COGNITIVE COMPUTING:Cognitive computing refers to technology platforms that, broadly speaking, are based on the scientific disciplines of artificial intelligence and signal processing.


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.

Cognitive computing is a distinct field collectively where it serves as an assistant rather than the one finishing the task. In this way, it provides humans with the ability to analyze data faster and more accurate, without having to worry about the wrong decisions taken by the machine learning system

Cognitive computing will give rise to personal cognitive assistants for students, teachers and support staff. For instance, a cognitive assistant can make adjusting to a new campus so much smoother, acting like a kind of companion, explaining directions on a new campus and answering any questions that might come up.

Cognitive computing describes technology platforms that combines machine learning, reasoning, natural language processing, speech, vision, human computer interaction, that mimics the functioning of the human brain and helps to improve human decision making.

Best examples of Cognitive computing

  1. Siri
  2. Google Assistant
  3. Cortana,
  4. Alexa

Cognitive computing systems are thinking, reasoning and remembering systems that work with humans to provide them with helpful advice in making decisions. Its insights are intended for human consumption. AI intends to use the best algorithm to come up with the most accurate result or action.

To work effectively cognitive computing systems must have the following attributes:

  • 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.
  • 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.
  • 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:

  • 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.
  • 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.
  • 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 cc 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 cc 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.

Key Differences Between cognitive and AI

1.     . Interaction with humans

CC systems are thinking, reasoning and remembering systems that work with humans to provide them with helpful advice in making decisions. Its insights are intended for human consumption. AI intends to use the best algorithm to come up with the most accurate result or action. It works without human input.

2.     Contextual solutions

CC can take into consideration conflicting and changing information that fits contextually into the situation at hand. It’s results come from using predictive and prescriptive analytics–not pre-trained algorithms. For example, if an adult in her sixties wished to learn what program to follow to increase muscle strength, AI would come up with the best program there is (maybe suited to an athlete). In contrast, cognitive computing would consider her age and abilities and suggest modifications to the program. In the end, AI uses algorithms to solve problems to come up with a final decision; cognitive computing provides the pertinent information that will allow humans to make the final decision for themselves.

3.     Use cases of cognitive computing

In general, cognitive computing use cases are predominantly found in analysis-intensive industries. Some examples include:

  • In healthcare, cognitive computing is helping doctors make better diagnoses and individualize their treatment decisions. As a result of cognitive computing’s ability to access databases worldwide via the cloud, doctors now have access to treatments and diagnoses they would not have otherwise. Cognitive systems used to read patient images are finding things that human radiologists often miss.
  • Finance services companies use cognitive computing’s analytic capabilities to find the right products to meet their clients’ needs. And, if a product suggested by the system is not among their current offerings, companies are making use of the analysis to develop more personalized services. By combining market trends with customer behavior data, cognitive computing is helping finance companies assess an investment risk. Cognitive computing also helps companies combat fraudby analyzing past parameters that can be used to detect fraudulent transactions. Insurance companies use cognitive computing to reduce underwriting risks and claims. For example, auto insurance companies are pairing cognitive analysis with data from IoT devices that capture their clients’ driving behaviors to adjust premiums accordingly.
  • Retail companies are using cognitive computing to provide customers with a personalized online shopper capability that makes it easy to find what they are looking for online.
  • Manufacturers use cognitive computing technologies to maintain and repair their machinery and equipment, identify defective parts, and reduce production times and parts management.

AI has use cases in all these industries, but its output is directed toward automating processes, often as chatbots, virtual assistants, and smart advisors, rather than decision support. For example, an AI virtual assistant will provide a doctor with a specific treatment option that should be followed, whereas cognitive computing will come up with several viable treatments, leaving it to the doctor to decide which is best for the patient.

Other use cases for AI include digital assistants such as Siri, Cortana and Alexa; in the computer vision systems of self-driving cars; in video games; and as healthcare bots that schedule appointments and offer round-the-clock assistance to patients. Banks also use AI to monitor their credit and debit card transactions and applications in such industries as finance, logistics, aviation, and transportation.