Artificial Intelligence: Driven by Data, Not Code

In the ever-forward-looking world of the Silicon Valley, lately there's been a lot of hype surrounding the use of AI and machine learning processes in order to build the next generation of software products and features -- with Google's self-driving cars taking the spotlight as the representative for this line of thought. Though largely an unproven concept at this point, given that a working, reliable model could yield untold benefits, it's something that a lot of companies are pushing as the "next big thing" in the world of tech. Not to say that the possibility of making it work isn't there, but there's a lot of challenges that go into building "AI" systems that often go undiscussed, which, in most cases, leads to the product's lack of adoption in the long run. I've put "AI" in quotes here, because what gets categorized as "artificial intelligence" in the media these days isn't actually something that's driven by "intelligence", per se -- the majority of AI or machine learning projects tend to be driven by data, rather than the code itself. If you look under the hood of how Google's self-driving algorithms work, you'll see that a lot of its functionality is heavily reliant on the accuracy of Google Maps, which gives the software enough of an understanding of its environment in order for the car to navigate through its terrain.