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History of Machine Learning and AI

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Pre-lecture quiz

In this lesson, we will walk through the major milestones of the history of Machine Learning and AI.

The history of Artificial Intelligence as a field is intertwined with the history of Machine Learning, as the algorithms that underpin ML fed into the development of AI. It is useful to remember that, while AI as a field of inquiry began to crystallize in the 1950s, important algorithmical, statistical, mathematical and technical discoveries predated and overlapped this era.

Notable Discoveries

• 1763, 1812 Bayes Theorem and its predecessors. This theorem and its applications underlie inference, describing the probability of an event occuring based on prior knowledge.
• 1805 Least Square Theory by French mathematician Adrien-Marie Legendre. This theory, which you will learn about in our Regression unit, helps in data fitting.
• 1913 Markov Chains named after Russian mathematician Andrey Markov is used to describe a sequence of possible events based on a previous state.
• 1957 Perceptron is a type of linear classifier invented by American psychologist Frank Rosenblatt that underlies advances in deep learning.
• 1967 Nearest Neighbor is an algorithm originally designed to map routes. In an ML context it is used to detect patterns.
• 1970 Backpropagation is used to train feedforward neural networks
• 1982 Recurrent Neural Network are artificial neural networks derived from feedforward neural networks that create temporal graphs.

Do a little research. What other dates stand out as pivotal in the history of ML and AI?

1950: Machines that Think

Alan Turing, a truly remarkable person who was voted by the public in 2019 as the greatest scientist of the 20th century, was

1956: Dartmouth Research Project

"To proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it -- John McCarthy"

They named the field of "Artificial Intelligence". This is the first time the phrase was coined.

1956 - 1974: "The Gold Rush"

Optimism was high in this era that AI could solve many problems. Research was very well funded. Shakey the robot could maneuver and decide. Eliza could converse w/ ppl. Blocksworld

1974 - 1980: "AI Winter"

Funding stopped, optimism lowered. Some issues included: compute power was too limited combinatorial explosion: the amount of parameters needing to be trained exploded w/o compute keeping up paucity of data, hindered the process of using algorithms how to frame the question...were we asking the right questions, were they specific enough lots of criticism about approaches criticism on turing tests chinese room theory ethical criticism of eliza

scruffy vs. neat AI neat AI has lots of trees and logical reasoning scruffy AI encompasses an idea's metadata -led to progressions in OO programming

1980s Expert systems

knowledge became the focus of AI and its businenss impact became acknowledged

revival of connectionism (NN) behind the scenes, in research hopfield net backpropagation applied neural networks

1987 - 1993: AI Chill

hardware had become too specialized moving into an era of personal computers - computing becoming democratized

1990s: AI based on Robotics

To show real intelligence AI needs a body

1993 - 2011

Same issues start to be solved excessive data huge compute power more powerful algorithms better able to frame question

Now

AI started as a single area, now there are many parts and they cross-collaborate

Yann LeCun discusses the history of Deep Learning in this lecture

Challenge

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Post-lecture quiz

Review & Self Study

Check out this podcast where Amy Boyd discusses the evolution of AI

Assignment: Create a timeline