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How to explain machine learning in plain English
What is machine learning? What is ML vs. AI? What data problems could ML solve for your organization? How does it improve security? Let's explore the key issues in plain terms
Machine learning's benefits: How to evangelize
Some explanation is likely necessary, but the longer-term task is better described as evangelism: Extolling the benefits of ML rather than getting into the technical nitty-gritty, as Fernandez advises above.
For many companies, a starting point in identifying those benefits – or the problems ML can solve – again comes down to data. This time, though, it’s a matter of the massive amounts of data so many organizations are generating. It’s getting harder and harder for humans alone to make sense of it on their own, at least in an efficient manner.
“The ability of machine learning models to deal with high-dimensional data is extremely helpful for businesses,” Brock says. “AI-enhanced software can do things such as finding patterns in user access data and accurately predicting customer retention, which would otherwise be impossible for a human to do.”
McCourt takes a similar tack when it comes to explaining the potential importance and value of data in the business world. It’s about making sense of data that might otherwise be indecipherable in a timely manner.
“In a business context, I usually talk about ML as a tool that can provide insights into complicated (or simple) circumstances,” McCourt says. “ML gives a mechanism to make smarter decisions by more completely and effectively using the data which is available to your company. It also gives a mechanism by which certain choices can be automated, freeing up resources for teams to make more complex decisions.”
Machine learning’s superpowers: Learning from practice, detecting patterns
When trying to help non-technical audiences understand the basic concepts of machine learning, McCourt likes to note that machines learn much like humans do: with practice.
“Machines need a structure in which to exist (a set of rules) a goal to achieve (to measure success and provide feedback), and opportunities to fail and learn (iterating over data),” McCourt says.
Finally, when evangelizing ML to a broad audience, McCourt finds success in emphasizing that machines have the potential to identify patterns that humans would not otherwise see. It’s not about machines replacing humans per se, but about creating new possibilities that didn’t exist previously.
“Machine learning is often applied with the goal of identifying new behavior and taking smarter action. At times, this can mean that tedious tasks on which a human would normally be wasted, such as flagging images or posts with offensive content, can now be delegated to a machine,” McCourt says.
In the past, writing rules (i.e., writing code) to teach a computer the parameters of what’s offensive and what’s OK would have been challenging at best, McCourt notes.
“Now computers can be shown a stream of content from which they learn how to make appropriate distinctions,” McCourt says. Longer-term, that’s hopefully a relatively mundane use case too. “Ideally,” he adds, “this goes even further than this example, allowing humans to leverage ML to solve problems that humans solve poorly or at great cost, such as diagnosing illnesses or predicting equipment failures.”
How do machine learning and security fit together?
As Mark Runyon, principal consultant with Improving, recently noted, "In 2020, the average cost of a data breach is $3.86 million worldwide and $8.64 million in the United States, according to IBM Security. The number of endpoints we must secure keeps multiplying as our technology stacks become more complex with microservices, IoT, and cloud services."
"CIOs can use the power of AI to combat craftier malware and phishing attacks. We can also use it to augment our security teams, enabling them to handle an ever-growing volume of threats."
"Malware and phishing attacks are growing more sophisticated. Malware authors are constantly producing new variations, ditching their old virus signatures to evade detection. It is the ultimate game of whack-a-mole as security professionals chase these ever-changing virus blueprints."
"Machine learning can help. By consuming the historical catalog of all known malware in the wild, it can pinpoint familiar behavioral patterns such as common file sizes, what is stored in those files, and string patterns tucked within the code. By identifying these fingerprints, new viruses, or variants of existing ones, can be shut down in real time," Runyon explains.
[ Read Mark Runyon's full article: How artificial intelligence (AI) and machine learning change security's future. ]
Machine learning's relationship to data science
While ML is a branch of AI, data science is the discipline of data cleansing, preparation, and analysis.
“At its core, data science is a field of practice and machine learning is a set of tools and methodologies,” says J.P. Baritugo, director at business transformation and outsourcing consultancy Pace Harmon. “Data science uses a broad array of expertise, business knowledge, tools, and methodologies to process big data to generate meaningful insights that drive actions and enable impactful business outcomes.”
Effective ML demands good data science. “A data scientist’s expertise is absolutely required to ensure machine learning is used and deployed properly,” Baritugo says. The data scientist may ensure the model is provided with the requisite amount of cleaned and normalized datasets for training and that the right algorithms are used based on the datasets and the business question to be addressed.
However, data science can be applied outside the realm of machine learning. “Data science is the practical application of artificial intelligence, machine learning, and deep learning – along with data preparation – in a business context,” says Ingo Mierswa, founder and president of data science platform RapidMiner.
“While the goal of data science is to extract insights from data, predict future developments, and suggest actions – sometimes even performing those actions automatically – this is achieved with tools like AI and ML,” Mierswa says.
At a basic level, a data scientist gathers and prepares data sets from multiple sources and then applies some capability to extract insight from them. In some cases, they may reach for machine learning. In others, a more basic analysis may make sense. “In my view at least, data science is just the manipulation of data,” says Wayne Butterfield, director of cognitive automation and innovation at ISG.
When to use machine learning
When facing a situation in which a solution is hidden in a large volume of data, machine learning is your friend. “ML excels at processing that data, extracting patterns from it in a fraction the time a human would take, and producing otherwise inaccessible insight,” Mierswa says.
For example, machine learning (informed by data science) powers risk analysis, fraud detection, and portfolio management in financial services; GPS-based predictions in travel; and product and content recommendations for Amazon and Netflix.
Machine learning is best matched with problems for which large amounts of well labelled historical data already exist, or for which data can be simulated very quickly. “There isn’t much mileage in using ML if you don’t have enough existing data that it can train on,” says Butterfield.
ML models are only as good as the quality of the data they learn from. “Luckily, there are many types of problems for which lots of data exist,” says Timothy Havens, the William and Gloria Jackson Associate Professor of Computer Systems in the College of Computing at Michigan Technological University and director of the Institute of Computing and Cybersystems
“Certain problems lend themselves to ML very well,” Butterfield explains. Data science (minus machine learning) has been applied to forecasting and planning for years with limited accuracy, for example. “However, now because you can now build complex algorithms that can take into account multiple data sources – such as weather, historic sickness patterns, external events, past demand – you get a much more accurate forecast,” Butterfield says. “And this isn’t just on a daily basis, it can be hourly too.”
In financial services, ML and data science can power solutions for underwriting and fraud prevention. In IT, they can improve network management. Healthcare organizations can apply them to improve diagnostic accuracy, determine optimal price and volume mix, or predict patient outcomes. In customer experience management, they come together to improve customer interactions, predict customer lifetime value, and estimate churn. Retailers can tap them to forecast demand, optimize pricing, and segment customers. In manufacturing, data science and ML can be used to automate the supply chain and improve planning.
[ How can automation free up staff time for innovation? Get the free eBook: Managing IT with Automation. ]
Editor's note: This article was originally published in July, 2019 and has been updated. Stephanie Overby contributed to this article.