What is cognitive computing?
Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers may 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 have 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 a human brain works.
To accomplish this, cognitive computing makes use of 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
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 speech recognition, sentiment analysis, risk assessments, face detection and more. In addition, it 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 are able to refine the way they identify patterns and the way they 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 is able to learn and the more accurate it becomes over time.
To achieve those capabilities, cognitive computing systems must have the following attributes:
- Adaptive. These systems must be flexible enough to learn as information changes and as goals evolve. They must digest dynamic data in real time and adjust as the data and environment 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 those needs 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 previously occurred.
- Contextual. Understanding context is critical in thought processes. Cognitive systems must understand, identify and mine contextual data, such as syntax, time, location, domain, requirements and a user's profile, tasks and goals. The systems may draw on multiple sources of information, including structured and unstructured data and visual, auditory and sensor data.
Examples and applications of cognitive computing
Cognitive computing systems are typically used to accomplish tasks that require the parsing of large amounts of data. For example, in computer science, cognitive computing aids in big data analytics, identifying trends and patterns, understanding human language and interacting with customers.
Examples of how cognitive computing is used in various industries include the following:
- Healthcare. Cognitive computing can deal with large amounts of unstructured healthcare data such as patient histories, diagnoses, conditions and journal research articles to make recommendations to medical professionals. This is done with the goal of helping doctors make better treatment decisions. Cognitive technology expands a doctor's capabilities and assists with decision-making.
- Retail. In retail environments, these technologies analyze basic information about the customer, along with details about the product the customer is looking at. The system then provides the customer with personalized suggestions.
- Banking and finance. Cognitive computing in the banking and finance industry analyzes unstructured data from different sources to gain more knowledge about customers. NLP is used to create chatbots that communicate with customers. This improves operational efficiency and customer engagement.
- Logistics. Cognitive computing aids in areas such as warehouse management, warehouse automation, networking and IoT devices.
IBM's Watson for Oncology is an example of a cognitive computing system. It provides oncologists at Memorial Sloan Kettering Cancer Center in New York with evidence-based treatment options for cancer patients. When medical staff input questions, Watson generates a list of hypotheses and offers treatment options for doctors to consider. Watson Health is another IBM tool that helps clients in medical and clinical research.
Advantages of cognitive computing
Advantages of cognitive computing include positive outcomes in the following areas:
- Analytical accuracy. Cognitive computing is proficient at juxtaposing and cross-referencing structured and unstructured data.
- Business process efficiency. Cognitive technology can recognize patterns when analyzing large data sets.
- Customer interaction and experience. The contextual and relevant information that cognitive computing provides to customers through tools like chatbots improves customer interactions. A combination of cognitive assistants, personalized recommendations and behavioral predictions enhances customer experience.
- Employee productivity and service quality. Cognitive systems help employees analyze structured or unstructured data and identify data patterns and trends.
Disadvantages of cognitive systems
Cognitive technology also has downsides, including the following:
- Security challenges. Cognitive systems need large amounts of data to learn from. Organizations using the systems must properly protect that data -- especially if it is health, customer or any type of personal data.
- Long development cycle length. These systems require skilled development teams and a considerable amount of time to develop software for them. The systems themselves need extensive and detailed training with large data sets to understand given tasks and processes.
- Slow adoption. The slow development lifecycle is one reason for slow adoption rates. Smaller organizations may have more difficulty implementing cognitive systems and therefore avoid them.
- Negative environmental impact. The process of training cognitive systems and neural networks consumes a lot of power and has a sizable carbon footprint.
How cognitive computing differs from AI
The term cognitive computing is often used interchangeably with AI. But there are differences in the purposes and applications of the two technologies.
AI is the umbrella term for technologies that rely on data to make decisions. These technologies include -- but aren't limited to -- machine learning, neural networks, NLP and deep learning systems. With AI, data is fed into an algorithm over a long period of time so that the system learns variables and can predict outcomes.
The term cognitive computing is typically used to describe AI systems that simulate human thought. Human cognition involves real-time analysis of the real-world environment, context, intent and many other variables that inform a person's ability to solve problems.
A number of AI technologies are required for a computer system to build cognitive models. These include machine learning, deep learning, neural networks, NLP and sentiment analysis.
In general, cognitive computing is used to assist humans in decision-making processes. AI relies on algorithms to solve a problem or identify patterns in big data sets. Cognitive computing systems have the loftier goal of creating algorithms that mimic the human brain's reasoning process to solve problems as the data and the problems change.
Learn more about cognitive computing and how it can also be used in employee education and training.