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Demystifying AI

Demystifying AIAsk any IT executives about the hottest enterprise IT topic and the sexiest job in 2017, the answers are like to be artificial intelligence (AI) and data scientist.  But according to technology providers and academics, AI is nothing new and data scientists have become a less sexy job.

The sci-fi concept of machine mimicking human’s cognitive functions was an academic discipline since 1956. But in recent years it is starting to realize, to some degree, in the business environment. According to IDC, machine learning (ML) and AI will produce 30-40% cost savings and productivity improvements in operations management by 2020.

In Asia, more CIOs are also looking to adopt AI. Gartner’s CIO Agenda survey indicated 37% of Asia Pacific IT leaders noted they have deployed or are in short-term planning to deploy AI for its business environment.

Machines do not learn

Despite the enthusiasm and recognition in the market, executives from analytic software provider SAS noted that the popularity of AI simply reflects the advancement in data modeling and processor technologies.

“AI is based on sets of models and machines don’t really learn,” said Jim Goodnight, CEO and co-founder of SAS. “People that write about machine is learning are caught up with the [concept of] neural networks, which is basically applying models.”

At the company’s recent user conference SAS Analytics Experience 2017 in Amsterdam, Goodnight said analytics is about fitting data in models and AI is simply using new types of modeling to provide predictions.

“AI is the computerization of what human could do,” added SAS CTO Oliver Schabenberger. “Algorithm helps to do the job better when problem is too big for us. We consider AI is just an extension of what we’ve been doing in analytics for the past 41 years.”

The new types of modeling and extension of analytics in AI that Goodnight and Schabenberger referred to is the application of artificial neural networks (AAN), which are computing systems inspired by biological neural networks, to develop deep learning algorithms.

These algorithms enable systems to progressively improve performance (i.e. learning). With the use of ANN, the system is able to improve faster and better than using the traditional computing algorithm using rule-based programming.

Schabenberger added that deep learning allows SAS to reexamine its existing analytics offerings like sentiment analysis with “new lenses.”

“AI technology today based mostly on deep learning, so we have heavily invested in that area and we’ll release our deep learning solutions and tool kit at the end of this year with support of GPU,” he said.

Specialized to process image, graphic processing unit (GPU) has extended from processing images in online games to enabling deep learning neural networks. Multiple studies, including a research from Hong Kong Baptist University, have indicated GPUs are achieving significant speed over CPUs in processing deep learning neural networks. The development in computing power is also another major reason for the popularity of AI.

Analytics 4.0

In addition to the development in data modeling and processors technologies, the rise of AI is also a reflection of autonomous analytics in Analytics 4.0, according to academics, authors and consultant Tom Davenport.



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