Machine learning is about to become a common part of enterprise software; and for it to be effective, real data is needed. "Data is the fuel for machine learning, as you need data to train algorithms," said Markus Noga, SAP SVP of Machine Learning, in an interview with Networks Asia. "Depending on the quality of the underlying data, ML can even outperform human performance (e.g. in image recognition). Hence, enterprises need to capture, prepare, and clean data in order to build intelligent models. Moreover, ML requires real data and cannot be effectively trained with generated (or fake) data."

Artificial intelligence is the buzzword for 2018 and it is being used everywhere and often to describe fairly mundane automation and analytics-driven processes. One of the earliest adoptions has been speech to text, for example in call centres. AI enables the speech of both the call centre operator and the customer to be converted into text files and then an algorithm scans that text looking for keywords that may indicate an area of risk for the enterprise or sentiment analysis of the customer. "All AI today is narrow-focused, in other words, we have a system and we give it a specific challenge or task, a series of input data and a whole bunch of training data which is usually labelled or pre-classified," said Charles Sevior, CTO Unstructured Data Solutions, APJ and Greater China, Dell EMC, in an email interview with Networks Asia. "We are at the early stages of having computers accurately interpret unstructured data in a time so fast that it is considered to be "just like a human" – so-called AI."

Technology has accelerated human progress to unprecedented levels. Three centuries ago, the first industrial revolution altered society with the introduction of machines that have since become factory fixtures and key instruments in the manufacturing process. Today, technology is once again bringing humanity to the precipice of a new era, typified by the rapid digitalization of core operations across sectors. Now, more than ever, technology is taking center stage and transforming work in the new digital economy. While technologies were mostly relegated to support functions in the recent past, the age of the digital economy redesignates the role of modern technology as a business enabler, with progressive technologies such as blockchain, the Internet-of-Things, Big Data, Virtual Reality, and Artificial Intelligence (AI) quickly altering work paradigms in modern enterprises across a multitude of industries. The rising number of progressive technology use cases each day translates to a pressing need to reassess companies' fundamentals.

Machine learning (ML) is fast becoming a litmus test for forward-thinking CIOs. Companies that fail to adopt machine learning for product development or business operations risk falling behind more nimble competitors in the coming decade. That's according to Dan Olley, who as the CTO of Elsevier, the scientific and health information unit of RELX Group, has ratcheted up his organization's adoption of ML technologies in recent years. "I fundamentally believe that we are at a tipping point with machine learning and it's going to change the way we interact with the digital world over the next decade," Olley told an audience of his peers last month at the CIO100 Symposium in Colorado Springs, Colo. "We're going to have decisions increasingly made by machines."

On May 11, 1997, a computer showed that it could outclass a human in that most human of pursuits: playing a game. The human was World Chess Champion Garry Kasparov, and the computer was IBM's Deep Blue, which had begun life at Carnegie Mellon University as a system called ChipTest. One of Deep Blue's creators, Murray Campbell, talked to us about the other things computers have learned to do as well as, or better than, humans, and what that means for our future. What follows is an edited version of that conversation. Is it true that you and Deep Blue joined IBM at the same time?

On May 11, 1997, a computer showed that it could outclass a human in that most human of pursuits: playing a game. The human was World Chess Champion Garry Kasparov, and the computer was IBM's Deep Blue, which had begun life at Carnegie Mellon University as a system called ChipTest. One of Deep Blue's creators, Murray Campbell, talked to us about the other things computers have learned to do as well as, or better than, humans, and what that means for our future. What follows is an edited version of that conversation. Is it true that you and Deep Blue joined IBM at the same time? A group of us, including myself, joined IBM from Carnegie-Mellon University in Pittsburgh in 1989, but we didn't come up with the name Deep Blue until about a year later.

A majority (84 per cent) of companies polled for a study see the use of artificial intelligence (AI) as "essential" to competitiveness, with a further 50 per cent seeing the technology as "transformative," according to Tata Consultancy Services' Global Trend Study titled, "Getting Smarter by the Day: How AI is Elevating the Performance of Global Companies." Exploring the views and actions of decision makers from global companies with average revenues of $20 billion, the study revealed AI is spreading across almost all areas of a company. Asia Pacific companies reported an average 19 per cent increase in revenue stemming from AI. AI spend in the region is expected to hit US$57 million this year. The biggest adopters of AI today are, not surprisingly, IT departments, with two-thirds (67 per cent) of survey respondents using AI to detect security intrusions, user issues and deliver automation. However, by 2020, almost a third (32 per cent) of companies believe AI's greatest impact will be in sales, marketing or customer service, while one in five (20 per cent) see AI's impact being largest in non-customer facing corporate functions, including finance, strategic planning, corporate development, and human resources.