AIQ by Nick Polson & James Scott

We are interacting with dozens of smart systems on a daily basis today. AIQ tells us the story of how we got here in 7 parts:

Story 1: Recommender Systems
This is the story of Abraham Wald and his work in WWII. Initially, it looks like a case of missing data, but with the assistance of conditional probability and near close simulations (where, supposedly, actual bullets were fired!), Wald was able to fill in the gaps. This goes on to build the case of recommender systems which is widely used today. (Netflix, Amazon..)

Story 2: Regression
Did linear regression help measure the Universe? Pulling up the concept ‘the principle of least squares’ dating back 200 years, it gives a model exuding simplicity. We have moved way beyond fitting straight lines, but that is were it all started.

Story 3: The Bayesian Paradigm
Searching for a needle in a haystack? Thomas Bayes might help! This centuries-old thought can help us find a lost submarine, avoid crashing into Kangaroos (autonomous driving) and even pick the right wealth manager.

Story 4: Natural Language
Natural Languages are more complex than Python. A WWII veteran thought of teaching a computer to understand human speech more than 50 years ago. What followed were decades of bloated rules, lack of robustness and attempts to resolve ambiguity in processing textual data. Things have started looking up in the last decade, we have Alexa!

Story 5: Newton
A story is never complete without Newton. In the late 1600s, England was losing silver via smart manipulation of its coinage . There were famous trials to detect anomalies in a production line. However, Newton couldn’t get it, nor did England and the trial was a failure. De Moivre might have been able to help… maybe they despised consultants.

Story 6: AI & Healthcare
Florence Nightingale became a legend after the Crimean War in the 19th century. Her reliance on statistics is what helped her stand out. But, it did little to change the system back then. The problem continues even to this day and it is something to think about.

Story 7: Assumptions
In the end, it is all about the assumptions that we make. These assumptions, like humans, age and become obsolete over time. It requires new thinking and infusion of youthfulness. Hence, AI can be thought of as ‘ever-evolving’ and likewise has a story to tell…..

Hope AI continues to improve and add value to our lives.

(PS: this is a personal interpretation of the facts presented by the authors)

Data science enthusiast