  This paper presents a technique called evolving self-supervised neural
networks - neural networks that can teach themselves, intrinsically motivated,
without external supervision or reward. The proposed method presents some
sort-of paradigm shift, and differs greatly from both traditional
gradient-based learning and evolutionary algorithms in that it combines the
metaphor of evolution and learning, more specifically self-learning, together,
rather than treating these phenomena alternatively. I simulate a multi-agent
system in which neural networks are used to control autonomous foraging agents
with little domain knowledge. Experimental results show that only evolved
self-supervised agents can demonstrate some sort of intelligent behaviour, but
not evolution or self-learning alone. Indications for future work on evolving
intelligence are also presented.
