These four recommendations can help prepare you -- or the novice analyst on your team -- for a career in this burgeoning field. When Aurora Peddycord-Liu started as an analytical education intern at SAS in the summer of 2017, she came with a solid educational background from Worcester Polytechnic Institute and NC State's computer science Ph.D. program. These programs prepared her well for her current position at SAS, where she uses data to derive actionable insights on the design and use of SAS e-learning courses, but she's had to adapt her skill set to face the challenges of a real-world analytics position. To learn how newbie analysts can prepare for their work in this hot new age of machine learning, I spoke with Peddycord-Liu and senior executive, Dan Olley, global CTO at Elsevier. Recommendation #1: Don't be overwhelmed -- just get started Don't be intimidated by the powerful tools at your disposal; find a point to start and dive in.

Few innovations are discussed in the same way as artificial intelligence. Governments see it as vital to the future of their security and economy. The media sees it as both savior and existential threat. And academia sees it as something in between: a tool with some very useful applications. Overall, none of these groups seems to agree on what it is. When a team of data analysts at Elsevier set out to analyze the state of play and map the trends of AI research for an in-depth report, they had significant challenges to overcome – not least of which was to decide what AI is.

Dan Olley was recently named to the prestigious CIO Hall of Fame by CIO Magazine. In many ways, however, Olley has not been a traditional chief information officer. For one, he has largely held chief technology officer roles. Moreover, he has also had customer-facing, product-centric roles. In his current role as Chief Technology Officer and Executive Vice President of Product Development of Elsevier, his purview is quite broad. Elsevier is a subsidiary of RELX Group, focusing on academic and clinical research. In his role, Olley helps develop solutions to help academics and clinicians train, while enhancing their ability to help patients at the bedside. Olley's focus in recent years has been in machine learning. In fact, he has been immersed in the subject long enough that this insights into its use, the value derived from it, the implications on teams, and the like are unusually deep.

Dan Olley was recently named to the prestigious CIO Hall of Fame by CIO Magazine. In many ways, however, Olley has not been a traditional chief information officer. For one, he has largely held chief technology officer roles. Moreover, he has also had customer-facing, product-centric roles. In his current role as Chief Technology Officer and Executive Vice President of Product Development of Elsevier, his purview is quite broad.

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."

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."

Heller goes on, "For years, computing has been stuck in an'if/then' paradigm. 'Computers are good at A B, or A B, but they are bad at A is similar to B' says Olley. 'Until now, only humans could handle'similar to' situations, but with machine learning, we can train algorithms to perform highly complex functions from describing an image to making judgement calls.' Say you want to sort and categorize all of your digital photos. 'If every picture of a dog were identical, it would be easy for an application to recognize dog photos and tag them appropriately,' Olley says. 'But dogs are not identical to one another, so the machine needs to see a series of photos labelled'dog' until it learns to recognize dogs in the abstract. But once it's trained, the machine can sort those photos on its own'."

Cornell University wants to help whales avoid getting hit by ships, so it is working on an algorithm that uses audio recordings to alert ships to the whales' whereabouts. Dassault Systèmes is creating a 3D model of a human heart that will allow surgeons to test the performance of pacemakers before opening up patients. Sure, machine learning has already had a significant impact on the worlds of science and culture, and in life, but it will be years before CIOs need to start worrying about enterprise machine learning applications ... right? "If CIOs invested in machine learning three years ago, they would have wasted their money," Olley says. "But if they wait another three years, they will never catch up."