What is Cognitive Computing from Coding compiler. Cognitive computing uses artificial intelligence technologies to simulate human thought processes. The goal is to develop own solutions and strategies based on experience. The systems interact in real time with their environment and process large amounts of data.
What is Cognitive Computing?
There is no clear and universal definition for Cognitive Computing. Typically, cognitive computing refers to computer models or computer systems that use artificial intelligence ( AI ) technologies such as deep learning or data mining use to simulate human learning and thought processes.
Cognitively working systems are not programmed in advance to concrete problem solutions, but learn independently through the experience and data collected. They develop their own solutions and strategies by analyzing the database. The self-learning IT systems are able to interact in real-time with your environment and take into account the information obtained for their independently developed knowledge. This requires storing large amounts of data and processing and analyzing them at high speed.
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Since the data is unstructured in many different formats, techniques and database systems from the Big Data environment are used. The goal of cognitive computing is to create intelligent computer systems that independently solve problems and automate solutions without human help. Core components of cognitive computing are machine learning algorithms, which continuously examine the available data for patterns and constantly refine their analysis methods.
Many systems use speech recognition for direct communication with humans. Cognitive Computing is used in a variety of areas and is used, for example, for predictive maintenance or in the e-commerce environment for product recommendations. Further areas of application are medicine, robotics or virtual reality. A well-known implementation of cognitive computing is the IBM computer system “Watson“.
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The most important requirements for cognitive computer systems
The most important prerequisite for cognitive computing is the ability to learn independently from the experience gained. At the same time, one’s own solutions must always be reflected and questioned. For the systems to work similarly to the human brain, further requirements have to be fulfilled.
Communication with people and the environment must be interactive and real-time. The cognitive systems must also be adaptive, taking into account that information is changing or ambiguous. This requires processing data in near real-time.
It is important that cognitive computing perceives all information in its context. These include contextual features such as time, place, and people that influence the meaning of information. Text, language or gesture-based inputs are to be supported as input methods.
Cognitive Computing and Big Data Technologies
An essential feature of cognitive computing is that huge amounts of data of various kinds have to be stored and processed within a short time. Because data is typically unstructured, traditional relational SQL-based database systems for cognitive computing can not be used efficiently.
It uses techniques and database systems from the big data environment that pursue no-SQL approaches and process large amounts of data in near real-time. Only then is it possible for cognitive systems to quickly process, enrich and understand the data.
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Uses and Examples of Cognitive Computing
For cognitive computing there is a wide variety of different applications. Cognitive systems and artificial intelligence are already being used in finance, medicine, marketing or industry and the Internet of Things ( IoT ).
In e-commerce, cognitive computing is able to deliver exactly the right product recommendations based on analyzed customer behavior and user profiles. Speech computers from telephone hotlines use cognitive methods to recognize the inputs and concerns of callers and independently provide suitable solutions.
Search engine providers use cognitive computing to provide better results for a wide variety of search queries. It can better understand complex, multi-level search queries and deliver more relevant results. In medicine, it is possible to draw conclusions about diseases by analyzing images and comparing them with other images. There are systems that support radiologists in their work and analyze X-rays or MRI findings. The purpose of these systems is to reduce the error rate of diagnoses and to prevent over-sighting.
For cognitive computing to do so, the systems have to be equipped with a variety of radiological images and trained using artificial intelligence. In autonomous driving, cognitive computer systems use sensor data, GPS information and previous experience to learn how to drive vehicles independently and how to make the right decisions when certain events occur. Other areas of application for cognitive computing are face recognition or customer relationship management.
Cognitive computing and the Watson computer system from IBM
A well-known example of cognitive computing is IBM’s Watson computer system. It is closely associated with the term Cognitive Computing and gained worldwide fame in 2011 when it competed against humans in the English quiz show “Jeopardy!“. Watson answered questions independently and meaningfully.
Even dialects and ambiguity or jokes could recognize the system. IBM sees Watson as an important element of its AI strategy and aims to revolutionize problem-solving practices. IBM uses evidence-based learning and deep content analysis.