What is cognitive computing and why you need to know about it

A computer with a brain that thinks and behaves like a human being? Nothing is impossible with this technological revolution that continues to surprise us day after day! Today, one can give “eyes and a brain” to his computer: thus, he can become able to replace humans for repetitive tasks and facilitate enormously our daily life! This concept is known today as “Cognitive Computing”. Indeed, computers might not possess cognitive abilities, but they are capable of executing operations which completely rely on human perceptions. It’s always possible to use the power of automation: from handwriting recognition, face identification and behavioral pattern determination to any task requiring cognitive skills, computers are capable of delivering the right solutions.

Artificial intelligence has been a far-flung goal of computing since the conception of the computer, but we may be getting closer than ever with new cognitive computing models. Cognitive computing comes from a mashup of cognitive science — the study of the human brain and how it functions — and computer science. Nowadays, researchers are developing new systems that amalgamate the incredibly intricate processes of the human brain with the vast data stores of a computer.

  • Definition of cognitive computing and what this technology can do

After this introduction, we arrive at describing cognitive technologies. In a simple sentence, cognitive computing is based on self-learning systems that use machine-learning techniques to perform specific, human-like tasks in an intelligent way.

The goal 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.

Some people say that cognitive computing represents the third era of computing: we went from computers that could tabulate sums (1900s) to programmable systems (1950s), and now to cognitive systems.

Cognitive technology empowers the IT infrastructure of an enterprise. As the result, business organizations are better equipped to make cost cut-downs by ensuring increased productivity and enhanced operational speed.

Deloitte refers to cognitive computing as “more encompassing than the traditional, narrow view of artificial intelligence”. Indeed, AI has been primarily used to describe technologies capable of performing tasks normally requiring human intelligence, he says.

  • Examples of cognitive technology in the corporate world today

Nowadays, we can find many examples of the strides made by cognitive computing. Indeed, some organizations are deploying cognitive tools and using cognitive systems for product recommendations, pricing optimization, and fraud detection. Companies are also developing conversational AI platforms (in the form of chatbots) for automated customer support, automated sales assistance, and decision augmentation.

Presently, the cognitive computing landscape is dominated by large players like IBM, Microsoft, and Google. IBM, being the pioneer of this technology, has invested billion dollars in big data and analytics and now spends close to one-third of its R&D budget in developing cognitive computing technology. Moreover, many new companies are investing heavily in this technology to develop better products.

We can have a look at the way key players of this sector are implementing cognitive technologies:

IBM Watson: Watson is an IBM supercomputer that combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a “question answering” machine. IBM Watson leverages deep content analysis and evidence-based reasoning. Combined with massive probabilistic processing techniques, Watson can improve decision making, reduce costs, and optimize outcomes.

Google Deepmind: In the past few years, Google has acquired a lot of AI and machine learning-related startups and rivals within the market, moving the space towards consolidation. The company has created a neural network that learns how to play video games in a fashion similar to that of humans.

A concrete example of cognitive technology implementation into product offerings is Netflix. It is a famous movie rental service for movies and TV Series online. This platform also suggests users more stuff to watch. Now if the users’ interest can be predicted and the suggested content is in accordance with the interest, it means that the technology works perfectly.

  • Limitations and major challenges of cognitive computing

Presently, there are problems and limitations in cognitive systems that we need to be conscious of and to figure out how to solve such issues.

With this technology, there is a limited analysis of risk which is missing in the unstructured data. This includes socio-economic factors, culture, political environments, and people. We can take the example of a predictive model discovering a location for oil exploration. The fact is that if the country is undergoing a change in government, the cognitive model should take this factor into consideration. Thus human intervention is necessary for complete risk analysis and final decision making.

Also, cognitive systems need a meticulous training data process. The laborious process of training cognitive systems is most likely the reason for its slow adoption. Moreover, the complex and expensive process of using cognitive systems makes it even worse.

Another thing is that cognitive computing systems are most effective as assistants which are more like intelligence augmentation instead of artificial intelligence. It supplements human thinking and analysis but depends on humans to take the critical decisions.

What is also going to be a main challenge to tackle in this sector is about privacy. Access to data is easy and vulnerable for organizations, so measures should be taken to safeguard the right to privacy.

So cognitive computing is definitely the next step in computing started by automation. It sets a benchmark for computing systems to reach the level of the human brain. But presently it has some limitations which make AI difficult to apply in situations with a high level of uncertainty, rapid change or creative demands. The complexity of problem grows with the number of data sources.

We can say that cognitive computing is going to be a big deal as it’s a powerful tool in the making. Nevertheless, humans having this tool must decide how to best use it and must know the art of incorporating it. If this technology is used correctly, the power of cognitive and artificial intelligence will take-off towards unsurpassed excellence in the next 5 years.

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