Five Things Most People Get Wrong About AI Posted on : Mar 08 - 2019

For better or worse, the term “artificial intelligence,” or AI, has become central to understanding the landscape of today’s technology and innovation. Despite that, its true definition and implications for the future remain misunderstood by many. Here, I explore five of the most prominent misconceptions around AI:

1. AI has one universal definition.

Because the term “AI” has grown to encompass so many different things -- from voice assistants to self-driving cars to automation -- it’s important to be clear about what type of AI one is referring to. One thing is for certain: Not all AI is created equal, and not all AI relies on the same core technologies.

For instance, Deep Blue, the supercomputer that was able to beat Garry Kasparov at chess in the 1990s, relied on vastly different technology from the program that beat world-class Go player Lee Sedol. Moreover, given that most organizations these days choose to focus on their machine-learning or deep-learning initiatives, it’s important to note the terms and nomenclature that the researchers themselves are using to describe their work. In many instances, companies are simply scratching the surface of what is possible with AI.

2. AI is all hype.

One of the biggest misconceptions I’ve noticed regarding AI and deep learning is the idea that it’s all hype, that there’s no substance behind the concept. This is far from being true and, in fact, one could argue that the public’s increasing fascination with deep learning is to blame. Very often, the instinct is to seize on one particular news item (a chatbot gone rogue, gender bias in an AI recruiting tool) and use that to extrapolate on the general state of the industry.

As a field, AI is constantly shifting. Techniques that people once staunchly championed frequently fall to the wayside, whereas concepts that first arose decades ago might quickly come back into prominence. As with anything else, there’s no strict linear progression. View More