Boosting AI’s smarts in the absence of training data

AI (artificial intelligence) is the most perfect field of dreams in modern culture. If you ask the average person on the street what AI runs on, they probably won’t mention training data. Instead, they might mumble something about computer programs that magically learn how to do useful stuff from thin air.

However, some of today’s most sophisticated AI comes close to that naïve dream. I’m referring to a still-developing approach known as “zero-shot learning.” This methodology—which is being explored at Microsoft, Uber, Baidu, Alibaba, and other AI-driven businesses—enables useful pattern recognition with little or no training data.

Zero-shot pattern learning will enable intelligent robots to dynamically recognize and respond to unfamiliar objects, behaviors, and environmental patterns that they may never have encountered in training. I predict that zero-shot approaches will increasingly be combined with reinforcement learning in order to enable robots to take the best actions iteratively in environments that are chaotic and one-off.

In addition, gaming applications will use zero-shot approaches such as iterative self-play as an alternative to training on voluminous data derived from successful gameplay. This will enable the training of agents to master complex winning strategies in spite of knowing nothing about these games at the outset.

Furthermore, zero-shot learning promises to make object recognition applications more versatile, due to its ability to drive:

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