April | 2017
Today, organizations are constantly innovating and reinventing their offerings and themselves with the sole aim to make the lives of their customers easier. Currently, at the pinnacle of this drive to innovate lies Cognitive computing, or as we call them ‘Super-Intelligent Computers’. Personal Assistant Apps such as Apple’s Siri, intelligent machines like Google’s autonomous car, Amazon Go for Smart shopping and even Facebook’s face recognition feature think, mimic and behave just like humans. These algorithms thrive on data; the more the quantum of data you feed into them, the smarter they become and in turn, more accurate the results. Cognitive computing today is bringing about a disruption in the Quality Assurance Lifecycle, and are impacting every phase; powered by automation,, it is enabling organizations in better decision making, analyzing historic data and reports to predict or forecast future results.
Let us now try to understand what Cognitive computing is and how does it impact Quality Engineering (QE).
Cognitive computing is a product of Artificial Intelligence (AI). AI is the theory and development of computer systems which are able to perform tasks that normally require human intelligence. AI has been around for quite some time (since 1950s), and has been evolving ever since. Today, enterprises have to deal with large quantities of unstructured data across varied domains, increasing the scope of application of AI.
What does cognitive computing entail?
To answer this question, let’s focus on the major facets of cognitive systems:
Impact on QE
Cognitive algorithms, if infused into the different phases of the software life-cycle, has the ability to alter it completely and result in enormous benefits to QE from a cost, time and quality perspective. Use cases include:
Now coming to the million dollar question - can these intelligent systems replace humans someday? Well, the aim of AI is not to replace humans but only to augment them. For instance, in the QE&A world, when automation became easy to implement and in turn popular, it was rumored that a lot of people would lose their jobs. However there are some tasks for which humans cannot be replaced such as designing the automation framework and associated automation scripts. Hence, the role of testers evolved from being manual testers to automation experts. So, it is very crucial for humans to expand their skills in the direction where the industry is moving.
It is not always the case that AI systems work in the right direction. If not fed with the right data or tested thoroughly, they are bound to falter as well. A very fresh example is that of the debacle of Microsoft’s teen girl inspired chat-bot named ‘Tay’, which had to be removed from the site a day later due to malicious content and data sources. When it comes to the applicability of AI systems across industries, there are infinite scenarios, and each of these would require rigorous amounts of testing, coupled with use of tools and analytics. The integration of new code based AI systems with the traditional code could also face several integration issues. So, one can easily conclude that these systems can’t replace humans completely.
Hence in short, AI systems are very much the need of the hour, but they have to be tested very patiently with all possible scenarios, else they can hinder with the privacy of the users- a worrisome issue that could have drastic consequences.
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© 2021 Wipro Limited |
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