  As humans we are driven by a strong desire for seeking novelty in our world.
Also upon observing a novel pattern we are capable of refining our
understanding of the world based on the new information---humans can discover
their world. The outstanding ability of the human mind for discovery has led to
many breakthroughs in science, art and technology. Here we investigate the
possibility of building an agent capable of discovering its world using the
modern AI technology. In particular we introduce NDIGO, Neural Differential
Information Gain Optimisation, a self-supervised discovery model that aims at
seeking new information to construct a global view of its world from partial
and noisy observations. Our experiments on some controlled 2-D navigation tasks
show that NDIGO outperforms state-of-the-art information-seeking methods in
terms of the quality of the learned representation. The improvement in
performance is particularly significant in the presence of white or structured
noise where other information-seeking methods follow the noise instead of
discovering their world.
