Robinhood Math -Noah Giansiracusa

Sub-title: Take Control of the Algorithms That Run Your Life

Once again, I think this was recommended from a YouTube video I watched.  The author is a mathematician who is able to explain statistical analysis clearly.  He introduces basic concepts about how numbers are used to interpret the world and builds on them to explain the many ways they are used and abused when presenting information.  His intent is to provide understanding of the use of math in our complex world and how we can take control over it.

Some terminology / concepts he covers:

- average error rate

- expected value

- covariance

- logarithm

- algorithms

- engagement probabilities

Quotes:

"In the end 2 main factors separate professional gamblers from amateurs: 1) the ability to methodically stick to a plan for a long time, resisting temptation and impulsivity and 2) a gambling strategy that edges the odds enough to achieve a positive expected value."

"To insulate yourself from the risk posed by AI and automation, you want to diversify your skill set. That doesn't mean just learning more skills. It means learning more skills that are as different from each other as possible--skills with minimal covariance between them, so to speak."

"The Kelly strategy [even money bets] provides the optimal betting strategy in the sense that it maximizes the expected value of the logarithm of the winnings"

"if you don't want [a type of content] online, then don't engage [like, comment, share] with it. You too can shape your social media experience in whatever way you want."

Published: 2025  Read: March 2026 Genre: Non-fiction

Hidden Harmonies - Robert and Ellen Kaplan

Sub-title:  The Live and Times of the Pythagorean Theorem

This is a different read.  Two mathematicians trace the history of the discovery and proofs of the Pythagorean Theorem - the one the scarecrow quotes in the Wizard of Oz when he gets his brain:

"The square of the hypotenuse of an isosceles [sic right angled] triangle is equal to the sum of the squares of the other two sides"

That quote was about the extent of my knowledge of the theorem.  This book provided a delightful and quixotic look at its evolution and application throughout history.  I admit I skimmed the proofs and my eyes glazed in parts.  I felt I was listening to a discussion by two experts on a topic I barely understand and yet enjoy their enthusiasm for the subject.

They quote Georg Christoph Lichtenberg "What you have been obliged in discover by yourself leave a path in your mind which you can use again when the need arises."

Quotes:

On math as a craft:

"Should you conclude that so much doggedness, wedded to such inspiration, is beyond your wildest dreams, remember that what you've followed here is the tidied remainder of who knows how many lively conversations -- and conversation among practitioners as devoted to their craft as are cooks to theirs." 

Published:  2011  Read:  November 2017  Genre: Non-fiction, mathematics

Humans Need Not Apply - Jerry Kaplan

Sub-title: A Guide to Wealth and Work in the Age of Artificial Intelligence

READ THIS BOOK.  This is the most thought provoking read I've encountered in a very long time.
The author explains the basics of artificial intelligence applications and how they will be utilized and lead to profound impacts on our lives. It won award as one of the top 10 science books of 2015.

It's easy to read, like the author and you are having a conversation and puts a positive view on the future of the technology.

Published:  2015  Read: February 2017  Genre: Science

The Master Algorithm - Pedro Domingos

I recently began reading on the topic of artificial intelligence (AI) because I’d read where Bill Gates (Microsoft) identified it as one of the three most important career areas of the future.

I was struck by the potential application of AI to the research of genetic genealogy, not for the scientists, but for the genealogist.  The math and science of AI is way, way over my head yet the book's review sparked my curiosity to dive in.

Quotes and notes:
This definition set me on the right track to reading the book.

"...machine learning is about prediction: predicting what we want, the result of our action, how to achieve our goals, how the world will change."  

And this quote gave me a frame of reference.

"The psychologist Don Norman coined the term conceptual model to refer to the rough knowledge of a technology we need to have in order to use it effectively.  This book provide you with a conceptual model of machine learning."

The author addresses 5 Schools of Thought in machine learning (ML) each with a different emphasis and scientific basis:
1) Symbolists - view learning as the inverse of deduction; philosophy, psychology and logic
2) Connectionists - reverse engineer the brain; neuroscience and physics
3) Evolutionaries - simulate evolution on the computer; genetics and evolutionary biology
4) Bayesians - learning as a form of probabilistic inference; statistics
5) Analogizers - learn by extrapolating from similarity judgments; psychology and mathematical optimization.

"Machine learning is the scientific method on steroids - it can test hypotheses in a fraction of a second."  

Reading this made me wonder if ML could incorporate the rules of evidence and decide if a fact or relationship is proven in the genealogical meaning of proof.

"Today, the main limitation of computers compared to brains is energy consumption: your brain uses only about as much power a a small light bulb, while Watson's (IBM's ML) supply could light up a whole office building."

It was comforting to realize our brains are still a much more powerful computer!

The author's explanation of S curves - gradually then suddenly, output increases as a function of input- made me wonder if there is an S curve for DNA inheritance?

"Psychologists have found that personality boils down to five dimensions - extroversion, agreeableness, conscientiousness, neuroticism, and openness to experience -which they can infer from your tweets and blog posts."

That quote made me think twice about what I blog about!

In all, this was a fascinating read and a peak into the future of computing.

Published:  2015  Read: March 2017  Genre: Science