It’s understandable if you’re not familiar with “microspheres.” They weren’t invented until 1968, and even then, Spencer Silver had a hard time convincing people about the merits of his discovery. It wasn’t until 1979 that the company he worked for as a chemist, 3M, officially released a product to the world based on his innovation. In 1968, Mr. Silver had simply been trying to create a better adhesive. In 1979, 3M released the first iteration of the Post-It. Billions and billions of yellow pads later, the fabled office product—famously described by its inventor as the “product nobody thought they needed until they did”—is inarguably ubiquitous. This is just one of the many great examples history offers us about “unintentional” product successes—those unexpected byproducts of innovation that become innovations in their own right. Here’s another one: Perhaps the most famous modern example of an innovation byproduct has to do with a certain budgetary move:“Bob Taylor convinced ARPA’s Director Charles M. Herzfeld to fund a network project in February 1966, and Herzfeld transferred a million dollars from a ballistic missile defense program to Taylor’s budget.“ Could they have known they had just given birth to the modern internet? The modern history of technological innovation is rich with stories of future-facing dreamers who both did—and didn’t—foresee the long-term impacts of their achievements. When the early pioneers of artificial intelligence began their work in the mid-20th century, the scope of their goals was already remarkably expansive, if perhaps naive to the reality of the challenges they would ultimately face. The question of what constitutes a legitimately “intelligent” machine was notably posed in 1950, when Alan Turing proposed his now-famous (or infamous, depending on your perspective!) Turing Test. The test was a variation on something called the imitation game “in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players.” In the Turing Test, the two entities are a human and a machine. According to Turing, if the evaluator, in reviewing the responses, cannot accurately determine which entity is the machine, then the machine can be said to have passed the test; thereby affirming its “intelligence.” The test’s infamy has to do with the fact that no machine has ever passed the test, and to some, this has served to set the field up for a sense of failure. Yet while the goal of Artificial General Intelligence (AGI) remains elusive, the ongoing quest to advance the field of AI has produced a vast array of impactful byproducts; these include everything from the technologies behind Alexa and Siri to the algorithms that power Amazon and Netflix, and from smart Roombas and the Nest Learning Thermometer to mobile check deposits and loan approvals. In actual practice, much of the true power of AI has been in automating those functions that we either don’t want to do, or that we can’t do. Today, as we stand on the cusp of becoming a genuinely AI-powered world, we can’t help but wonder where else AI might have an impact. Certainly, self-driving technology stands to revolutionize many fields—the trucking industry, for example, just reported that, “On Thursday, July 8, Starsky Robotics and Loadsmart issued an announcement about the trucking industry’s first autonomous dispatch and delivery.” Finance is another field actively being transformed by AI’s ability to automate. As reported by The Telegraph: “According to financial research firm Autonomous, artificial intelligence (AI) will introduce more than $1trillion (£782bn) in cost savings by 2030.” AI-powered automation has both physical and virtual applications. On the physical side, today’s manufacturing industry is being wholly transformed by automation. On the virtual side, AI’s impact is being felt across essentially every industry that relies on big data. One of the most compelling arenas for AI innovation is the field of fraud prevention. The situation is a unique one, in that both good and bad actors are locked in a race to see who can leverage the latest technological developments first. As a result of this high-stakes competition, innovations can come at blinding speed, and as with so many of the innovations described above, there is a great deal of cross-industry “borrowing” going on, and what might be seen as byproducts from one industry emerge as primary components in another field. Unsupervised machine learning (UML) is a good example. Initially deployed as one of many techniques for managing unlabeled data, UML today represents fraud prevention’s best opportunity yet to stay ahead of modern fraudsters who are agile, adaptive, and able to vary their techniques with great rapidity. The World Economic Forum has coined a term for the technologically-powered era we are now entering: The Fourth Industrial Revolution. As they describe it:“The First Industrial Revolution used water and steam power to mechanize production. The Second used electric power to create mass production. The Third used electronics and information technology to automate production. Now a Fourth Industrial Revolution is building on the Third, the digital revolution that has been occurring since the middle of the last century. It is characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres.” In this era, as with those that have preceded it, our innovators will pursue lofty goals. Along the way, many new “byproducts” will emerge—unintended inventions that will become part of our daily lives. In an AI-powered world, what might we see next? View posts by tags: AI | Innovation Related Content: DataVisor Introduces dCube A Data Breach is Just the Beginning AI-Powered Solutions, AI-Empowered Clients Stay up-to-date on the latest fraud insights and intelligence. 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