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Is It AI or BS? Here's Why the Answer Doesn't Matter

A recent article in Ad Age argues that, for some marketers and product developers, AI’s buzz-worthiness may be more enticing than the functionality of the tech at hand. The author rehashes a number of complaints from within the industry: machine learning erroneously gets called “AI”; companies use AI technologies without understanding them; marketers and product developers are riding a wave of AI hype to fuel the promotion of AI-powered products.

Fair enough. But there’s a sunny side to the hype. In fact, hype is a good thing.

What’s in a name?

Terms such as “machine learning,” “neural networks” and “natural language understanding” (among others) may not be as sexy as “AI,” but does that really matter? Is it really detrimental to have a term that can be used to describe a complex set of technologies in order for them to be better understood and … gulp … marketed?

I say no. Having a term that acts as a categorical signpost of a family of technologies is enormously helpful because, over the long term, it helps promote understanding and adoption of technologies that have the potential to change the world.

A prime example is the term “internet.” This single name belies the rather large and complex set of technologies that work together to create our understanding of what the internet is. But over time, “internet” came to stand for sum total of our digital connectivity, and we all wanted to be involved with it. Right now, a number of platforms and technologies are finding their way to market thanks, in part, to the iconographic aura surrounding “AI.” The internet was hyped. And now, so is AI.

What’s really worth getting hyped about?

The technologies that make up AI have advanced to a point where they have come out of academia to become accessible to more people. In other words, the barrier to entry is low — lower than most people realize — and will continue to decrease over time. And that’s exciting.

The code required to support the creation of neural networks need not be written from scratch. Advanced computer science degrees are no longer required. For example, you can download
TensorFlow, an open source machine learning toolset, from Google, and focus only on the problem at hand, the neural network itself.

The result? Advanced technology is wielded by more hands and is used in more and more creative and interesting ways. I’m the first to stand in awe of the computer scientists and academics who create and advance the technological foundations of AI, but I also stand in awe of the creative visionaries who can find ingenious uses for new technologies. When different kinds of brilliant minds can advance the way a technology can be used, we get more products and solutions.

Which leads to innovation.

Which then leads to hype. Healthy hype.

It also doesn’t hurt that these technologies are actually proving to be useful and have started to have a massive impact in our lives (e.g., if you’ve used Siri, or searched for something on Google today).

In other words, they have real purpose, which actually is the fundamental issue we should be thinking — and getting hyped — about. Technology used for technology’s sake rarely works or sticks around very long.

It must have a purpose and value.

It must improve a customer pain point, or enable new business models and products.

If you can’t clearly see the purpose of a particular AI-driven technology — if you can’t answer the question, “What problem are we solving?” — then AI may not be what you need. But people are indeed asking those questions, because AI is inspiring creative minds, providing fundamentally different ways to approach problem solving. It’s sparking wonder and innovation. That can’t be a bad thing.

I, for one, can’t wait to see how these technologies will evolve, and what innovations they will spur. And if a few companies decide to use AI as a marketing and product strategy, so be it. The market itself will filter out the empty hype eventually. It always has.

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