  The claims that big data holds the key to enterprise successes and that
Artificial Intelligence is going to replace humanity have become increasingly
more popular over the past few years, both in academia and in the industry.
However, while these claims may indeed capture some truth, they have also been
massively oversold, or so we contend here. The goal of this paper is two-fold.
First, we provide a qualified defence of the value of less data within the
context of AI. This is done by carefully reviewing two distinct problems for
big data driven AI, namely a) the limited track record of Deep Learning in key
areas such as Natural Language Processing, b) the regulatory and business
significance of being able to learn from few data points. Second, we briefly
sketch what we refer to as a case of AI with humans and for humans, namely an
AI paradigm whereby the systems we build are privacy-oriented and focused on
human-machine collaboration, not competition. Combining our claims above, we
conclude that when seen through the lens of cognitively inspired AI, the bright
future of the discipline is about less data, not more, and more humans, not
fewer.
