Artificial intelligence (AI) is the term we use for the multitude of technologies that enable machines to behave like humans – to think and make decisions and, in the future, perhaps to have consciousness and emotions. These technologies include machine learning (ML), deep learning, neural networks, and natural language processing (NLP).
The term AI is often used interchangeably with cognitive computing. Cognitive computing uses the same technologies as AI but with the addition of self-learning programs such as data mining and pattern recognition.
Although AI and cognitive computing have many similarities, they differ significantly in their performance and interaction with humans.
This article will teach you about cognitive computing, its characteristics, advantages, and uses.
What is cognitive computing?
Cognitive computing is a term that defines the use of computerized models to mimic human thought processes in complex situations where the answers are not obvious but can be interpreted in multiple ways.
Cognitive computing aims to simulate the functions of the human brain, and to succeed, it uses various advanced technologies. Along with neural networks, machine learning, deep learning, and natural language processing, cognitive computing also uses expert systems, speech and object recognition, as well as robotics.
Cognitive computing combines these technologies with self-learning algorithms, data analysis, and pattern recognition to create the perfect learning technology to acquire new knowledge more efficiently. This enables him to recognize human speech and faces, understand and analyze human emotions, and assess risks in various situations.
Advanced cognitive computing functions are applied in various spheres of life and business – from financial sectors to retail, healthcare, and culture.
What does cognitive computing replace?
As we mentioned, AI and cognitive computing have similarities and differences. Still, the most important thing to note is that they complement each other by creating a comprehensive system capable of responding to various challenges.
While AI aspires to be equal to or more advanced than human intelligence, cognitive computing makes that task easier. When faced with a particular problem, cognitive computing analyzes all components and a risk assessment, while AI makes the final decision on the action to be taken.
Researchers and experts in AI disagree on categorizing these two fields of computer science – some consider cognitive computing to be a subcategory of AI, and others think that it is just a marketing gimmick because machines are incapable of thinking.
However, cognitive computing can be an excellent replacement term for AI since AI carries a negative connotation because it is often misinterpreted in sci-fi movies and novels.
What is the role of cognitive computing?
We can understand how significant the role of cognitive computing is with the example of medicine. Thanks to the systematic literature review (SLR), cognitive computing systems can process vast amounts of data, analyze and compare them, and draw conclusions that will improve human decisions.
They combine the best of both worlds – the speed and automation of machines with the critical thinking of people, which ultimately results in time savings and a lower error rate (which is often crucial in medicine).
How does cognitive computing work?
Cognitive computing systems use technologies that mimic human intelligence to evaluate large amounts of data and suggest the best solution to a given problem.
Since cognitive computing still lacks human critical thinking and reasoning power, understanding context and conflicting evidence would be impossible without feeding large amounts of structured and unstructured data into machine learning algorithms.
Over time, these systems develop patterns by which they process and differentiate data. They can predict future problems and create conceptual solutions based on existing information.
For example, if thousands of books are stored in a cognitive computing system’s database, it will learn to recognize which set of words belongs to which book over time.
Cognitive computing must possess four essential attributes to store, process, and evaluate data. We talk about these attributes in the next section:
Four attributes of cognitive computing
Cognitive computing systems must be:
- Adaptive. Information changes frequently, and with a constant flow of new data, flexibility is one of the most important features of a cognitive computing system. Systems must adapt to the situation in real time regardless of the circumstances and the latest data.
- Interactive. Cognitive computing systems must be able to communicate both with people and devices (processors, devices, and platforms in the cloud). The interaction of the human factor with the computer system must run smoothly – users must be able to state their requests, which the system executes.
- Iterative and stateful. Cognitive computer systems store previously gathered information, but they can ask many sub-questions about any new or unfamiliar topic to get new data that will help them understand new subjects, situations, or problems.
- Contextual. Understanding context is characteristic of human thought processes. Cognitive systems must understand the syntax, timing, location, intent, requirements, and goals – the context of a given problem. Still, to perform complex tasks, cognitive computing systems must extract context from structured and unstructured data, relying on visual, auditory, and sensory data.
Examples and uses of cognitive computing
Cognitive computing is used in areas where a large amount of data needs to be analyzed to facilitate decision-making. Computer systems perform this work faster than people and speed up business processes, thanks to systematic literature review (SLR).
One of the prime examples of cognitive computing is IBM’s Watson for Oncology, which is “employed” at the Memorial Sloan Kettering Cancer Center in New York. He analyzes the patient’s health documentation and suggests possible treatment options based on the assessment of the condition. In addition to Watson for oncology, Watson Health is another cognitive computing system that facilitates medical and clinical research.
In addition to medicine, applications of cognitive computing include several other industries, such as retail, finance, and logistics.
In retail, cognitive computing collects customer data based on their past behavior so that it can send them personalized messages and improve the customer experience. In finance and banking, it also analyzes customer data and serves as a chatbot that makes it easy to find answers to frequently asked questions. In logistics, it enables the networking of retail and wholesale facilities with warehouses and condition monitoring – in short, it automates tasks and reduces the possibility of errors to a minimum.
What are cognitive computing advantages?
Cognitive computing has several advantages, including the following:
- High-accuracy data analysis. Cognitive computing aims to enable more manageable decision-making in important matters. While people make mistakes, cognitive computing systems are accurate, which is evident in IBM’s Watson for Oncology. This supercomputer analyzes the data from the patient’s records, medical books, and bulletins to decide on the best treatment for the patient.
- Improved efficiency of business processes. Cognitive computing recognizes patterns in business data analysis, speeds up work, and reduces risks.
- Improved interaction and user experience. Cognitive computing can substitute communication with human staff in business processes. Based on cognitive computing technologies, chatbots can provide customers with relevant and helpful information that saves time and improves the customer experience.
- Greater employee productivity. Cognitive computing solves monotonous tasks, such as collecting and analyzing data, allowing employees to express their full potential by devoting themselves to creative studies.
Does cognitive computing have disadvantages?
Unfortunately, cognitive computing comes with its disadvantages that still need to be overcome:
- Security risks. Cognitive computing must access a huge amount of data to express its full potential. This data is often personal and sensitive, making it susceptible to theft, so organizations that use these systems must invest in top-notch software to protect it.
- A long time of development. To develop cognitive computing systems, highly specialized teams of developers must work for an extended period. Also, developing and testing the technology requires a lot of time.
- Insufficient application. Smaller organizations that do not have large development teams may have trouble understanding, adopting, and implementing cognitive computing systems and avoid them.
- They are environmentally harmful. The development and testing of cognitive computing systems require a large energy consumption and significant carbon dioxide emissions, negatively affecting the ecosystem.
Difference Between Cognitive Computing and Artificial Intelligence
As we mentioned in the introduction, the terms artificial intelligence and cognitive computing are often used interchangeably, and although they have some overlap, their meaning is not the same. AI uses various technologies to make decisions, and cognitive computing uses these same (and more) technologies to prepare the data and ease decisions for AI or humans.
AI minimizes the human role by independent decision-making. At the same time, cognitive computing is much less independent – it does not pretend to replace people. It only complements the information people have so that the further development of the situation runs smoothly.
By taking over a large part of tasks, intelligent machines allow humans to devote themselves to commitments where they will be able to express their full creative potential. The benefits people get from artificial intelligence and cognitive computing succeed downsides of these technologies. Timely information, the minimal possibility of error, and decisions based on verified facts can save vast amounts of time, money, and human lives.