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.
winningAlgorithms uses social media feeds to keep cyclists in the Tour de France informed about the status of the race five minutes before the media broadcasts the same information.
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?
No. That's not true, according to Dan Olley, CTO of Elsevier, a global information solutions company. "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."
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."
So, unless they are dog lovers, why should CIOs care?
To Olley, machine learning fills a gap in technology that has existed for a long time: solving complex problems with pattern recognition. "With the majority of Elsevier's revenue coming from technology-based products and services, we started using machine learning in our commercial products, but it's equally applicable to internal IT platforms," Olley says. "Take the classic challenge of matching customer contacts and addresses or spotting trends in your financial data. The more you train the application to 'understand' the data, the better your predictive analytics."
At Elsevier, Olley puts his money where his mouth is.
"One of our divisions creates educational materials for nurses, but many of our students get frustrated with the challenging material, drop out of the course, and never take their certification exam," he says.
Elsevier would like to increase the number of nurses who pass the test, and they use machine learning to help. "We are using algorithms that learn how students actually use the course material," he says. "This way, we can create adaptability and personalization within the course to engage the students and drive better pass rates."
Education is just one example of how Elsevier uses machine learning, and it finds that the technologies are applicable across product suites, from helping scientists make new discoveries to supporting healthcare professionals so they can provide the best possible care.
If CIOs can take a body of information, such as a CRM system, a call center app, or even a corporate Intranet, and from that information construct a set of data to teach a machine how to solve a problem, they will create much more powerful, adaptable systems.
