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Deep Reinforced Learning: Making AI More Accessible by Bootstrapping
Machine Learning and Deep Learning have become popular tools in the arsenal of Artificial Intelligence (AI), but while AI has generated a lot of interest, one problem is that AI techniques are complex and not enough people are properly trained to apply them.
Fei-Fei Li, chief scientist at Google Cloud, said that “we need to scale AI out to more people. But there are an estimated 21 million developers worldwide today. We want to reach out to them all, and make AI accessible to these developers.”
One solution is to use artificial intelligence to bootstrap itself. There is an effort now to develop AI tools that can preprocess and tune the AI algorithms that ultimately get applied to solve and implement a problem. It’s a two step approach that will make it easier for someone not trained in AI to be able to use the technology and achieve good results.
A method called deep reinforcement learning does just that. It is a relatively unsupervised algorithm that applies a trial and error approach to learning what AI techniques work best. It attempts to automate and shorten the AI development and training process.
