[](https://youtu.be/esmzYhuFnds "Introduction to Clustering")
> MIT's John Guttag introduces Clustering
[Clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music.
✅ Take a minute to think about the uses of clustering. In real life, clustering happens whenever you have a pile of laundry and need to sort out your family members' clothes 🧦👕👖🩲. In data science, clustering happens when trying to analyze a user's preferences, or determine the characteristics of any unlabeled dataset. Clustering, in a way, helps make sense of chaos.
[](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd")
"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"
Describe what will be covered
They named the field of "Artificial Intelligence". This is the first time the phrase was coined.
> Notes
## 1956 - 1974: "The Gold Rush"
### Prerequisite
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
What steps should have been covered before this lesson?
## 1974 - 1980: "AI Winter"
### Preparation
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
Preparatory steps to start this lesson
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
[Step through content in blocks]
knowledge became the focus of AI and its businenss impact became acknowledged
## [Topic 1]
revival of connectionism (NN) behind the scenes, in research
hopfield net
backpropagation
applied neural networks
### Task:
## 1987 - 1993: AI Chill
hardware had become too specialized
moving into an era of personal computers - computing becoming democratized
Work together to progressively enhance your codebase to build the project with shared code:
## 1990s: AI based on Robotics
```html
code blocks
```
To show real intelligence AI needs a body
✅ Knowledge Check - use this moment to stretch students' knowledge with open questions
## 1993 - 2011
Same issues start to be solved
excessive data
huge compute power
more powerful algorithms
better able to frame question
## [Topic 2]
## Now
## [Topic 3]
AI started as a single area, now there are may 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
@ -54,4 +79,6 @@ Optional: add a screenshot of the completed lesson's UI if appropriate
## Review & Self Study
**Assignment**: [Assignment Name](assignment.md)
[Check out this podcast where Amy Boyd discusses the evolution of AI](http://runasradio.com/Shows/Show/739)
**Assignment**: [Create a timeline](assignment.md)
Jen Looper
4 years ago