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If your business talks with companies offering AI-based solutions, you’ll likely have heard a lot of big claims, ranging from “neuromorphic engineering [solutions], that allows machines to see like humans” (Prophesee) to “It is one of the most powerful tools our species has created. It helps doctors fight disease.” (IBM Watson, Superbowl Commercial). As a non-expert it is often difficult to say which claims are legitimate and which are overhyped.

Companies across all industries are exploring the opportunities AI holds for their business model. While identifying first AI use cases is challenging, actually implementing them adds a whole new layer of complexity. In this process, one of the most fundamental questions you have to answer is whether to build or buy.

In 2006, Geoffrey Hinton’s paper “A fast learning algorithm for deep belief nets” famously demonstrated how large neural networks can work. The nets had more layers than previous models — in other words they were deep, which ultimately rebranded the method as deep learning. After years, where the academic community had almost forgotten about neural networks, Geoffrey Hinton brought them back to life.