The AI Revolution That Was And Wasn't In 2018 Posted on : Dec 11 - 2018

Looking back on 2018, this has been a year in which AI has continued its meteoric rise over the digital landscape, infusing its magical powers into almost every corner of every industry and revolutionizing how society uses data. Or so one might be forgiven for thinking this year as companies big and small have rushed to demonstrate how they are harnessing deep learning to upend their business processes. The reality is that while AI has truly transformed areas like audiovisual recognition, given us powerful new tools for understanding language and offered a first glimpse at algorithms that possess glimmers of intuition, the mundane reality of the overwhelming majority of commercial AI applications to date have frequently offered little improvement over the traditional approaches they replaced if those systems had been built properly to begin with.

We speak today of deep learning in reverent tones and ascribe to it an almost mythical aura of superhuman capability. Companies rush to sprinkle the magical AI dust on every project. Even normally austere and risk adverse industries have been plunging headfirst into the AI world with reckless abandon, throwing deep learning models at every problem. The same funding agencies that once required the phrase “social media” in every successful proposal now require “deep learning” somewhere in the abstract to even consider funding a project, whether or not AI has even the slightest applicability to the problem at hand.

In the public consciousness and increasingly in the C-suite, AI is described as human-like algorithms that are basically childlike versions of ourselves that are improving by the day and that any limitations in their accuracy can be instantly fixed by just handing them a bit more training data.

The reality, of course, is that today’s deep learning algorithms are more art than science. Accuracy gains come not from simply blindly throwing more training data at an algorithm, but from careful hand selection of training data, intricate tuning, experimentation and often dumb luck. Successful algorithms are enigmas that even their own creators don’t fully understand and can’t automatically replicate in other domains. Even the most accurate models are frequently so brittle that the slightest change or malicious intervention can send them wildly off course.

Far from primitive silicon humans with childlike minds, today’s AI systems are nothing more than basic statistical encapsulations, more powerful and capable than past approaches, but little different from what we’ve been doing since the dawn of computing. View More