Artificial Intelligence (AI) and Machine Learning (ML) are receiving a lot of press — both good press and bad. Most of the bad press is reserved for artificial general intelligence and its potential for becoming a terminator-like technology that could threaten humankind’s future. Most AI in use these days is more benign and focused on addressing specific business, medical, or scientific challenges. Laura Mauersberger (@LauraMauersberg), a Communications Manager at LeanIX, notes, “Artificial Intelligence and Machine Learning are two trending topics of the moment, even [though] these are very different, many sources seem to use them interchangeably. … Both terms pop up frequently nowadays, especially when discussing Big Data, analytics and other hot topics.”[1] Although AI and ML might be “very different,” they are also related. Jeff Catlin, CEO of Lexalytics, explains, “The difference between ML and AI is the difference between a still picture and a video: One is static; the other’s on the move. To get something out of machine learning, you need to know how to code or know someone who does. With artificial intelligence, you get something that takes an idea or a desire and runs with it, curiously seeking out new input and understandings.”[2]
Machine Learning and Artificial Intelligence
Mauersberger notes, “Artificial Intelligence is the broader concept of machines that are able to carry out tasks in a way we would consider ‘intelligent’.” In contrast, “Machine Learning is one of the current applications of Artificial Intelligent based on the idea that we should be able to give machines data access and with that information, machines should be able to learn more themselves.” In other words, ML is a branch of AI. Catlin notes, “Machine learning is a step up from coding. In the words of Bill Kish of Cogniac, you’re ‘programming with data,’ such as images and video, not code. Essentially, it’s about building models. You select a training set, choose what reflects a positive or a negative for that set, then select a type of model to use.” Daniel Tunkelang (@dtunkelang), who led ML projects at Endeca, Google, and LinkedIn, adds, “Machine learning lives up to the hype: there are an incredible number of problems that you can solve by providing the right training data to the right learning algorithms. Call it AI if that helps you sell it, but know that AI is a buzzword that can mean whatever people want it to mean.”[3]
Tunkelang’s point is that ML is useful and is already being applied in numerous business settings. There is another term in popular use — cognitive computing — that needs to be defined. I define cognitive computing as a combination of semantic reasoning (i.e., the use of ML, natural language processing, and ontologies) and computational intelligence (i.e., advanced analytics). Cognitive computing systems are able to tackle ambiguous problems other computing platforms can’t. Jenna Hogue explains, “The types of problems [involved in cognitive systems] … tend to be much more complex and human-like than the average non-cognitive system. These problems tend to comprise multiple different variables included, shifting data elements, and an ambiguous nature.”[4]
Uses for Machine Learning
Tunkelang observes, “Machine learning isn’t as simple as ‘if you build it, it will learn.’ You need to make sure you’re giving it the right input and that you’re applying the right model. Your data is your cake mix, and your model is your method. Get it right, and you can have your cake and eat it, too. Get it wrong, and you’ll have to start over.” Alex Styl (@alexstyl), a coder at Novoda, notes, “Machine Learning is the kind of technology that allows machines to make sense of … data and extract useful information out of it.”[5] He adds, “Voice recognition, face recognition, text recognition are only some possibilities that are enabled through Machine Learning. It is a technology that can allow our devices to get a better understanding of our world, our actions and intentions of using them, allowing us that way to design and build better context-aware applications.” Machine learning can be so useful, Clint Boulton (@ClintBoulton) declares, “Companies that fail to adopt machine learning for product development or business operations risk falling behind more nimble competitors in the coming decade.”[6]
Styl reports ML is being broadly used in the business world. “Machine Learning,” he writes, “has been used in various different industries for various purposes. We see Machine Learning enabled applications in the fields of medicine, robotics, and even in various art projects. Smart cars use Machine Learning in order to maintain the car on the road, make it understand car signs, pedestrians and get an understanding of its surroundings.” The basic rule of thumb is this: If you have data from which you want to extract insights, put ML to work. Dan Olley, CTO of Elsevier, believes, “We are at a tipping point with machine learning and it’s going to change the way we interact with the digital world over the next decade. We’re going to have decisions increasingly made by machines.”[7] Tunkelang offers the following advice: “Data is the key ingredient that makes machine learning possible. You can have machine learning without sophisticated algorithms, but not without good data. … Unless you have a lot of data, you should stick to simple models. … The phrase ‘garbage in, garbage out’ predates machine learning, but it aptly characterizes a key limitation of machine learning. Machine learning can only discover patterns that are present in your training data.” Jeff Bodenstab agrees with Tunkelang that ML is all about the data. He writes, “Machine learning relies heavily on the availability of data and computing power. Rather than make a priori assumptions, machine learning enables the system to learn from data. Rather than following preprogrammed algorithms, it uses the data to build and constantly refine a model for making predictions. It helps understand demand volatility by capturing and modeling attributes that shape the demand. It learns from the data, and modifies operations accordingly.”[8] At this point, you might be asking: To what kinds of challenges can ML be applied? A few examples of how ML can be used include:
– Electricity load prediction based on weather conditions.
– Algorithm based credit approval for bank loans.
– Fraud detection and other applications in financial services.
– Migration trends based on geo tagged twitter feeds.
– Diagnostics and patient management in health care.
– Criminal justice.
– Media and knowledge management.
– Transportation and traffic management.
– Weather monitoring.
– Sustainability and wildlife conservation.
Summary
Mauersberger concludes, “The possibilities of Machine Learning are infinite… and ‘thanks’ to mainly sci-fi movies, we also have a lot of expectations for them, mainly being able to communicate with electronic devices as if there were real human beings.” If you have a business and have access to data, there is probably a business case that can be made for putting that data to use using ML.
Footnotes
[1] Laura Mauersberger, “The difference between Artificial Intelligence & Machine Learning,” LeanIX, 27 June 2017.
[2] Jeff Catlin, “What’s The Difference Between Machine Learning And Artificial Intelligence?” Forbes, 20 October 2017.
[3] Daniel Tunkelang, “Ten Things Everyone Should Know About Machine Learning,” Forbes, 6 September 2017.
[4] Jenna Hogue, “Cognitive Computing: The Hype, the Reality,” Dataversity, 12 January 2017.
[5] Alex Styl, “In plain words, Machine Learning,” Novoda, 5 October 2017.
[6] Clint Boulton, “10 tips for getting started with machine learning,” CIO, 12 September 2017.
[7] Ibid.
[8] Jeff Bodenstab, “Traditional Statistics versus Machine Learning. What’s the Difference?” ToolsGroup, 19 September 2017.
