  Man-Computer Symbiosis (MCS) was originally envisioned by the famous computer
pioneer J.C.R. Licklider in 1960, as a logical evolution of the then inchoate
relationship between computer and humans. In his paper, Licklider provided a
set of criteria by which to judge if a Man-Computer System is a symbiotic one,
and also provided some predictions about such systems in the near and far
future. Since then, innovations in computer networks and the invention of the
Internet were major developments towards that end. However, with most systems
based on conventional logical algorithms, many aspects of Licklider's MCS
remained unfulfilled. This paper explores the extent to which modern machine
learning systems in general, and deep learning ones in particular best
exemplify MCS systems, and why they are the prime contenders to achieve a true
Man-Computer Symbiosis as described by Licklider in his original paper in the
future. The case for deep learning is built by illustrating each point of the
original criteria as well as the criteria laid by subsequent research into MCS
systems, with specific examples and applications provided to strengthen the
arguments. The efficacy of deep neural networks in achieving Artificial General
Intelligence, which would be the perfect version of an MCS system is also
explored.
