The search for technology that can drive the next industrial revolution is heating up. Enterprises like mine are investing significantly on R&D to apply artificial intelligence (AI) techniques in our businesses. And AI assets are being acquired at a fast clip: CB Insights reports that 140 AI-focused enterprises have been bought out in the past five years alone. What's more, nearly three in ten of those transactions occurred in the first nine months of 2016 .
Of course, interest in AI technology isn't new. I went to school studying expert systems — the rage at the time. But we had prematurely hailed the arrival of the AI age back then. That traditional AI didn't so much reason as it did automate. It was akin to a math student being taught step-by-step to solve a very complex, yet narrow, problem, without grasping the underlying reasoning that could be applied to other, much broader problems.
This traditional AI approach knew what something was by comparison to other defined items of its kind. For example, we used to identify a non-compliant loan application using supervised learning from a labeled dataset. And from there on, all loan applications submitted could be automatically classified and acted upon with a pre-determined workflow.
The new generation AI — called deep reasoning — breaks this traditional dependency on known datasets. Instead, deep reasoning performs unsupervised learning from large unlabeled datasets to actually reason in a way that can be applied much more broadly. In other words, with enough (read: enormous) data, this next gen AI can “learn to learn" for itself.
And this is real. Today, we are working with clients to manage industrial assets and deliver automated customer support. In the latter case, AI helps us take in a corpus of knowledge and learn from it to start responding to customer service queries, and continuously learn more with every interaction across a whole wide variety of areas.
So why is this happening now? It isn't just the new algorithms. It's because the advancements in AI techniques can take advantage of two other converging trends: computing that is becoming ambient and elastic, and large data sets that are becoming easy to extract, store, manage, and use.
So this fundamental redefining of AI is bringing focus to the critical value of data. The need for these massive data sets is why my town is filled with self-driving Google cars, and why IBM bought The Weather Company — in order to build out vast data sets to train their AI engines. The idea that you can have AI without large sets of data is like trying to make ice without water.
And it's why enterprises like mine are harvesting transformational benefits from access to large sandboxes of (in our case, B2B) data sets. For those that are new to the space, it's an insight worth reflecting on.