The Curse of AI FOMO

Expectations about AI are extremely high. We are constantly hearing that “AI will change the world.” The big data hype wave felt very similar — big data was the new business intelligence, big data would solve everything. But the harsh reality is that the majority of companies were just not ready to handle, store, or use massive amounts of data. I saw many companies hire data scientists first, without thinking what those first data projects should be, or the larger goal of integrating how to integrate that data in a valuable way.

In those early years of big data, the outcome was always less than perfect. Most of the work ended up in powerpoint presentations without ever going into production because most teams simply did not have the right infrastructure or culture to maintain them. Rather, many companies just bought into the trend and invested in big data because they had the fear of missing out. Behavioral economist Dan Ariely also succinctly described the big data hype thusly:

The same thing applies to AI now. And before we go any further, let’s clear one thing up: when people in business say “AI” they really mean machine learning — which is the outcome we were all envisioning when we got excited about big data all those years ago.

The fear of missing out on AI is so high that everyone wants to be part of this wave even though they are not ready for this. I’ve seen companies who don’t have the basis for even the simplest machine learning algorithm — let alone the right people and culture — but they believe if they pour in enough money, they will get their AI transformation. They spend millions on tools, and yet they don’t have an infrastructure that can handle complex algorithms or deploy changes to it in a fast, iterative manner.

And some opportunists are ready to capitalize on this FOMO. IBM’s Watson is the best example. It promises to solve everything from winning game shows like Jeopardy to bolster applications that help with cancer treatments. IBM wants you to think Watson can solve every problem that you can imagine because it uses deep learning and has a cool name.

“IBM Watson is the Donald Trump of the AI industry — outlandish claims that aren’t backed by credible data.” — Gizmodo

But we all know that Watson is a big marketing gimmick. Moreover, we have too many so-called experts that say AI is the next industrial revolution. A new digital frontier. They paint a picture where AI machines can learn by themselves. And cast every problem as a reinforcement learning problem. On the other hand, we have people that think we should be fearful of AIs. In fact there is an active discussion whether AI is “the biggest risk we face as civilisation.” But that concern is about artificial general intelligence (AGI), not machine learning. And as you can see from the video below, which shows a soccer game from the recent RoboCup 2017, here’s how dangerous AGI is at the moment:

Current state of artificial general intelligence.

After the AI Hype Storm

As you can see, the expectation for AI is exploding. But just like it happened for big data, you should expect this bubble to burst. And after that we’ll get a new trend. Will it be quantum computing? I don’t know yet, but I hope that after the AI hype storm there will be some calm.

I anticipate that after this passes, we can start to do the right thing — focusing on using machine learning to build things that are meaningful and realistic. Like driverless cars, which will go on to shape the future of mobility for generations. We can work on improving medical diagnosis, education retention, and much more. Those are just some examples; there are many ways machine learning can help, and many will be realistic applications that improve our everyday lives. We just need the right people, culture, and infrastructure to execute. Instead of surfing the wave which will just take us back to the beach we’ve always known, we can sail towards new ground, a promising shore.

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