What is cognitive computing?

Built on the broad computing frameworks of artificial intelligence (AI) and signal processing, cognitive computing combines a range of machine learning (ML) disciplines with human-computer interaction principles, dialogue and narrative-generation techniques to create machines that can learn, reason and understand like humans. Effective cognitive computing systems can process large amounts of data to identify patterns and relationships beyond human abilities. 

While there are many areas where computers can outperform human beings, even advanced AI systems still struggle with some tasks, such as understanding natural language and recognizing specific objects. Cognitive computing seeks to emulate the cognitive systems of the human brain (for example, pattern recognition, speech recognition, and so on) to improve decision-making. Cognitive computing systems can be designed to use dynamic datasets in real-time and multiple information sources in combination, including sensory inputs like visual, gestural, auditory, or sensor-provided data. 

Some real-world use cases for cognitive computing include sentiment analysis, risk assessment and forms of image recognition, such as facial and object detection. Cognitive computing is of particular value in the fields of robotics, healthcare, banking, finance and retail. 

The overarching goal of cognitive computing is to develop systems capable of solving complex, multistep problems that typically require human cognition. These types of problems typically involve high-level, context-dependent pattern recognition. When it comes to things like interpreting language or images, humans are very good at recognizing the context clues that can inform accurate decision-making. These types of tasks can be a lot more challenging for rules-based computer systems.

Cognitive computers, unlike traditional systems, are developed to analyze large amounts of unstructured data from various sources with the specific goal of generating accurate and valuable insights through more sophisticated pattern recognition. Effective cognitive computing systems can interpret text (in regular and irregular fonts), images, and speech, and they are even able to make connections across disparate types of data. These types of systems are also capable of improving over time, mimicking the way human beings learn. 

Cognitive computing models are most commonly based on artificial neural networks, a type of AI that uses layers of nodes, or artificial neurons, inspired by the neural pathways in the human brain. These types of networks are able to improve over time by effectively learning from each piece of data they are fed to improve their decision-making process.

While neural networks can be powerful for specific types of tasks, cognitive systems will also frequently incorporate other types of AI-driven or AI-adjacent technologies, such as natural language processing (NLP) and machine learning, to better comprehend and interpret various inputs and signals. 

Cognitive computing systems are designed to combine large amounts of data from various types of sources. To analyze and weigh different, and sometimes conflicting, inputs and make informed inferences based on learned context, cognitive systems use various self-learning technologies designed to mimic human intelligence. These methods include predictive analytics, data analysis, big data mining and various pattern recognition models to optimize the decision-making process.

Training the types of machine learning algorithms used in cognitive systems requires vast amounts of both structured and unstructured data. During training, these types of systems begin identifying patterns and, over time, refine their data processing techniques to make faster and more accurate connections. 

For example, an AI system trained to identify different types of flowers can be fed a database storing hundreds of thousands of different pictures of flowers. As the system is presented with more data, its ability to recognize differences and similarities between flower varieties improves, and the more accurate and agile it becomes. 

However, a system trained to identify flowers based only on pictures of flowers might misinterpret certain context clues that pictures aren’t able to convey. In order to achieve cognitive capabilities similar to human decision-making, cognitive computing systems must hybridize various types of technologies and possess certain specific attributes. Namely, to be considered a cognitive computer, a system must have the following attributes.

Cognitive computing systems are created by connecting many different types of computing models into a hybrid system that can better approximate human thought processes and intelligence. These models include various types of artificial intelligence and AI-adjacent or AI-related models, such as:

• Narrow AI: Encompassing all existing forms of AI, artificial narrow intelligence, or weak AI, is the only type of realized AI currently available. While more powerful, theoretical forms of AI have been hypothesized, narrow AI can only be trained to perform single, or narrow, tasks. While narrow AI is limited in scope, it can typically perform specific tasks faster than humans can, with increasing, although not perfect, accuracy. However, narrow AI is not capable of performing outside its programmed task set. Narrow AI is designed to target specific subsets of cognitive abilities. Even apparently advanced AI systems like Apple’s Siri, Amazon’s Alexa, or ChatGPT are considered to be narrow AI.

• Expert systems: Expert systems are designed to function like a narrow AI replacement for highly trained subject matter experts. They are trained on comprehensive datasets containing factual information and distinct rules, paired with an inference engine tuned to apply the rules most accurately. The goal of expert systems is to offer advice or solutions just as a human expert would. These systems can be used to uncover trends and patterns, and they are often used to help businesses predict future events or gain a better understanding of past occurrences. 

• Machine learning: Machine learning (ML) is a branch of AI focused on enabling computer systems to learn the ways that humans do. ML algorithms help computer systems perform tasks autonomously and improve their performance and accuracy over time as they are presented with more data and positive and negative feedback.
• Neural networks: Neural network models are a subset of machine learning models that use reinforcement learning to make decisions. These types of networks use layers to mimic the way biological neurons work together to weigh options and identify phenomena.

• Deep learning: Deep learning is also a subset of machine learning that uses multilayered neural networks called deep networks to approximate the complex decision-making capabilities of the human mind. The primary difference between deep learning and machine learning is the increased layer of complexity in the network architecture. While traditional machine learning models use simple neural networks with one or two computational layers, deep learning models use more—typically hundreds or thousands of layers. 

• Natural language processing (NLP): Natural language processing combines computational linguistics, a rules-based modeling approach to language, with statistical modeling, machine and deep learning to enable computers to understand and respond with natural speech or text. NLP also incorporates the subset disciplines of natural language understanding (NLU) and natural language generation (NGU) to provide a holistic user experience.

• Automatic speech recognition (ASR): Speech recognition, also known as computer speech recognition or speech-to-text, refers to techniques that allow computer programs to process human speech into a written format. Must not be confused with voice recognition, which is a technique that allows computers to identify an individual user’s voice and differentiate it from others.

• Object detection: Object detection, a component of computer vision, uses neural networks to locate and classify objects in images according to semantic categories. Object detection is a useful tool for a wide range of industries and applications, including self-driving autonomous vehicles, visual search and medical imaging.

• Robotics: Cognitive systems often incorporate robotics in application. Robots equipped with cognitive systems can use narrow AI to perform repetitive and routine tasks, from consumer-grade home vacuum cleaners to medical-grade surgical assistants. In agriculture, robotics help cognitive systems engage in tasks like autonomous pruning, moving, thinning, seeding and spraying. 

Recent advancements in AI technologies have had a major impact on cognitive computing applications, from generative AI programs like ChatGPT and Midjourney to self-driving cars and beyond. Some common real-world applications for cognitive computing include several aspects, such as:

Popular virtual AI assistants like Alexa, Siri and Google Assistant rely on cognitive computing to improve their utility through automation and interactivity. Assistants like these use machine learning systems to process natural language and tailor their suggestions to provide better results for individual users.      

Cognitive computing systems have proven to be valuable for many banking and finance applications. Cognitive systems are used to monitor economic conditions like supply chain variables and market trends to predict and model both future opportunities and potential crises.

Cognitive computing systems have proven to be adept at deep data analytics and pattern recognition. These abilities have been put to good use especially in the field of cybersecurity. Here, specialists use cognitive computing to analyze user behavior, such as financial transactions, to flag patterns of potential fraud and risk. 

Cognitive systems have been useful in retail applications. Tech-forward retailers like Amazon and Netflix use cognitive computing to gain deeper insight into users’ purchasing history and provide better product recommendations targeted toward individuals’ personal interests.

Cognitive systems have also been useful in customer service across industries, powering advanced chatbots to serve as virtual agents. These agents provide detailed and informed support at a greater speed and scale than ever before.

Certainly one of the most famous and high-profile cognitive systems, IBM Watson® rose to prominence competing in the popular trivia game show Jeopardy, while the Watson predecessor, Deep Blue, shocked the world when it became the first computer system to beat a world chess champion.

Today’s iteration (IBM watsonx®) and applications are even more impressive. One notable use case is the healthcare industry, where watsonx has aided providers in improving medical diagnoses. Watsonx is capable of accumulating and comprehending some of the most up-to-date research and complicated patient histories, and it has successfully extrapolated suggested treatment plans to bolster patient care.