In August, Facebook introduced its new digital assistant, M, to a limited number of users, whose response was unlike anything Siri or Google Now had faced. People were flummoxed over whether M was actually an artificial intelligence (AI) win for Facebook or if there was a human on the other side of the screen.
The answer: Both.
A tremendous amount of learning is involved in AI, and Facebook's new baby was taking its first wobbly steps, with adult supervision. A Wired profile explained that M used AI to field the initial response to every question, but that a person approved or adjusted every answer before it went out. With every adjustment or implied thumbs up, M learned and got a little better at answering on its own.
There are a few key things happening that speak to the bigger picture of big data and analytics today, when a technology like M -- or Cortana or Alexa -- can do something like send your mom a bouquet on Mother's Day.
One is that the software is learning processes and finding out how to make connections. Because thousands of other people have made the same requests, it's already made corrections and figured out efficiencies that have nothing to do with any individual user, but rather the learning that can come from the enormous datasets being created when thousands or millions of people are contributing data points.
Another key thing that these assistants are learning about each of us is how to better sell to us.
Still another is that we consumers are getting increasingly used to the idea of sharing data, like our credit card number, our mom's name and address, and many other data points that we may or may not realize can be of value to an algorithm and a company like Facebook or Apple.
That willingness, along with our understanding of the larger value we receive in return, will drive a similar and inevitable shift within enterprises, as each of us in our professional roles needs difficult questions answered, such as knowing whether we'll meet a sales forecast, or tasks accomplished, such as thwarting hackers.
Enterprises, particularly in regulated industries like government and healthcare, are increasingly understanding and embracing the benefits that come with sharing data and contributing data -- in secure ways -- to create larger datasets that can reveal critical and otherwise unavailable insights.
This first wave of digital assistants may be all it takes to warm consumers to the concept and push more enterprises beyond legacy, pre-Internet thinking about data, how it should be treated, and what it can make possible.
In the following pages we take a look at digital assistants from five of the largest tech players and how they're using your data to perfect their AI. Take a look and let us know your thoughts in the comments section below.
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