What is Cognitive Computing?
Cognitive computing refers to technology platforms that, broadly speaking, are based on the scientific disciplines of artificial intelligence and signal processing. These platforms encompass machine learning, reasoning, natural language processing, speech recognition and vision (object recognition), human–computer interaction, dialog and narrative generation, among other technologies.
At its core, cognitive computing aims to simulate human thought processes in a computerized model. Unlike conventional AI systems designed for specific, often narrow tasks, cognitive systems are built to learn at scale, reason with purpose, and interact with humans naturally. They are designed to solve complex problems that are characterized by ambiguity and uncertainty—problems that humans are typically good at solving.
Key Characteristics of Cognitive Systems
Cognitive systems exhibit several key characteristics that distinguish them from traditional computing systems:
- Adaptive: They are designed to learn as information changes and as goals and requirements evolve. They can digest dynamic data in real-time and make adjustments accordingly.
- Interactive: They can easily interact with users so that those users can define their needs comfortably. They can also interact with other processors, devices, and cloud services, as well as with people.
- Iterative and Stateful: They can aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They "remember" previous interactions in a process and return information that is suitable for the specific application at that point in time.
- Contextual: They understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user'''s profile, process, task and goal. They draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided).
Cognitive Computing vs. Traditional AI
While cognitive computing is a subset of artificial intelligence, it has a more specific focus. Traditional AI might involve a program that can play chess or identify images based on training data. Cognitive computing goes further by aiming to create systems that can tackle problems where the "rules" aren'''t clearly defined, and where learning and adaptation are paramount. They are designed to augment human intelligence, helping us make better decisions rather than replacing human thought entirely.
For example, in complex fields like financial analysis, platforms like Pomegra.io leverage AI to provide data-driven insights, helping users navigate market complexities. This aligns with the cognitive computing goal of augmenting human capabilities in decision-making processes by processing vast amounts of information and identifying patterns, similar to how cognitive systems are designed to assist in various domains.
Cognitive computing systems are not about creating sentient machines; they are about building tools that can process information in a way that is analogous to how humans do, helping us to understand and act upon the vast amounts of data in our world.
Understanding these fundamentals is crucial as we explore the key technologies that underpin cognitive computing and its diverse applications. It also sets the stage for considering the challenges and future outlook as well as the ethical implications.
To further contextualize, understanding Explainable AI (XAI) can be beneficial, as it addresses the need to make the decisions of complex AI systems, including cognitive ones, transparent and understandable to humans.