Cognitive computing is a field of artificial intelligence (AI) that aims to create computer systems capable of mimicking human thought processes and cognitive abilities. Unlike traditional computing, which follows a predefined set of instructions and rules, cognitive computing systems are designed to learn from and adapt to data and patterns without explicit programming.
Cognitive computing will give a rise to personal cognitive assistants for students, teachers, and support staff. It can make adjustments to a new campus so much smoother and act like a kind of companion, explaining the directions to a new campus and answering the questions that might come up. The main aim of cognitive computing is to simulate human thought processes in a computerized model. By using the self-learning algorithms to use data mining, pattern recognition, and natural language processing. The phrase is closely associated with IBM’s cognitive computer system. Computers are faster than humans at processing and calculating but they have some tasks such as understanding natural language and recognizing objects in an image. It is an attempt to have computers mimic the way a human brain works.
Types of cognitive computing
Deep Learning Systems – It may mimic the human brain’s ability to learn by using finding patterns and looking at previous examples. It can perform human tasks like identifying the image or recognizing speech. It can learn in an unsupervised manner from unlabelled unstructured data and is already used in driverless cars and voice control technology in smartphones, tablets, IoT devices, etc.
Machine Learning Systems – It is the process of teaching a system to learn without human intervention. This kind of system does need to be trained against initial data but can learn beyond it. They are capable of classification, prediction, and decision-making and already see myriad uses in a variety of industries like healthcare, retail, manufacturing, etc.
Neural Networks – It is inspired by biological networks mainly found in the human brain, an artificial neural network is a computing system that interprets data to categorize and find patterns and identify relationships. They can group unlabelled data based on similar characteristics, making neural networks highly useful for organizing massive amounts of data and already being widely used in several different ways, from filtering emails to diagnosing cancer.
Artificial Intelligence – It is difficult to define and it encompasses a variety of different concepts, processes, and practices. It is a computer system that can perform tasks that typically require human intelligence. Research areas have focused on tasks like natural language processing and automated reasoning. People have discovered uses for artificial intelligence in seemingly every field including transportation, education, agriculture, government, and media.
Key Characteristics of Cognitive Computing
- Natural Language Processing (NLP): Cognitive systems can understand, interpret, and generate human language, making them capable of engaging in natural language conversations and text analysis.
- Machine Learning: These systems employ machine learning algorithms to process and analyze data, recognizing patterns, and making predictions based on historical data and experience.
- Reasoning and Problem Solving: Cognitive computing systems can perform complex reasoning tasks, draw conclusions, and make informed decisions, often in ambiguous or uncertain situations.
- Adaptability: They continuously learn and improve their performance over time, refining their understanding and decision-making capabilities with additional data and experiences.
- Context Awareness: Cognitive systems can understand and take into account the context in which information is presented, considering factors like user location, time, and preferences.
- Emulating Human Senses: Some cognitive systems incorporate elements that simulate human senses, such as computer vision for visual recognition or speech recognition for auditory input.
- Interactivity: They are designed for interaction with users and can respond to queries, provide recommendations, and engage in meaningful conversations.
The systems used in the cognitive sciences combine data from various sources while weighing context and conflicting evidence to suggest the best possible answers. To achieve these cognitive systems include sold learning technologies use data mining, pattern recognition, and NLP to mimic human intelligence. A computer can solve these types of problems by analyzing vast amounts of structured and unstructured data fed to the algorithms in machine learning. Cognitive systems can refine the way they process the data. They become capable of anticipating new problems and modeling possible solutions. With more data exposed, a system can learn and becomes more accurate over time.
To achieve these capabilities the systems must have the following attributes
Adaptive: These cognitive systems must be flexible enough to learn as information changes and as goals evolve. They must digest the dynamic data in real-time and adjust to the data and the environment change.
Interactive: Human-computer interaction is a critical component of cognitive systems. Users must be able to interact with the cognitive machines and define their needs as those needs change. The technologies must also be able to interact with other processors, devices, and cloud platforms.
Iterative and stateful: These cognitive computing technologies can ask questions and pull in additional data to identify or clarify a problem. They must be stateful in that they keep information about similar situations that have previously occurred.
Contextual: Understanding context is critical in the thought processes. The systems must understand and identify my contextual data such as syntax, time, location, domain, requirements, and a user’s profile tasks and goals. These systems may draw multiple sources of information including structured and instructed data and visual auditory and sensor data.
Advantages of cognitive computing
- It helps in the improvement of customer engagement and service.
- It helps in enhancing employee productivity and quality of service/product outcomes.
- It helps in providing very accurate data analysis.
- Hence cognitive systems are employed in the healthcare industry.
Disadvantages of cognitive computing
- Security is a major concern as digital devices manage critical information in cognitive computing.
- The cognitive computing-based systems/products require lengthy development cycles.
- Change management is another challenge as this technology has the power to learn like humans and behave like natural humans.
- Hence people are fearful as they feel machines would replace humans in the future.
Applications of Cognitive Computing
Though it is relatively new, cognitive computing already has many applications in various industries.
Cognitive computing is often used in applications that require complex data analysis, natural language understanding, and decision-making. It finds application in various fields, including:
- Healthcare: Cognitive systems assist in medical diagnoses, drug discovery, and patient care by analyzing medical data, images, and research papers.
- Finance: They are used for fraud detection, portfolio management, and customer service through chatbots and virtual assistants.
- Customer Support: Cognitive chatbots and virtual agents provide personalized and efficient customer support by understanding and responding to customer inquiries.
- Education: Cognitive computing enhances personalized learning experiences, adapts educational content, and supports educators in their teaching efforts.
- Content Recommendation: Cognitive systems power content recommendation engines, suggesting products, articles, videos, and more based on user preferences.
- Language Translation: They facilitate real-time language translation, breaking down language barriers in global communication.
- Legal Services: Cognitive systems assist legal professionals by analyzing vast amounts of legal documents and providing legal research support.
IBM’s Watson is one of the most well-known examples of a cognitive computing system. It gained recognition by winning the game show “Jeopardy!” in 2011, showcasing its natural language processing and knowledge retrieval capabilities. Since then, Watson has been applied in various industries, including healthcare, finance, and customer support.
Cognitive computing continues to evolve, with ongoing research and development aimed at making these systems more capable, adaptable, and human-like in their interactions and problem-solving abilities.
Its most noteworthy uses include:
Chatbots: These programs can simulate human conversations and communicate through text with users in real-time. They can answer questions and are commonly used on e-commerce websites to assist customers.
Face Detection: Cognitive computing can learn to distinguish images of peoples’ faces from each other and then identify them. This can be useful in security systems, such as unlocking a mobile phone.
Fraud Identification: Cognitive computing has a promising future in finances and banking when it comes to detecting strange and fraudulent transactions. This can help prevent fraudsters from taking money that isn’t theirs and protect consumers who are taken advantage of.
Healthcare: Physicians can use cognitive systems to comb through medical records and patient data to find treatment options. Depending on the system, they may even be able to interact with it or ask questions about patient care.
Health and Wellness: Wearable items like smartwatches and fitness apps often make use of cognitive computing to organize and analyze a user’s health data. These health technology can then make recommendations about diet, exercise, and sleep to help users improve their health.
Travel: Cognitive systems can be used on travel websites and apps to help customers find better arrangements based on their budget and preferences. It can aggregate information, like flight times and hotel room availability, scan that information, and match it up with customers’ specifications to make the planning process easier and more satisfying.
In conclusion, cognitive computing represents a remarkable advancement in the field of artificial intelligence (AI) and computing. It seeks to bridge the gap between traditional computing and human-like cognitive abilities, enabling machines to understand, reason, and interact with data and users in a more natural and intelligent manner.
Key takeaways about cognitive computing include:
- Mimicking Human Intelligence: Cognitive computing aims to replicate human cognitive processes such as natural language understanding, learning, reasoning, and problem-solving. It strives to create systems that can think and adapt like humans.
- Learning and Adaptation: These systems leverage machine learning algorithms to continuously learn from data and experiences, improving their performance and decision-making capabilities over time.
- Broad Applications: Cognitive computing finds applications across various industries, including healthcare, finance, customer support, education, and content recommendation. It addresses complex problems that require data analysis, language understanding, and contextual awareness.
- Enhanced User Interaction: Cognitive systems enable more natural and interactive interactions between humans and computers. They power chatbots, virtual assistants, and recommendation engines that enhance user experiences.
- Context Awareness: Cognitive systems understand and consider contextual information, such as user preferences, location, and timing, in their interactions and decision-making processes.
- Real-World Impact: These systems have made significant contributions to fields like healthcare by assisting in medical diagnoses and drug discovery, finance through fraud detection and investment strategies, and customer support by providing efficient and personalized assistance.
- Ongoing Development: Cognitive computing is a dynamic field with continuous research and development efforts aimed at improving the capabilities, adaptability, and ethical considerations of these systems.
As cognitive computing technology evolves, it holds the promise of addressing increasingly complex challenges and augmenting human capabilities in decision-making and problem-solving. While it has already made substantial strides, the future of cognitive computing is likely to be marked by even more sophisticated, context-aware, and interactive systems that empower individuals and organizations to make better-informed decisions and achieve greater efficiencies in various domains.