Introduction in Artificial Intelligence: Labbook 2020

November 9, 2020

Read this excellent tutorial on fairness in Machine Learning. Equal opportunity is a far weaker notion than equal odds, but requires a much lower threshold.
In her presentation, Hinda pointed to Verma & Rubin, FairWare 2018 paper.

Read this article about 4 important philosophical questions which should be answered for AI. It pointed to the Boltzmann brain.

The students suggested this crash course on symbolic systems. The consultant on the AI part was Lana Yarosh.
Jabril lists the following strong points of symbolic systems:
1. A human expert can easily define and redefine the propositional logic in an expert system.
2. Expert systems make conclusions based on logic and reason, not just trail-and-error guesses like a neural network.
3. An expert sytem can explain its decisions by showing which parts were evaluated as true or false.

A nice overview of AI competition is from IBM Debater. At the end IBM lost with -17%, but it gives a nice overview.

Bestaat er voor iedere wetenschapper van de artikelen die we opvoeren een wikipedia pagina? Alleen bij de eerste helft:

Melanie Mitchell has a short course on 'Artificial Intelligence: A guide for thinking humans'.

This article describes the Origins of Deep Laerning quite well.
The 'cat paper' that made Google Brain famous, showing the eigenfaces of cats and the eigenbodies of humans as high-level features.