Advanced Technology is Indistinguishable From Magic
“Any sufficiently advanced technology is indistinguishable from magic,” Arthur Clarke famously said. Just as certainly, even an advanced technology that is a general purpose technology (like electricity, computing, internal combustion engine), once ubiquitous, recedes into the background and is not commonly referred to as “advanced technology” any longer.
That happened with electricity, computing, the internal combustion engine, steam power, railroads, automobiles, the domestication of plants and animals, the wheel, writing, printing and mass production, none of which is generally considered to be advanced technology in the same sense as artificial technology now is viewed.
One normally hears artificial intelligence mentioned as one of three forms of machine learning, with deep learning and neural networks often said to be more-intense forms of AI, where machines learn on their own, without direct and specific programming by humans, or where computers are able to recognize new patterns on an unsupervised basis, creating new rules based on those insights.
But early forms of AI already surround us. Apple’s Siri, Amazon’s Alexa, Amazon’s shopping experience, Netflix video and Google’s Nest thermostat system all are based on use of AI. So are algorithmic trading, robo financial advisors, Google Maps, Google search, ridesharing services such as Uber and Lyft, Gmail, mobile check deposit, Facebook, Pinterest, Instagram and Snapchat, for example.
Videogames, credit card fraud protection, online customer support and personalized content recommendations likewise are powered by AI. By 2025, financial asset trading, image recognition and health applications are likely to be among the biggest users of AI, Tractica argues.
The point is that the business function of artificial intelligence and machine learning is to glean insight from massive amounts of data human beings cannot analyze quickly enough, and discover relationships in all that apparently random data, and allow application of such insights to organizational and business processes.
With regard to mobile communications, the most-significant data store probably is “current location,” even if that data is rarely used in a direct sense by mobile operators except as required to “connect to the best radio at the moment.”
As always, privacy issues are barriers to using such location information in a more-useful way, for generating insight about future behavior. And location data, by itself, is not as useful as location data combined with detail from many other data stores that inform the analysis engines about past behavior.
In that regard, it is virtually impossible to separate formerly-separate trends, including artificial intelligence, big data, and the Internet of Things (IoT) and mobility itself. Big data requires AI to create new insights, while IoT puts into place the sensors that will generate granular new data, while mobile networks will be key to connecting many of those new sensors.