The 2010s were huge for artificial intelligence, thanks to advances in deep learning, a branch of AI that has become feasible because of the growing capacity to collect, store, and process large amounts of data. Today, deep learning is not just a topic of scientific research but also a key component of many everyday applications.
But a decade's worth of research and application has made it clear that in its current state, deep learning is not the final solution to solving the ever-elusive challenge of creating human-level AI.
What do we need to push AI to the next level? More data and larger neural networks? New deep learning algorithms? Approaches other than deep learning?
This is a topic that has been hotly debated in the AI community and was the focus of an online discussion Montreal.AI held last week. Titled "AI debate 2: Moving AI forward: An interdisciplinary approach," the debate was attended by scientists from a range of backgrounds and disciplines.
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