As promising as machine-learning technology is, it can also be susceptible to unintended biases that require careful planning to avoid. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Many companies are turning to machine learning to review vast amounts of data, from evaluating credit for loan applications, to scanning legal contracts for errors, to looking through employee communications with customers to identify bad conduct. New tools allow developers to build and deploy machine-learning engines more easily than ever: Amazon Web Services Inc. recently launched a "machine learning in a box" offering called SageMaker, which non-engineers can leverage to build sophisticated machine-learning models, and Microsoft Azure's machine-learning platform, Machine Learning Studio, doesn't require coding. But while machine-learning algorithms enable companies to realize new efficiencies, they are as susceptible as any system to the "garbage in, garbage out" syndrome.
While walking around the larger industry shows, those hosting say more than 140 vendors, it doesn't take long to realise that artificial intelligence and machine-learning are the current'it' girls of the cyber-security industry. In an effort to define what'artificial intelligence' actually is, Luger & Stubblefield described in their 2004 book on artificial intelligence, that an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximise its chance of success at some goal based on a complex set of calculations. As notifications from UBA, SIEM and threat intelligence systems continue to grow, artificially intelligent systems are being touted as the solution to the fatigue experienced by SOC teams who have to try and figure out what to do with each threat, and whether or not they should investigate it further. Research from security company Hexadite, a security automation company, claimed that 37 percent of cyber-security professionals face 10,000 alerts per month" with 52 percent of alerts turning out to be false positive. He responded: "Highly repetitive and intricate tasks may be well suited for a machine rather than a human.
Breakthroughs in the application of complex calculations to large volumes of data have enabled machine-learning methodologies to revolutionize business processes in nearly every industry. Some of the more recognized examples of machine-learning applications include personalized Netflix recommendations and related product modules from online retailers such as Amazon and Nordstrom. However, there are less sexy yet equally impactful machine-learning examples, which include revenue management solutions used in hotels that incorporate these methodologies into an algorithmic engine to help produce pricing and inventory recommendations. Unlocking the potential of machine learning for the office of finance remains a hot topic for financial planning and analysis (FP&A) leaders, industry analysts, and technology vendors alike. Even more specifically, continuous chatter surrounds the ways that machine learning can improve future FP&A processes and how finance leaders can prepare for deploying advanced analytics within their organizations.
As a young brand manager at Miller Brewing Company in 1995, I crunched data using Excel spreadsheets, a process not so far away from what's going on at a lot of companies today, I'd wager. What does AI have to do with marketing--a human-to-human endeavor if there ever was one? Consider this noteworthy bullet point in Gartner's "Top Predictions for IT Organizations and Users for 2016 and Beyond." When most people envision AI, they think of game show-playing computers, self-driving cars, or robot armies. Robotics is at one end of the AI spectrum; at the other is what's referred to as "machine learning," the ability to program a computer to recognize patterns and build models that let it make decisions or generate predictions.
Almost a third of all human deaths in the world are caused by cardiovascular disease (CVD), so encouraging news about breakthrough developments in diagnoses powered by machine-learning is enough to make your heart skip a beat. Traditionally, healthcare providers are sluggish to embrace technology and, in addition, the industry is highly regulated, understandably so as people's lives are at stake. However, the growing evidence that machine-learning should be fully utilised is becoming overwhelming. The meshing of big data and machine-learning enables much speedier and accurate diagnostic processes. It is already proving life-saving in healthcare in general and for those with CVD specifically as related conditions can be identified early, and impending strokes and heart attacks spotted in advance.