesize data from multiple sources and weigh context and conflicting information to derive the most appropriate answer. They use self-learning algorithms based on data mining, pattern recognition, and natural language processing to do this.
To do this effectively, they need to possess a set of key attributes listed by the Cognitive Computing Consortium. If any of these attributes are absent in a system, it cannot achieve cognitive computing.
If you want to use computers to solve problems that humans are generally required to handle, you’ll need huge amounts of structured as well as unstructured data to be fed into machine learning algorithms. Gradually, these cognitive systems would be able to improve the manner in which they detect patterns and the way they analyze data, thus improving the way in which they anticipate new problems and model new solutions to these problems.
To do this effectively, they need to possess a set of key attributes listed by the Cognitive Computing Consortium. If any of these attributes are absent in a system, it cannot achieve cognitive computing.
What are the 4 key attributes of cognitive computing systems?
The Cognitive Computing Consortium lists the key attributes that cognitive computing systems are required to have. These cognitive computing systems must be:
1. Adaptive
They must be able to process data in real-time, and learn & adapt constantly as information, goals, and the environment change. Cognitive computing systems need to be very flexible in this regard.
2. Interactive
Human-Computer interaction (HCI) is considered to be vital in cognitive computing. It must be possible for users to interact with the computers, set parameters, and define their needs.
The systems also need to interact with processors, cloud platforms, and other devices.
3. Iterative and stateful
Cognitive computing systems must be able to identify problems by asking questions. If they deduce that a problem is vague or incomplete, they can pull in supplementary information. Cognitive computing systems can do this by storing information about similar situations that have occurred in the past.
4. Contextual
The systems need to be able to understand, identify, and mine contextual data like syntax, domain, time, location, a specific user's profile, tasks, goals, etc. They can rely on various sources of data including sensor data, auditory data, visual data, etc. They can use structured as well as unstructured data.
Is cognitive computing the same as AI?
Laypeople tend to use the terms ‘cognitive computing’ and ‘artificial intelligence’ interchangeably. But they are not the same. There are differences that can be seen in the purposes and applications of cognitive computing and artificial intelligence.
Cognitive computing vs. AI:
Artificial intelligence aims to find new solutions to problems that could be more effective than the manner in which humans solve those problems. Cognitive computing, on the other, seeks to imitate the manner in which humans would solve those problems.
Artificial intelligence looks to make decisions on its own, without human intervention, while cognitive computing does not try to make decisions for humans; it presents humans with information so that they can make the best decisions possible.
While artificial intelligence seeks to automate processes that would otherwise be carried out by humans, cognitive computing aims to augment human capabilities. Artificial intelligence tries to identify patterns in big data that it can learn and can later either uncover hidden information or create solutions to particularly complicated problems. Cognitive computing focuses on trying to help humans find solutions to rather complicated problems.
However, both, artificial intelligence (AI) and cognitive computing are powered by similar technologies. They both make use of deep learning, neural networks, natural language processing, machine learning, etc.
What is the role of cognitive computing?
Cognitive computing is used widely in analysis-heavy industries like healthcare, finance, marketing, etc.
It has even been used to help in the treatment of disease. Cognitive computing has even been used at Memorial Sloan Kettering Cancer Center to share evidence-based cancer treatment options with oncologists.
Cognitive computing is mainly used to help humans in their decision making process.
