You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
97 lines
4.8 KiB
97 lines
4.8 KiB
# History of Machine Learning and AI
|
|
|
|
Add a sketchnote if possible/appropriate
|
|
|
|
[](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd")
|
|
## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
|
|
|
|
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](https://wikipedia.org/wiki/Timeline_of_machine_learning) predated and overlapped this era.
|
|
|
|
## Notable Discoveries
|
|
|
|
- 1763, 1812 [Bayes Theorem](https://wikipedia.org/wiki/Bayes%27_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](https://wikipedia.org/wiki/Least_squares) by French mathematician Adrien-Marie Legendre. This theory, which you will learn about in our Regression unit, helps in data fitting.
|
|
- 1913 [Markov Chains](https://wikipedia.org/wiki/Markov_chain) named after Russian mathematician Andrey Markov is used to describe a sequence of possible events based on a previous state.
|
|
- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) is a type of linear classifier invented by American psychologist Frank Rosenblatt that underlies advances in deep learning.
|
|
- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) is an algorithm originally designed to map routes. In an ML context it is used to detect patterns.
|
|
- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) is used to train [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network)
|
|
- 1982 [Recurrent Neural Network](https://wikipedia.org/wiki/Recurrent_neural_network) are artificial neural networks derived from feedforward neural networks that create temporal graphs.
|
|
## 1950: Machines that Think
|
|
|
|
Alan Turing
|
|
|
|
## 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
|
|
|
|
[](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of Deep Learning")
|
|
> Yann LeCun discusses the history of Deep Learning in this lecture
|
|
|
|
✅ Knowledge Check - use this moment to stretch students' knowledge with open questions
|
|
## 🚀Challenge
|
|
|
|
Add a challenge for students to work on collaboratively in class to enhance the project
|
|
|
|
Optional: add a screenshot of the completed lesson's UI if appropriate
|
|
|
|
## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
|
|
|
|
## Review & Self Study
|
|
|
|
[Check out this podcast where Amy Boyd discusses the evolution of AI](http://runasradio.com/Shows/Show/739)
|
|
|
|
**Assignment**: [Create a timeline](assignment.md)
|