Future of AI: data won't be enough

The enthusiastic embracing of AI as the go-to technology for solving specific problems is both undeniable and remarkable. But while there is still much progress being achieved every day through the most popular AI approaches like supervised learning or reinforcement learning, the often monolithic way in which those classic approaches are used may also be the very thing that holds AI back. While AI is increasingly successful in a growing number of fields, it still operates primarily as a tool to execute narrow-focus tasks, or as a simple form of automation, rather than a supporting partner in a relationship with human users. It largely relies on carefully curated or annotated, mostly historical, data, and only very indirectly learns from human users. AI has remarkable predictive power in some cases, yet is incapable of the adaptive prowess routinely demonstrated by humans from their infancy. It simply is not (yet) able to extrapolate on data that it has never encountered quite like humans can.