“Machine learning techniques may have been used for years,” observes Lukas Biewald (@l2k), Co-founder and CEO of CrowdFlower, “but recently there has been an explosion in their applications.”[1] The reason for that explosion is clear; to work well, machine learning requires lots of data and today the business world is being flooded with data. In fact, so much data is being generated that it is virtually impossible for humans, without the aid of powerful computers, to analyze it in a timely manner. Lisa Morgan (@lisamorgan) explains, “Machine learning can identify patterns that humans tend to overlook or may be unable to find as fast in vast amounts of data. Organizations are using machine learning to make new discoveries, as well as to identify and remediate issues faster. … Today, it’s being used to fortify cybersecurity, ensure public safety, and improve medical outcomes. It can also help improve customer service and make automobiles safer.”[2] Biewald believes in the years ahead many more companies will find uses for machine learning. He writes:
“Gartner already puts machine learning at the top of its hype curve, and no: machine learning won’t replace all of your employees with computers or suddenly double your revenue. But that doesn’t mean that it can’t give every business a competitive advantage. There are plenty of business processes that can significantly benefit from machine learning.”
Raul Valdes-Perez (@RaulValdesPerez), Co-founder and CEO of OnlyBoth, notes that machine learning and machine discovery are often lumped together under the machine learning moniker.[3] He believes, however, there is a subtle difference between these two siblings that deserves clarification. “Machine learning is hot,” he writes. “Where it applies, it heatedly enables data-rich and knowledge-lean automation of valuable tasks of perception, classification and numeric prediction. Its sibling, machine discovery, deals with uncovering new knowledge that enlightens or guides human beings.” Both machine learning and machine discovery have their place in the business environment. Valdes-Perez continues:
“The key idea of machine discovery is that discovery is like other intellectual tasks. Thus, the key AI idea of heuristic search in problem spaces applies also to discovery tasks. On the other hand, the key idea of machine learning is that, given enough data with associated outcomes, together with notions of what data features are relevant to predicting those outcomes, software can be trained to make those associations in future cases.”
Another way of looking at it is that machine learning is good for situations in which autonomous decisions can made while machine discovery is good in situations requiring actionable insights before decisions can be made. Better decision making is a compelling reason for companies to employ machine learning and discovery. Bain analysts, Michael C. Mankins and Lori Sherer (@lorisherer), note that decision making is one of the most important aspects of any business. “The best way to understand any company’s operations,” they write, “is to view them as a series of decisions.”[4] Machine learning can help business decisions to be made more reliably, faster, and with a better understanding of the consequences. Ben Rossi (@BenRossi89) observes, “Organisations that realise value from their data assets faster through advanced analytics will quickly surpass their competition.”[5] He continues:
“When it comes to big data, the real opportunity for enterprises is in advanced data analytics, specifically machine learning. With this methodology, big data can be mined to automatically uncover business insights as well as generate predictive models. The ultimate scenario is one where machine learning can accurately guide forward-looking business decisions and reveal patterns never before seen. It is this promise of delivering accurate, actionable, predictive information that will drive machine learning to play a greater role in big data analytics, and make 2016 the start of the age of enlightenment for high-performance machine learning.”
As I noted in a previous article, we are entering the era of cognitive computing.[6] In this era, cognitive computing systems will use machine learning to power the enterprise. Dan Briody, Senior Editor of THINK Leaders, defines cognitive computing as “systems that learn at scale, reason with purpose, and interact with humans naturally.”[7] He continues:
“Cognitive computing systems aren’t programmed; they’re trained to sense, predict, infer and, in some ways, think, using artificial intelligence and machine learning algorithms that are exposed to massive data sets. These systems improve over time as they build knowledge and acquire depth in specialty areas or ‘domains’.”
Morgan provides us with a glimpse of how machine learning is already being used. She lists a few ways organizations are already taking advantage of machine learning capabilities:
Stopping Malware — Morgan notes that malicious files are generated “at a rate, humans and even signature-based security solutions can’t keep up, which is why machine learning and deep learning are necessary.”
Making Important Discoveries — Valdes-Perez notes there are three types of machine learning “engines” that can be involved in the discovery process: search engines, clustering engines, and benchmarking engines.
Understanding Legalese — Because cognitive computing systems employ natural language processing, they can use machine learning to translate technical language into plain language as well as be used for tasks like litigation discovery. Morgan notes that one system, Legal Robot, can translate legalese into plain language and it “can determine what’s missing from a contract and whether there are elements in a contract that shouldn’t be there.”
Preventing Money Laundering — Morgan reports, “PayPal is using deep learning to prevent fraud and money laundering at granular levels.”
Improving Cybersecurity — This may be one of the primary uses a machine learning in today’s business environment.
Competing Intelligently — Morgan notes that algorithms analyzing social media inputs are used to help bicycle racing teams understand how races like the Tour de France are unfolding in real time. Moneyball on a bike.
Getting Ready for the Driverless World — Morgan reports, “Seventy-four percent [of auto industry executives] expect that by 2025, vehicles will self-optimize and provide advice in context. Specifically, they’ll be able to learn about themselves, the surrounding environment, and the behaviors of the drivers and the occupants.”
Detecting Fraud — Anyone with a credit card knows that their service provider is watching their back using machine learning to detect fraud.
Improving Security Screening — Morgan notes, “Human screeners often overlook items that machine learning can identify. And, machine learning can easily adapt to seasonal changes affecting bag types and bag contents, or the specific requirements of a particular venue.”
Improving Customer Service — “Machine learning can improve the efficiency of customer service by understanding customers and their issues at a granular level.”
Urban Planning — “Cities around the world are using a crowd-sourced alternative to GoogleStreetView for city planning and to inventory roads and signage.”
Debugging Code — Adding to Morgan’s list, Katherine Noyes (@noyesk) reports that MIT has developed an algorithm that can debug code.[8] “Here’s yet another new application of machine learning,” she writes, “MIT has developed a system for fixing errors in bug-riddled code. The new machine-learning system developed by researchers at MIT can fix roughly 10 times as many errors as its predecessors could, the researchers say.”
Rossi concludes, “Machine learning is a fundamental tool in creating a world that can sense and react to dynamic, distributed phenomena. The number of variables and factors that can be taken into consideration by this methodology is unlimited.” Biewald adds, “As the algorithm becomes increasingly more accurate, the unit economics of your business process become better — and as the machine learning becomes able to handle more cases, the expensive humans are only called in on the toughest, rarest situations. That means you use the best of both human and machine intelligence in tandem: leveraging the speed and reliability of computers for the easy judgments and the fluency and expertise of humans for the difficult ones. And if that sounds like smart business, it’s because it is.”
Footnotes
[1] Lukas Biewald, “How machine learning will affect your business,” Computerworld, 20 November 2015.
[2] Lisa Morgan, “11 Cool Ways to Use Machine Learning,” InformationWeek, 4 December 2015.
[3] Raul Valdes-Perez, “Machine Learning Versus Machine Discovery,” TechCrunch, 17 November 2015.
[4] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[5] Ben Rossi, “Machine learning set to unlock the power of big data,” Information Age, 13 January 2016.
[6] Stephen DeAngelis, “The Era of Cognitive Computing,” Enterra Insights, 10 February 2016.
[7] Dan Briody, “New Vocabulary: Cognitive Computing,” THINK Leaders, October 2015.
[8] Katherine Noyes, “Turns out machine learning is a champ at fixing buggy code,” InfoWorld, 3 February 2016.
