Cognitive computing versus AI: Key differences

At the most basic level, AI systems are built to ‘think’ and decide independently. At the same time, cognitive computing is used to simulate more human-like thought processes to inform human decision-making and not replace it. 

For example, think of AI as a tool that can serve a specific purpose. In contrast, a cognitive computer acts more like a digital assistant that helps achieve a broader goal by informing the overall decision-making process.

If an AI is a GPS that can provide the fastest route between A and B, a cognitive computer is more like a travel guide. The AI can reference existing maps and traffic data to provide what it ‘thinks’ is the best route.

However, a cognitive system works with a user to learn their preferences and respond to more context-dependent information. It might highlight interesting sights along the way or choose a more scenic route when the weather is nice and simple efficiency isn't the top priority.

Generally, AI can be thought of as a specialized problem-solving tool. AI systems excel at quickly analyzing large amounts of data to recognize patterns and decide based on predefined rules. Cognitive systems, designed to think more like people, build on AI capabilities, but are better at understanding complex, unstructured data. They learn from interactions and provide explanations and recommendations.

The umbrella term AI is more commonly used to refer to specific types of limited computer models, such as neural networks and large language models (LLMs). In contrast, cognitive computing is better thought of as a hybrid methodology. It combines cognitive science and computer science to create systems that help augment and inform the human decision-making process.

Cognitive systems often use AI technologies like machine learning (ML) or deep learning to improve pattern recognition or speech recognition capabilities. In addition, these types of systems are engineered to process, ingest and respond to large amounts of data in real time. They pull information from a wide array of potential data or input sources, such as visual, gestural or auditory cues.

While the scope of any individual AI model can be limited—causing it to struggle outside its intended range—cognitive computing systems are designed differently. They are well suited for tackling complex problems that involve ambiguity, uncertainty or nonspecific answers.

Put another way, AI—as we know it today—is meant to bridge gaps, providing shortcuts through mundane or challenging tasks. Cognitive computing is more of an attempt to bolster human cognition to make more informed decisions. Cognitive computing combines AI with complementary disciplines like human-computer interaction, dialog and narrative-generation techniques to create machines that can learn, reason and understand like humans. This approach helps users make better decisions.

While some AI models can be remarkably proficient, even beyond human capabilities, even the most advanced AI systems are only designed to perform a narrow range of tasks. Although widely used AI systems might seem to be vastly capable, rules-based instructions constrain them from grasping the flexibility and nuance of human cognition.

In tasks involving context, such as understanding natural language or recognizing specific objects, AI can’t quite replace or replicate human intelligence—at least not yet.

Cognitive computing doesn’t aim to replace human decision-making. Instead, it seeks to mimic the types of cognitive systems responsible for human thought processes in order to improve user decision-making.

While individual systems can have countless unique differences, broadly, the following points highlight some of the main key differences between AI and cognitive computing.

AI systems are great for automating repetitive or difficult tasks.

Cognitive computing is used to enhance and inform human-based decision-making.

AI systems are trained on specific datasets and are good at handling problems with specific answers that can be found. For instance, an AI system could be trained on a customer service handbook to provide answers based on existing employee training.

Cognitive computers are more contextual, drawing from and responding to different types of inputs. For these reasons, AI systems are better at solving problems with specific answers, while cognitive computing is more valuable for addressing open-ended problems and challenges.

AI systems are built to solve problems to the best of their abilities. They can provide solutions quickly, but their output can be limited, inaccurate or not entirely reliable.

Cognitive computers are intended to help humans find better solutions faster. Cognitive systems are not designed to provide finalized results or complete tasks independently. As such, a cognitive system can help a user find a better solution than the one an AI system might simply provide right away. 

AI systems are limited by the scope of their training data. For this reason, AI systems can be built to be highly specialized, but that specialization comes at the cost of flexibility.

Cognitive systems are much more adaptable. Designed to pull from a wider range of variable inputs, cognitive computers can respond to dynamic situations better. 

Artificial intelligence is technology that enables computers and machines to exhibit characteristics similar to that of human intelligence. These characteristics include learning and retaining new information, comprehension, problem solving, decision making, creativity and autonomy.

As a field of study dating back to the 1950s, AI can be seen as a series of nested concepts that have evolved over time. Over the last 70 years, it has progressed from theoretical models to machine learning, then deep learning and now to generative AI (gen AI).

Recent technology advancements have catapulted AI into the global spotlight. Many exciting AI applications—ranging from industrial supply chain optimization to consumer-level gen AI art generators and chatbots—have captured the imaginations of investors and hobbyists alike. While the potential impact of AI is hard to overstate, in current iterations, AI can still struggle with certain tasks. 

AI systems require enormous amounts of training data to learn about a specific topic. These vast datasets are fed to the AI, which uses pattern recognition to make connections and generate insights.

Upon receiving a problem, an AI system references what it’s learned from the training data and provide the best possible answer based on probabilities. In this way, depending on the quality of the training data and algorithms, an AI can be more or less capable, or more or less restricted.

While modern AI capabilities often can appear expansive, AI is best deployed for more narrow tasks—specialized models tuned for specific purposes. Cognitive systems are likewise tuned for specific purposes, although these types of systems can combine multiple types of AI to be flexible and responsive. 

Some of the various types of AI and  AI-adjacent or AI-related models used in cognitive computing include:

• Narrow AI:  The most advanced AI today—narrow AI, or weak AI—is highly effective at solving well-defined problems with clear, specific goals. Smart assistants like Apple’s Siri, Amazon’s Alexa and even ChatGPT are all narrow AI.

• Machine learning (ML): Machine learning is a subcategory of AI that enables digital systems to learn from data in a way that mimics human learning. Using ML algorithms, AI systems are capable of autonomously performing tasks with improved accuracy over time. Machine learning helps computers learn from their mistakes and improve their output based on user feedback.

• Neural networks: Neural networks use layers of nodes combined with machine learning methodologies to simulate the way the human brain processes information through neurons. Neural networks improve AI capabilities to problem solve and weigh better or worse solutions contextually.

• Deep learning: Deep learning takes neural networks even farther by increasing layer density. A simple neural network has one or two layers. In contrast, a deep neural network can have three to thousands of layers, allowing it to model complex decision-making processes similar to the functions of the human brain.

Cognitive computing technologies are sometimes referred to as a type of AI, though it is more accurate to say that cognitive systems often incorporate various types of AI. Cognitive computing combines machine learning AI systems with other common cognitive computing technologies, such as different types of user interfaces (for example, speech, text) and robotics.

Cognitive systems enhance AI capabilities by ingesting large datasets. These datasets can be structured or unstructured data and come from various different sources.

These types of self-learning systems use data science to process input in real-time, weighing contextual information to help users come to a final decision. In this way, humans can allow the cognitive system to handle the heavy data mining and data analysis and make data-driven decisions without the need to master the complex data science themselves.

Real-world use cases for cognitive computing include general tasks like sentiment analysis, risk assessment and optimization. 

While the exact parameters for a cognitive computing system are not strictly defined for a system to be considered cognitive, it must meet certain criteria. A cognitive computing system must be:

• Adaptive: Cognitive systems must be able to flex and respond to dynamic, new and shifting data. As information shifts, they must also be able to adapt to evolving mission goals and objectives. 

• Interactive: While an AI system can function autonomously, cognitive systems are built to be responsive. As such, they must be able to interact with and respond to both user and signal input. 

• Iterative and stateful: In order to simulate cognition, these types of systems must be iterative. They use pattern recognition to identify unique problems or classes of problems, asking clarifying questions when appropriate.

• Contextual: While AI systems can thrive when solving simple, well-defined problems, real-world scenarios are often context-dependent. The correct solution for a problem in one specific context does not necessarily apply in others. Cognitive computing systems are developed for challenges that require context-dependent problem solving. These types of systems must be able to understand not only the context of the presented information, but also the context in which a solution might be put into application. 

Cognitive computing works by adding several AI or AI-adjacent solutions to a foundation neural or deep network. To achieve adaptability, interactivity, statefulness and contextual comprehension, cognitive systems are built to combine machine learning algorithms with various other technologies, such as:

• Expert systems: Expert systems are narrow AIs that have been exhaustively trained in specific areas or subjects. The goal of an expert system is to create an artificial subject-matter expert as capable as possible to stand in for a human specialist. Expert systems can offer advice or guidance to help humans understand complex problems and make smarter decisions. These systems are often used in business to predict market trends or understand past events. 

• Natural language processing (NLP): Natural language processing enables computers to understand and respond with natural speech or text by combining computational linguistics, a rules-based modeling approach to language, with statistical modeling. 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, enables computers to process human speech into written text.

• Voice recognition: Not to be confused with ASR, voice recognition enables computer systems to recognize distinct voices and differentiate between different users, a common feature found in virtual assistants and other Internet of Things (IoT) devices. 

• Object detection: A main component of computer vision, object detection is a method for using neural networks to locate and classify objects in images according to semantic categories. Object detection and image recognition are useful for self-driving autonomous vehicles, visual search and making medical diagnoses.

• Robotics: Cognitive computing systems often incorporate robotics as a means of performing simple and repetitive tasks, such as agricultural pruning, seeding and spraying. While simple AI systems can enable robots to perform basic tasks like these, in healthcare, cognitive systems paired with robotics can even assist in delicate surgeries.