Machine Learning sounds like something out of an 1980s movie about a deceiving robot we end up fighting against to defend the existence of the planet. That may explain the distrust people have with it. How understandable is this distrust? Computers monitoring and absorbing information, learning and adapting to become better? For an unknown end? Add to this distrust the purported technological singularity: the moment when artificial intelligence surpasses the human in any practical competency. Will they write better music than Mozart? The prospect feels grisly… but what if it is beautiful in ways we cannot guess today?
Reality is a lot less dramatic. We are not yet at a level where robots are capable of learning behaviors and emotions the way humans do. Talking about Machine Learning today is a less colorful discussion. It involves programs and functions that take a large heap of information to suit our needs in more useful ways. It is a lot less “AI”, and much more “Big Data.”
Machine learning today might sound not as apocalyptic, but it is still revolutionary. Never before could a system capture our past habits to maximize our satisfaction with such potency. We live in a new age of user experience; not one that thinks for itself, but one that absorbs massive volumes of data, performs operations millions of times per second, test and dismisses patterns according to thresholds set by humans, and incorporates them into the programs and tasks we decide.
The term “machine learning” is used just about wherever a slightly resembling tech comes up. The mobile devices spectrum, with IoT and smartphones, is a prominent example. But mobile, as it turns out, is poised to be the next ecosystem where computer-fueled user experience will evolve. An increasingly prominent place for people to interact with companies, services, products and knowledge, mobile is becoming a key element in all communications strategies. And with machine learning, there is a clear leap forward to a valuable, easier and more user-friendly experience.
Mobile should also get credit for an unparalleled opportunity in social research: micro targeting. Here is Kurt Marko, from Forbes:
[T]he goal isn’t to merely provide an integrated omnichannel experience, but to be proactive not reactive: anticipate customer needs and prevent problems, don’t just solve them.
Machine learning facilitates this. Predicting customer intent would allow us to create a more effective customer service experience, for every person. Agile data mining, feeding on growing sets of inputs from mobile interaction, seeps into advertising, marketing, education, and learning management systems (LMS).
Machine Learning and Mobile
Which is why it is no secret mobile is the new king. A category where unmanned vehicles, like drones and cars, also belong. Running learning algorithms, a multi-layered neural network for example, consumes 90 percent less power than what was previously possible. The implications are endless as we consider what we want to accomplish with machine learning. Here are some examples:
Advertising: What is more important in advertising than being able to know the needs and desires of your target demographic, inside and out? Algorithms using machine learning are able to take historical data from consumers, and use it to predict future habits. They can also be used to track past and future ad performance significantly more effectively than any human would be able to, and help to define audience clusters.
App Development: With the mobile user’s demand for the shortest learning curves possible, user experience rules functionality and performance, across all kinds of mobile apps. Google perfected language translation and speech recognition. Amazon encourages machine learning with easy-to-deploy, real-time engines for apps supported by their web services. Many already service users according to informed profiles of sharing with friends or spending money on in-app purchases.
Professional Training: As training methods develop, and mobile LMS become the norm, machine learning is one of the crucial factors in perfecting apps that can train working professionals in a smart and efficient way. If you look at gamification examples you will see that it requires smart interfaces to keep an employee engaged and stimulated throughout the entire onboarding/training experience and beyond.
There is a world of other ways in which machine learning can improve the mobile experience for users. What we have described is just the beginning. So instead of viewing this concept as the end of the world, think of it as only the beginning of a new one.