Machine learning is driving trends that will blur the lines between digital and human worlds.
This era of symbiotic technology – enabled by combining AI, device miniaturization, 5G and an API-rich ecosystem – will untether us from screens and keyboards.
And as we think about training rather than programming these systems, new problems around bias and ethics will arise that we need to consider carefully.
These were some of the ideas Dan Olley talked about in the May 29 issue of Forbes. In “A CIO Hall of Famer’s Approach to Machine Learning,” technology author Peter High interviewed Elsevier’s CIO and Executive VP of Product Development about tech trends and their applications at Elsevier and beyond. They spoke in depth about machine learning, with High pointing out that his subject “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.”
At Elsevier, Olley’s main focus is on building electronic products and services: “Think of it more like Google and Amazon or Facebook building a technology solution than a more traditional company,” he said.
To that end, he’s constantly examining the latest technologies and how they can be applied to make work easier and more productive for our users, who range from individual researchers and clinicians to universities, hospitals, pharmaceutical companies and government funding agencies.
People in all fields are grappling with information overload, he said, and technology can help them find the information that’s most relevant to them:
We are using artificial intelligence and machine learning to understand what a researcher is working on, how they are working, and provide relevant information to them. People do not want articles to read, they want knowledge. They want the right bit of information at the right time. How do we service that within their discipline? Importantly, we know that a lot of the most significant breakthroughs are going to come from cross-disciplinary research. How do we bring in stuff from other disciplines that are relevant to what they are doing?
Because much of the information comes from unstructured data, like medical images and videos in scholarly journals, he explained, Elsevier’s technologists use deep learning (a subset of machine learning) to extract information and put it into a context that’s meaningful to the user. They also build adaptive learning systems to help nursing students master their coursework and pass board exams.
As for AI’s role in the future, Olley sees its power in combination with the human element:
I do believe that the next era of this evolution is going to be around augmented intelligence rather than artificial intelligence. It is going to be about the human (plus) machine system being greater than the sum of the parts. This will be the doctor with an advisory machine saying, ‘Have you thought of this?’ ‘Have you seen this?’ ‘Is this relevant?’ This will be like getting a (real-time) second opinion.
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