What is cognitive computing – More and more companies are joining the implementation of Cognitive Computing to optimize their processes. In fact, global spending on Artificial Intelligence (AI) and Cognitive Computing systems will continue to grow for the following years, to the extent that by 2022 an investment in the sector of 77.6 billion dollars is expected, which means a growth of 223% compared to the 24 billion dollars reported in 2018.
What Is Cognitive Computing?
Cognitive Computing is a combination of Artificial intelligence, neural networks, machine learning, natural language processing, sentiment analysis to solve everyday problems faced by humans.
Logical data analysis, improved operational efficiency, improved customer interaction, are some of the functions behind Cognitive Computing, which has made companies such as Facebook, Apple and Google join the use of this technology to be able to make better announcements through data analysis, pattern identification and prediction.
Myths About Cognitive Computing
Although many large corporations use and benefit from Cognitive Computing, there are still certain myths around them that are important to clarify.
Cognitive Computing – Replaces the humans and their jobs
One of the main fears of the development and advancement of technologies such as AI is the disappearance of jobs where machines represent more benefits than having a worker.
While it is impossible to know what will happen for sure in the future regarding this issue, it is anticipated that job loss will not be an important implication with the implementation of cognitive technologies.
According to the Deloitte State of Cognitive survey, managers with knowledge of the area do not contemplate that dismissals are a consequence of the implementation of these technologies. And he points out that, although automation results in the reduction of workforce templates, new jobs will also be developed due to this.
Cognitive Computing Equals Robots And Humanoids
Although it is constantly related to AI and cognitive computation with robots or humanoids, the reality is that this technology can be present in any computer system without us noticing, because its objective is to complement and make operations more efficient with the least human intervention.
On the other hand, intelligence is limited by its developers to fulfill the functions for which they are intended, learn from mistakes and be able to correct them. So the development of features or functions that exceed the initial purpose with which a machine was created is a myth.
Cognitive Programming – It will only be understood by expert programmers
The tasks we can solve with cognitive computation are endless. It is currently present in banks, insurers, hospitals and, above all, in aerospace and automotive companies. So the results are visible to anyone.
There are more and more experts in the field and firms that seek to implement this technology. Toyota and IBM, for example, announced millions of investments for this sector. The IBM Watson computer has been a very effective auxiliary in the field of public health, so, when developing this kind of product, it is intended to be understandable and operable by anyone.
To imagine a chatbot that requires the insertion of codes instead of questions about a certain service, would be useless. To apply Cognitive Computing, the advice of the service provider is required, to check if it can work or not in your company.
Cognitive Programming – More financial benefits for companies
According to a survey conducted by the professional services firm, Deloitte, 83% of the companies that have implemented AI claim to have obtained economic benefits from their projects, and only 16% of respondents said their company had not achieved any economic benefit
In fact, the survey suggests that the economic benefits of AI may increase with experience. The more deployments of this technology the respondents reported, the higher the percentage that obtained economic benefits. Among executives whose companies have implemented 11 or more AI projects, 92% reported economic gains.
What Is Meant By Cognitive Computing?
Artificial Intelligence – since the conception of computer science, AI has been an objective that experts have sought to achieve. At present, it seems that this technology is being mastered thanks to cognitive computing models.
Cognitive Computing uses a combination of AI, neural networks, machine learning, natural language processing, sentiment analysis to solve everyday problems faced by humans. In the near future, cognitive computing will have a major impact not only on our private lives but also on various industries.
According to David Kenny, general manager of IBM Watson, the most advanced cognitive computing framework, “artificial intelligence can only be as intelligent as the people who teach it.” It is expected that this does not apply to the last cognitive revolution.
How Cognitive Computing Works
The cognitive computing process uses a combination of artificial intelligence, neural networks, machine learning, natural language processing, feelings analysis and contextual awareness to solve everyday problems as well as humans.
What is the goal of cognitive computing
IBM defines cognitive computing as an advanced system that learns at scale, reasons with a purpose and interacts with humans naturally.
The objective of cognitive computing is to simulate human thought processes in a computerized model. Using self-learning algorithms that use data mining, pattern recognition, and natural language processing, the computer can mimic the way the human brain works.
While computers have been faster in calculations and processing than humans for decades, they have not been able to perform tasks that humans take for granted as simple, such as understanding natural language or recognizing unique objects in an image.
Some people say that cognitive computing represents the third era of computing: we move from computers that could tabulate sums (1900) to programmable systems (1950), and now to cognitive systems.
Cognitive Computing System – IBM Watson
These cognitive systems, especially IBM Watson, rely on Deep Learning algorithms and neural networks to process information by comparing it with a set of teaching data. The more data the system is exposed, the more it learns and the more accurate it becomes over time, and the neural network is a complex “tree” of decisions that the computer can make to arrive at an answer.
Cognitive computer systems can synthesize data from various sources of information 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 natural language processing (NLP) to mimic the way the human brain works.
The use of computer systems to solve the types of problems that humans generally deal with requires large amounts of structured and unstructured data, fed to machine learning algorithms. Over time, cognitive systems can refine the way they identify patterns and the way they process data to be able to anticipate new problems and model possible solutions.
Cognitive Computing Consortium
To achieve these capabilities, cognitive computing systems must have these key attributes, according to the Cognitive Computing Consortium :
- Adaptive: cognitive systems must be flexible enough to learn as information changes and goals evolve. Systems should be able to digest dynamic data in real-time and make adjustments as the data and the environment change.
- Interactive: Human-computer interaction (HCI) is a critical component in cognitive systems. Users should be able to interact with cognitive machines and define their needs as those needs change. Technologies should also be able to interact with other processors, devices and cloud platforms.
- Iterative and stateful: Cognitive computing technologies can also identify problems by asking questions or extracting additional data if a declared problem is vague or incomplete. Systems do this by maintaining information on similar situations that have previously occurred.
- Contextual: understanding the context is fundamental in thought processes, so cognitive systems must also understand, identify and extract contextual data, such as syntax, time, location, domain, requirements, the profile of a specific user, tasks or objectives. They can rely on multiple sources of information, including structured and unstructured data and visual, auditory or sensor data.
What is the difference between cognitive computing and artificial intelligence?
Due to its proximity to Artificial Intelligence, it is necessary to be clear about the differences between it and Cognitive Computing.
- While the basic use case of artificial intelligence is to implement the best algorithm to solve a problem, cognitive computing goes one step further and tries to mimic human intelligence and wisdom by analyzing a number of factors. Compared to AI, cognitive computing is a completely different concept.
- Cognitive computing learns and mimics the process of human thinking.
- Unlike artificial intelligence systems that only deal with a given problem, cognitive computing learns by studying patterns and suggests that humans take relevant measures based on their understanding. In the case of artificial intelligence, the system takes full control of a process and takes steps to complete a task or avoid a scenario by using a predefined algorithm.
- While in comparison, cognitive computing is a completely different field in which it serves as an assistant rather than the one that completes the task. In this way, cognitive computing gives humans the power of faster and more accurate data analysis without worrying about the wrong decisions made by the machine learning system.
- The main objective of cognitive computing is to help humans in decision making. This gives humans a higher degree of accuracy in the analysis while ensuring that everything is under their control. To illustrate, let’s take the example of artificial intelligence in the health system. An AI-backed system would make all treatment decisions without consulting a human physician, while cognitive computing would complement the human diagnosis with its own set of data and analysis that helps improve the quality of the decision and adds a human touch. to the critical processes.
What technologies are “cognitive”?
“Cognitive technologies” include machine learning, deep learning neural networks, natural language processing, rule engines, robotic process automation, and combinations of these capabilities for higher-level applications.
Robotic process automation (RPA) – RPA is software that automates repetitive, rules-based processes usually performed by people sitting in front of computers. By interacting with applications just as humans would, software robots can open email attachments, complete e-forms, record, and re-key data, and perform other tasks that mimic human action.
Computer vision – Computer vision is the ability to extract meaning and intent out of visual elements, whether characters (in the case of document digitization), or the categorization of content in images such as faces, objects, scenes, and activities.
Machine learning (ML) – Machine learning is the ability of statistical models to develop capabilities and improve their performance over time without the need to follow explicitly programmed instructions.
Natural language processing/generation (NLP/G) – NLP is the ability to extract or generate meaning and intent from the text in a readable, stylistically natural, and grammatically correct form.
Speech recognition – Speech recognition is the ability to automatically and accurately recognize and transcribe human speech.
Rules-based systems – Rule-based system is the ability to use databases of knowledge and rules to automate the process of making inferences about information.
Deep learning – Deep learning is a relatively complex form of machine learning involving neural networks, with many layers of abstract variables. Deep learning models are excellent for image and speech recognition but are difficult or impossible for humans to interpret.
Physical robots – Physical robots can perform many different tasks in unpredictable environments, often in collaboration with human workers. The broader field of robotics is embracing cognitive technologies to create robots that can work alongside, interact with, assist, or entertain people.