The term ‘Cognitive Computing’ was initially coined by IBM which was meant for machines that could think and act like humans. With the help of Artificial Intelligence (AI) today, machines are smarter and can not only capture data, but also make human-like decisions with the help of the data. With technological advancement, today Cognitive Computing is the in thing. Google DeepMind and IBM Watson are the present leaders in Cognitive Computing.
Reach of Cognitive Computing
Cognitive Computing may include varied components such as: emotional intelligence, image recognition, deep learning, machine learning, natural language processing and so on. By utilizing diagnostic, predictive, and prescriptive analytics tools, Cognitive Computing observes, learns, and offers insights and suggestions for the user to perform actions. It has been used in sectors such as banking, healthcare, media and entertainment via the use of machine learning, natural language processing, speech recognition technology etc.
Applications of Cognitive Computing
Cognitive Computing is already at work. It has proved to be extremely helpful in fraud detection. The techniques used can unearth emerging patterns and trends in data which may prove difficult for humans to track. Earlier this detection that was done manually would require several hours, thanks to cognitive computing; it is done even quicker. It has made way in retail too. By collecting shopper history, it can help retailers maintain optimal inventory levels to meet targeted customer needs. In addition to that, if the weather data can be captured, then a mobile notification may be sent of a sale on umbrellas just as a customer is approaching a retail shop on a day when rain is expected. Thus, by the help of Artificial Intelligence, Cognitive Computing can work on improving user interface.
Nowadays, mobile payments have been a primary source of transactions in many areas. This is where Cognitive Computing comes to play. With customers using smartphones for a majority of transactions, retailers can get access to additional data and insights on spending patterns and shopper history. They can work with ecosystem partners to deliver customized experiences which retail customers want — such as offering a mobile payment option for customers’ convenience who are making a purchase while on the go.
Doctors will be able to design better treatments for patients if provided with patient’s history. Cognitive computing applications will also be able to enable governments to improve taxation systems, better understand how to deploy government funding to achieve the highest impact on citizen welfare and enable less skilled professionals to perform higher-value roles. These advances will improve their ability to cope with social problems.
Risks of Cognitive Computing
A more immediate risk than the transformation of the workforce is that companies will fail to properly implement cognitive computing applications into their workflows. Around 50% of business ethics violations will occur as a result of improper use of ‘big data’ analytics by 2018. These breaches will occur in different ways, from the poor execution of applications (failing to deliver on promised efficiency gains, resulting in wasted time and money) to improper storage or usage of data that will lead to security breaches, damaging the reputations and finances of the companies. There will also be privacy issues of people. With smart lens coming up, focus must be given to cyber security so that the pool of data does not get in unsafe hands.
By 2020 it is expected that India will be home to the largest developer population in the world and 3 million programmers will be helping develop at least ten per cent of the apps this year. Cognitive Computing and technology is still an assistive technology to help suggest strategies and probabilities of outcomes, and thus human expertise is still important.
