Much of the current discussion about artificial intelligence (AI) actually involves a sub-set of AI called machine learning. Erika Morphy explains, “Machine learning is the use of computing resources that have the ability to learn without being explicitly programmed — that is, acquire and apply knowledge and skills that maximize the chance of success.”[1] It’s no coincidence machine learning has matured as the amount of data being generated has increased. The more data machine learning platforms have to learn from the better. Machine learning is the preferred term (rather than AI) because as, Marykate Jasper writes, “Machine learning … is ultimately ‘pattern recognition masquerading as understanding’.”[2] There’s absolutely nothing wrong with that. “Now that we have such amazing data sets,” Jasper writes, “algorithmic learning is far more effective.” The following video demonstrates why discussing machine learning can be difficult.
Some basics about machine learning
As I noted at the beginning of this article, machine learning is a sub-set of AI. Cynthia Harvey explains, “All machine learning systems are AI systems, but not all AI systems have machine learning capabilities.”[3] With that bit of trivia out of the way, Harvey goes on to note there are four types of machine learning. They are:
- Supervised Learning: “Supervised learning requires a programmer or teacher who offers of examples of which inputs line up with which outputs.”
- Unsupervised Learning: “Unsupervised learning requires the system to develop its own conclusions from a given data set.”
- Semi-supervised Learning: “Semi-supervised learning, as you probably guessed, is a combination of supervised and unsupervised learning.”
- Reinforcement Learning: “Reinforcement learning involves a system receiving feedback analogous to punishments and rewards.”
Other terms you often hear or read about during machine learning discussions are:
Neural Networks. “Neural networks,” explains Eric Knorr (@EricKnorr), “are a form of machine learning dating back to early AI research. They very loosely emulate the way neurons in the brain work — the objective generally being pattern recognition. As neural networks are trained with data, connections between neurons are strengthened, the outputs from which form patterns and drive machine decision-making.”[4]
Deep Learning. “The term Deep Learning (DL),” writes Paramita Ghosh, “is a specialized subfield of Machine Learning that can enable software systems to self-train for the performance of particular tasks. Thus AI is the parent technology with ML as a child, and DL probably as a grandchild. Machine Learning is essentially an intermediary between Artificial Intelligence and Deep Learning.”[5] “In most cases,” Knorr adds, “deep learning refers to many layers of neural networks working together.”
It should be remembered that machine learning has no common sense. For example, several years ago Eric Blattberg (@EricBlattberg) reported, “When deep learning startup AlchemyAPI exposed its natural language processing system to the Internet, it determined that dogs are people because of the way folks talked about their pets. That might ring true to some dog owners, but it’s not accurate in a broader context. That hilarious determination reflects the challenges — and opportunities — inherent to machine learning.”[6] To overcome this challenge at Enterra Solutions®, we can pass machine learning results through an ontology. The ontology knows dogs aren’t people and would immediately invalidate that conclusion. The following infographic provides a few more details about machine learning.
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Summary
Machine learning algorithms touch our lives in numerous ways. As the video notes, machine learning helps companies determine what we like to buy and watch, what price they should charge for their goods or services, as well as providing them with numerous other insights. Machine learning helps autonomous vehicles become better drivers. Harvey offers the following list of machine learning use cases:
- Fraud detection
- Recommendation Engines
- Search
- Video surveillance
- Handwriting recognition
- Natural language processing
- Customer service bots
- IT security
- Streaming analytics
- Predictive maintenance
- Anomaly detection
- Demand forecasting
- Logistics
- Financial trading
- Healthcare diagnostics
- Self-driving cars
- Robots
Harvey also lists some of the benefits of machine learning:
- Speed — “Humans can create the models, input the data and run the calculations necessary for predictive analytics on their own. However, humans — or humans using software without AI capabilities — might need days, weeks or months to accomplish tasks that machine learning tools can complete in just seconds, minutes or hours.”
- Accuracy — “That speed allows machine learning systems to utilize a larger volume of data and a larger number of models than humans ever could. As a result, AI systems are much better than people at some tasks, such as predictive analytics. However, in other areas, such as voice recognition or image recognition, computer systems still haven’t achieved the same level of accuracy as human beings.”
- Efficiency and cost savings — “Machine learning software isn’t cheap; in fact, in some cases it can be very expensive. However, it is often far more affordable to use software to automate a chore than to hire dozens or hundreds of people to complete the same task.”
By now it should be obvious that machine learning is not a passing fad despite the fact Gartner put machine learning at the peak of its most recent Hype Cycle for Emerging Technology. Harvey reports, Gartner “predicted that by 2020, artificial intelligence technologies, including machine learning ‘will be virtually pervasive in almost every new software product and service.’ According to IDC, organizations [spent] $12.5 billion on AI systems in 2017. That’s a huge 59.3 percent increase over 2016 levels, and the analysts say that spending will continue to grow at more than 50 percent per year through 2020.” Those should be reasons enough for your company executives to learn all they can about machine learning.
Footnotes
[1] Erika Morphy, “How to Differentiate Machine Learning From Dressed-up BI,” CMS Wire, 2 January 2018.
[2] Marykate Jasper, “How Do Machines Learn?” The Mary Sue, 31 December 2017.
[3] Cynthia Harvey, “What is Machine Learning?” Datamation, 3 January 2018.
[4] Eric Knorr, “Making sense of machine learning,” InfoWorld, 6 March 2017.
[5] Paramita Ghosh, “Is Machine Learning Ready to Take on Artificial Intelligence?” Dataversity, 9 February 2017.
[6] Eric Blattberg, “Cognitive computing is smashing our conception of ‘ground truth’,” Venture Beat, 20 March 2014.
