Data science is shifting towards a new paradigm where machines can be taught to learn from data to derive conclusive intelligent insights. Artificial Intelligence is a disruptive technology that collates the intelligence displayed by machines mimicking human intelligence. AI is a broad term for smart machines programmed to undertake cognitive human tasks that require judgment-based decision making. With all the hype and excitement surrounding Artificial Intelligence, businesses are already churning data in massive quantities over call logs, emails, transactions and daily operations. Machine learning (ML) is a dynamic application of artificial intelligence (AI) that empowers the machines to learn and improve the model accuracy levels.
When producing a tiny threaded fastener or manufacturing fuselages, building a small calculator app or releasing an extensive enterprise software, an attribute that is most common, unwavering, and paramount is quality. The job or activity which ensures that products, software, or services delivered are of the highest quality, is one of the most important activities in the entire life cycle of building a product or service. In other words, testing and QA are critical and indispensable, However, the role and nature of testing have been ever-evolving and we already live in an era where the latest technologies are set to transform testing–software testing in particular. One of the key reasons for the emergence and prevalence of the stated technologies is that process efficiency and automation are no longer differentiating factors, but imperative for any organization. How can this transformation be achieved and what can be the chief ingredient infused to bring about this metamorphosis?
In an increasingly competitive world, we should have a deep understanding of the business in which we operate, how it is evolving, and the new innovations that we could embrace or build to remain competitive and conquer new market segments. To do this, we must be able to develop a clear vision of transformation that takes us to another level of performance. By embracing Digital Transformation, we will deal with artificial intelligence, machine and deep learning, virtual reality, and a lot of other innovative technologies. At first sight, it might even sound fearful to lead the business in such a complex and intricate direction. With this in mind, we will consider some strategies to better understand and take competitive advantage of the huge streaming of data in the current era of the digital revolution.
In the first article in this series, we discussed how humans have always desired to better understand the present and predict the future.1 The algorithms to help achieve this understanding have been around for decades, including even those of artificial intelligence (AI) approaches for enabling computers to reason about things that normally require human intelligence. However, only in recent years have we accumulated the massive digital data and developed the sufficiently powerful processors needed to put these AI algorithms to work on real human and business problems, with excellent performance and accuracy, on a broad scale. In this article, we describe some of the fundamental technologies and processes that enable enterprises to put AI to work to transform their businesses. In particular, we explain the concepts of data analytics, data science and machine learning, including deep learning.