Deus Ex Machina: Machine Learning Acts to Create New Business Outcomes
The term deus ex machina means “a god from a machine.” “Machine,” in this example, pertains to a crane that held a god over a theater stage in ancient Greek drama. Typically, the playwright would introduce an actor portraying a god at the end of his play who, from his elevated perch on the crane, would magically provide a resolution to an impossible dilemma to advance the plot to its end. Over the centuries “deus ex machina” has evolved to mean the intervention of unlikely saviors, devices or surprising events that bring order out of chaos in fast and often remarkable ways.
Today, machine learning is acting in much the same way. The technology is providing new and surprising solutions to seemingly unsolvable problems. And, in doing so, it’s producing insights that that are changing business outcomes as never before. It is turning the improbable to the probable – using predictive analytics. And, it’s enabling companies to march faster toward digital transformation.
Predictive Analytics Using Present Results
So, what is machine learning? Put simply, machine learning digitally processes data – millions upon millions of bytes of data – to run predictive models that learn from existing data and/or data generated in real time to forecast future behaviors, outcomes and trends. In turn, these “predictions” can make the applications or the devices you use smarter and more adaptable to both you and/or the context in which they are being used.
Each of us experiences the benefits of machine learning intelligently applied each day, whether it’s a recommendation engine used on an online shopping site or when your credit card is swiped and the transaction is automatically compared with a database to help your bank determine possible fraud.
The underlying software technology that makes machine learning possible is optimization algorithms. These dynamically leverage data from both sensors and intelligent devices. But because the conditions in which they operate are highly variable, the algorithms need to sense, respond, and adapt within broad parameters.
One example in our personal lives is automobiles. Using onboard computing, they automatically interact and respond to environmental data – outside objects, lights and weather conditions to name a few. On the business side, machine learning applications in logistics, industrial automation, utilities and security systems can let machines speak directly with other machines. Installed algorithms can evolve and adapt based on continuous data analysis, so that machine and/or system performance is constantly optimized based on operational parameters.
Continuous Learning Creates Consistent Success
Research from TDWI indicates that the number of companies planning to use machine learning is expected to triple over the next 3 years, bringing market penetration to over 50%. From speaking with Cisco customers, I think the research may understate the case, especially among companies with large data sets and good data quality. Machine learning has the potential to create so much value for corporations and users that it has the power to transform entire industries.
Machine Learning Secures Machines from Risk
Cisco is adopting machine learning in many ways both for itself and its customers. One of the most important areas is security. Unfortunately malware is ever increasing and ever changing. With 50 billion devices expected to be connected via the Internet of Things (IoT) by 2020, the network risk is exponential. One way to identify and stop malware is by analyzing the communications that the malware performs on a network.
Thanks to machine learning, network traffic patterns can be analyzed to identify the culprit. Cisco OpenDNS is a great example. Think of Pandora automatically learning from your music listening habits or Amazon learning your preferred shopping patterns, cloud-based, OpenDNS is constantly learning from new Internet activity to prevent malicious attacks. Its underlying algorithms are always adapting to live events. Rather than reverse engineering malware reactively, OpenDNS focuses on removing the biggest obstacle to blunting attacks — humans — by building machine learning systems that provide advanced threat protection before, during and after an attack.
The God in the Machine Moves to the Factory Floor
Moving from the network to the factory floor, Cisco is working with Mazak a world leader in innovative design and manufacturing of machine tools. It produces more than one-hundred models of turning and vertical machining centers.
Mazak asked us for a solution that would help them significantly improve machine efficiency for its customers. The answer is “SmartBox” powered by Cisco Connected Manufacturing software. The solution enables real-time manufacturing data and related analytics to be gathered from machines operating on the factory floor using software embedded on Cisco Industrial Ethernet (IE) 4000 switches.
The solution lets Mazak easily connect any off the shelf sensor to the system for machine data gathering. Advanced manufacturing cells and systems, along with full digital integration, can then achieve free-flow data sharing, i.e., process control and operation/equipment monitoring. Using Cisco Streaming Analytics, the company gains immediate visibility and insight to vital data produced by its manufacturing equipment. As a result, machine utilization is maximized and downtime is minimized through predictive maintenance.
Cisco edge analytics solutions provide a secure, end to end connection from any device to the cloud. In a manufacturing environment, this means that the software can listen to “hot” data produced by machines and provide real-time insights for operational decisions impacting product or operational requirements. The software can even monitor specific zones within a factory by residing on a larger computing system.
Are you currently using machine learning in your company? As all of us continue to push new boundaries with machine learning, sharing stories with each other is the fastest way to expand our efforts. I would love to hear yours!Tags: