If you haven’t heard of the Gartner hype cycle before, this visual model is really catching on across the AI community.
The Gartner hype cycle, a product of the famed consulting and reporting firm Gartner, takes individual innovations and puts them on a graph with specific staging, according to anticipated adoption models.
In other words, looking at where something is on the cycle, you see where it’s at in general popularity, familiarity and implementation.
One thing that’s interesting about this model is its five distinct stages: there’s an “innovation trigger,” a quick rapid increase in expectations, followed by a “peak of inflated expectations” and a fall back into what’s called a “trough of disillusionment.”
After that there is a redemptive “slope of enlightenment” and an eventual “plateau of productivity.”
Cool semantics (and alliteration) aside, this does offer us a sort of stable vision of many of these new ideas coming down the pike.
For instance, let’s talk about some of the items that Gartner has put right out in front of newer items like synthetic data, neomorphic hardware and generative AI in general.
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First, there’s semantic search, which comes in first place on this Gartner graph as a technology that’s reaching from the trough of disillusionment toward the slope of enlightenment, with a projected time frame of around two to five years.
Computer vision isn’t far behind, followed by autonomous vehicles and in turn, chatbots.
Chatbots has a shorter time frame anticipated, less than two years, where autonomous vehicles, according to the chart, are likely to take over 10 years to develop.
In general, though, it makes sense to put autonomous vehicles on the trough of disillusionment, considering that we’ve been working on them for quite some time, and the early hype was extreme.
One way to explain it is that company started rolling out the models, and people saw firsthand their capability to generate accidents, or in other words, their incapability to ensure safety. (Tesla’s technology comes to mind.)
As for chatbots, perhaps we had inflated expectations, and now we’re building toward stabler integration into business models that might have you talking to a computer a lot more in the years to come.
Computer vision was a puzzler to me, because we’ve come so far in AI capability to look and see what’s around it. With the implementation of convolutional neural networks, we seem to have generated robust computer vision that’s useful and applicable in so many ways. So why is it in the trough of disillusionment?
Well, maybe it’s also making its way upward toward the plateau of productivity, where we’re going to see robots seeing and perceiving their surroundings in popular consumer technologies, say, three years from now.
Here’s something interesting: in a recent conversation we have posted, a friend of mine, Sandy Pentland, asks two others, Ramesh Raskar and Hari Balakrishnan, the following question: Where are we in the hype cycle? (I run MIT classes with Ramesh).
“Well, with generative AI, we're near the peak,” Balakrishnan responds, suggesting that investors should get their bids in within 18 months. “I think that what people will find is tremendous misinformation.”
“I would say there's absolutely a lot of overinvestment in certain areas, but surprisingly, underinvestment in many areas,” Raskar said. “And I would say there's underinvestment in most of the areas. So as we said earlier, many real world sectors are just not getting enough attention. They're going to boom in the next three to five years, not right away, because it takes a long time for new technology to be absorbed … to see the productivity gains … that takes some time, but over the next three to five years, you'll see these underinvested areas really take off. And you can use an analogy from the internet era, you know, in the beginning, everybody bought stock in chip companies and OS companies, right, and Cisco and IBM and so on. But very soon, what was built on top of that, which is the applied internet, you know, eBay, Yahoo, Google, Facebook and so on. That's so much bigger than anybody who's selling chips or internet infrastructure.”
That’s insightful, and gives us a little more on the hype cycle in general. Look for additional reading of the tea leaves as experts and professionals use Gartner’s tool to fill in the blanks.
