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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...
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:
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Source: MoodleNews
