  Machine learning is the science of credit assignment: finding patterns in
observations that predict the consequences of actions and help to improve
future performance. Credit assignment is also required for human understanding
of how the world works, not only for individuals navigating daily life, but
also for academic professionals like historians who interpret the present in
light of past events. Here I focus on the history of modern artificial
intelligence (AI) which is dominated by artificial neural networks (NNs) and
deep learning, both conceptually closer to the old field of cybernetics than to
what's been called AI since 1956 (e.g., expert systems and logic programming).
A modern history of AI will emphasize breakthroughs outside of the focus of
traditional AI text books, in particular, mathematical foundations of today's
NNs such as the chain rule (1676), the first NNs (linear regression, circa
1800), and the first working deep learners (1965-). From the perspective of
2022, I provide a timeline of the -- in hindsight -- most important relevant
events in the history of NNs, deep learning, AI, computer science, and
mathematics in general, crediting those who laid foundations of the field. The
text contains numerous hyperlinks to relevant overview sites from my AI Blog.
It supplements my previous deep learning survey (2015) which provides hundreds
of additional references. Finally, to round it off, I'll put things in a
broader historic context spanning the time since the Big Bang until when the
universe will be many times older than it is now.
