Many today have come to equate AI with machine learning. But the two are not the same. This short presentation gives historical context for the distinctions between the terms in today's world.

1. AI - Not Just Machine Learning
David Vandegrift

2. First, a disclaimer
 There is no universally acknowledged definition of
artificial intelligence
 The idea of artificially intelligent beings is prehistoric,
with widely recognized references to intelligent
machines appearing as early as the 1300s
 What is now called “machine learning” got its start in
the 1700s/1800s well before there were machines
that could learn
 The first actual learning machines showed up in the early
1950s

3. A brief history of AI
 The first widely recognized work of AI was the 1943 design of Turing-
complete “artificial neurons”
 The field blossomed at a conference at Dartmouth in 1956, with the
presentation of the Logic Theorist program
 This is when the term “artificial intelligence” was coined
 Progress accelerated through the 60s, but slowed in the 70s, leading
to the first “AI winter”
 Expert Systems brought the field back into vogue in the 80s
 Progress continued through the late 90s and early 2000s with much
wider adoption of machine learning, though AI had become a bad
word
 The current AI Spring was heralded by Watson on Jeopardy in 2011
and cemented by ImageNet, DeepMind, and AlphaGo in 2014/2015

4. ML vs. Non-ML
Machine Learning
 Regression
 Probabilistic classifiers
 Support vector
machines
 Neural networks
 Clustering
 Decision tree learning
 Genetic algorithms
Not Machine Learning
 Logic systems
 Expert systems
 Decision trees
 Semantic graphs

5. A thought experiment...
 2010-2014
 DeepMind develops Q-Learning algorithms to beat Atari games
 Google buys DeepMind for $500M
 2014
 DeepMind begins development on AlphaGo
 AlphaGo is trained on 30 million professional moves and
thousands of matches against itself
 2015
 AlphaGo beats one of the top Go players in the world, Lee
Sedol (4-1)
Before playing Lee Sedol, AlphaGo’s learning mechanism
was turned off.
Was it still a machine learning program?

6. The role of non-ML AI today
 While the limits of expert systems were clearly
demonstrated in the 80s, there is much excitement
over the potential of “hybrid systems”
 “Human-in-the-loop” systems where ML does 80% of the
work and humans close the gap
 NLP: Deep learning applied to syntax disambiguation
 Driverless cars: Computer vision within the confines of a
well-defined task (driving)
 Agents: Human-defined guard rails on otherwise
autonomous AIs (see Microsoft Tay)