cognitive computing

 

What is cognitive computing?

Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers might be ambiguous and uncertain. The phrase is closely associated with IBM's cognitive computer system, Watson.

Computers are faster than humans at processing and calculating, but they've yet to master some tasks, such as understanding natural language and recognizing objects in an image. Cognitive computing is an attempt to have computers mimic the way the human brain works.

To accomplish this, cognitive computing uses artificial intelligence (AI) and other underlying technologies, including the following:

• Expert systems.
• Neural networks.
• Machine learning.
• Deep learning.
• Natural language processing (NLP).
• Speech recognition.
• Object recognition.
• Robotics.

Cognitive computing uses these processes in conjunction with self-learning algorithms, data analysis and pattern recognition to teach computing systems. The learning technology can be used for sentiment analysis, risk assessments and face detection. In addition, cognitive computing is particularly useful in fields such as healthcare, banking, finance and retail.

How cognitive computing works

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 this, cognitive systems include self-learning technologies that use data mining, pattern recognition and NLP to mimic human intelligence.

Using computer systems to solve the types of problems that humans are typically tasked with requires vast amounts of structured and unstructured data fed to machine learning algorithms. Over time, cognitive systems can refine the way they identify patterns and process data. They become capable of anticipating new problems and modeling possible solutions.

For example, by storing thousands of pictures of dogs in a database, an AI system can be taught how to identify pictures of dogs. The more data a system is exposed to, the more it's able to learn and the more accurate it becomes over time.

To achieve those capabilities, cognitive computing systems must have the following attributes:

• Adaptive. Systems must be flexible enough to learn as information changes and goals evolve. They must digest dynamic data in real time and adjust as the data and environments change.
• Interactive. Human-computer interaction is a critical component in cognitive systems. Users must be able to interact with cognitive machines and define their needs as they change. The technologies must also be able to interact with other processors, devices and cloud platforms.
• Iterative and stateful. 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 occurred previously.
• Contextual. Understanding context is critical in thought processes. Cognitive systems must understand, identify and mine contextual data, such as syntax, time, location, domain, user requirements, user profiles, tasks and goals. The systems can draw on multiple sources of information, including structured and unstructured data and visual, auditory and sensor data.