Cognitive Computing- How does Cognitive Computing work?
Artificial intelligence has evolved a lot since the first computer. Due to cognitive Computing, AI could soon become as efficient as human intelligence. This technology combines cognitive science with computer science, and could strongly influence all industries as well as our private lives. Also, it could help solve the main problem of Big Data. Discover the definition, operation and application areas of cognitive Computing.
What is Cognitive Computing?
Cognitive Computing, or cognitive Computing, is the simulation of the human thought process within a computer model. This technology is based on computer learning systems using data mining, pattern recognition and natural language processing to mimic the way the human brain works. Indeed, suppose computers have been able to perform calculations and process information faster than humans for several decades. In that case, they struggle to perform simple tasks for humans, such as understanding natural language or recognizing objects within an image.
For some, cognitive Computing represents the third era of Computing, after computers capable of tabulating in the 1900s and programmable systems of the 1950s. The best-known cognitive system is the IBM Watson cognitive computer, which relies on deep learning algorithms and neural networks to process information by comparing it to a set of data. The more data the system receives, the more it learns, and the more its accuracy increases over time. The neural network looks like a tree of complex decisions that the computer can take to arrive at an answer.
How does Cognitive Computing work?
Cognitive computing systems use machine learning algorithms. In such systems acquire knowledge on a continuous basis from the data they receive, by mining the data in search of information. The system refines the way it looks for patterns and the way it processes data in order to become able to anticipate new problems and model possible solutions.
What is Cognitive Computing used for?
The goal of Cognitive Computing is to create automated computer systems capable of solving problems without the need for human assistance. The Cognitive Computing is used by many applications of artificial intelligence, including expert systems, natural language programming, neural networks, robotics and virtual reality.
In healthcare, for example, Watson could look at a patient's condition and his or her liabilities with journal articles, known best practices, and various diagnostic tools to recommend the best treatment. The doctor can then choose the best treatment option based on a variety of factors such as a patient's history to make better treatment decisions.
In other words, cognitive Computing is not meant to replace a physician, but to expand its capabilities by analyzing an amount of data too vast for humans to provide a workable summary. This type of processing can be done in any field where large amounts of complex data must be processed and analyzed to solve problems, including finance, law, and education.
These systems will also be applied to other sectors such as consumer behavior analysis, customer service robots, travel agencies, security and diagnostics. Hilton Hotels recently launched its first concierge robot, Connie, capable of answering questions posed in natural language about a hotel, local attractions, and restaurants.
Personal digital assistants like Siri and Google Assistant are not really cognitive systems. They offer a set of pre-programmed responses and can only respond to a pre-recorded number of requests. However, in the near future, it will be possible to ask questions on our phones, computers, cars or homes and get a real answer without prior programming.
Cognitive Computing: the humanistic side of IT
Cognitive Computing means the practical application of Artificial Intelligence in everyday life, thanks to a mix of cognitive sciences applied to information technology.
We could say that it is the humanistic side of IT, in which the computer does not just respond to what it knows or what it has been programmed for, but introduces an unpredictable response factor derived from the self-learning of the system.
To understand this, an action done today by a human through software will probably give a different answer if replicated, without changing it, after some time. Or in other words, computers grow by themselves in cognitive capacity.
Imitating the human brain
The goal of Cognitive Computing is to simulate human thought processes in a computer model. By using self-learning algorithms that use data mining, pattern recognition and natural language processing, software becomes able to mimic the way the human brain works. To achieve this, however, it was necessary to overcome the limitations of traditional computer systems in the understanding of natural language (spoken or written) or in recognition of unique objects within an image.
The importance of calculation skills
These results are possible because, in addition to artificial intelligence, today, we have enormous computing capacity and speed. And, in fact, the quality of the response is directly proportional to the amount of data the system has available: the more it learns, the more accurate the response becomes over time.
It is no coincidence that among the pioneers of Cognitive Computing we find IBM with its IBM Watson architecture, a platform based on powerful learning algorithms structured on neural networks able to process and compare huge volumes of data.
Obviously, Google does not stand by and, due to the large database at its disposal has shown that it knows how to develop the cognitive capacity of its services by offering qualitative growth over time. Just think that its test systems, which in 2013 were able to recognize a pizza simply from a photo, can now tell that that pizza is topped with mushrooms and if it's overcooked. It’s all starting from the same image.
The fields of application are endless, from retail to healthcare, from finance to entertainment. There are no limits. In the healthcare context, Cognitive Computing helps in the diagnosis by crossing different types of specific data, for example, the patient's history, with external sources such as magazine articles, blogs, statistical data and much more. The doctor continues to make his own subjective contribution, but he does it supported in real-time by a quantity of information and a pre-analysis until recently obtainable only with long and expensive research and studies. The same process also applies to sectors such as finance, commerce, and training. If we consider, then, the usefulness of the time factor, we find various applications in sectors related to the analysis of consumer behavior. This is how the examples are created in which a virtual assistant, robotic or not, is made available for personal shopping, customer assistance, tourist information, safety tutors and much more. Among the pioneers in the Hilton Hotels chain in which Connie recently debuted was the first concierge robot capable of answering questions about hotels, local attractions and restaurants in natural language.
The foundation for tomorrow
The personal digital assistants we have in our smartphones, such as Siri, Cortana or Google Now are not true cognitive systems since they have a preprogrammed set of valid answers for a predetermined number of requests. Even if they are not cognitive applications in the strict sense, it is evident that they are the prelude to the entry of Cognitive Computing in our daily life. The time has come and, without knowing it, we are helping to build a huge database at the service of the Cognitive Computing of tomorrow.
Author: Vicki Lezama