Sunday, July 5, 2015

Two Amazing Books

First up is How to Invest Your Time Like Money by Elizabeth Grace Saunders. So just when you thought that Elizabeth had shared all imaginable wisdom on the subject of time investment, she comes back with yet another tour de force of a book. What had amazed me about her first book—The 3 Secrets to Effective Time Investment—was that I could randomly open the book to any page, and be guaranteed a nugget of wisdom. Just to make sure the magic was still working :) I opened the book randomly to the page which leads off a discussion (entitled Routines Require Intentional Practice), and found this insight that I'm partially excerpting here:
At first it will take a great deal of mental and emotional fortitude to even want to start putting these routines into practice.... It's like hacking a new pathway through the jungle of the day when you were used to strolling down a well-established trail or like breaking up scar tissue and retraining your muscles when your body developed bad compensation techniques after an injury.... Ultimately, though, strengthening simple routines leads to a life where you consistently achieve more success with less stress.
~ The 3 Secrets to Effective Time Investment.
This stellar follow-up book—How to Invest Your Time Like Money—is every bit as good. If anything, even more so! Each chapter begins with a delightful quote, of which I'd like to share a couple:
The difference between who you are and who you want to be is what you do.
Time is an equal opportunity employer. Each human being has exactly the same number of hours and minutes every day.
~Denis Waitley, writer
Much like its predecessor, this book is eminently down-to-earth, and packed with eminently sensible advice. In echoing what another reader has lucidly observed in their online review, I'm compelled to say that this will be a go-to resource for when I need help to re-calibrate what's important.

This book is highly recommended—Much like its predecessor, it is far from stuffy; it entertains even as it instructs. For example, here's the delightful quote that leads of the second chapter (Identify Your Time Debt), in the words of Wilkins Micawber, a fictional character from Charles Dickens's 1850 novel, David Copperfield:
Annual income twenty pounds, annual expenditure nineteen pounds nineteen and six, result happiness. Annual income twenty pounds, annual expenditure twenty pounds nought and six, result misery.
~Wilkins Micawber, David Copperfield

Of the two amazing books that I want to bring to your attention, the second one is a wide-ranging discussion of all things probability. Hang on, please, lest the mere mention of the word probability (erroneously) lead you down the path of contemplating boredom...

While ostensibly academic, this book is amazingly well-written and engaging. Even though my background is in engineering—both my BS and MS degrees are in engineering, with emphasis on computation and software—I can still appreciate the engaging style and sheer logical beauty with which the themes are explored in this book, and how ideas are tackled and presented with peerless reasoning and solid arguments. Here is a prelude—by way of the following excerpt—to the cool stuff that awaits you within its pages:
Models have practical uses of a quite different type. Many people are fond of saying, They will never make a machine to replace the human mind—it does many things which no machine could ever do. A beautiful answer to this was given by J. von Neumann in a talk on computers given in Princeton in 1948, which the writer was privileged to attend. In reply to the canonical question from the audience [But of course, a mere machine can't really think, can it?], he said: You insist that there is something a machine cannot do. If you will tell me precisely what it is that a machine cannot do, then I can always make a machine which will do just that!
I learned about this book—Probability Theory: The Logic of Science—from Eric Jonas in an interview that he gave to Sebastian Gutierrez (a data entrepreneur with a background in math at MIT). Thank you again, Eric! So Eric notes in his advice...
...understand probability theory forwards and backwards. I’m at the point now where everything else I learn, I then map back into probability theory. It’s great because it provides this amazing, deep, rich basis set along which I can project everything else out there. There’s a book by E. T. Jaynes called Probability Theory: The Logic of Science, and it’s our bible. We really buy it in some sense. The reason I like the probabilistic generative approach is you have these two orthogonal axes— the modeling axis and the inference axis. Which basically translates into how do I express my problem and how do I compute the probability of my hypothesis given the data? The nice thing I like from this Bayesian perspective is that you can engineer along each of these axes independently. Of course, they’re not perfectly independent, but they can be close enough to independent that you can treat them that way.
There is one other book which embodies some of the same amazing qualities—engaging style, fantastic material, and sheer loveliness of presentation—that I see in the Jaynes book. And that book is entitled The Nature of Computation by Cristopher Moore and Stephan Mertens (Oxford University Press). It, too, may well be worthy of your time invested in exploring it...

Enjoy :)


  1. Probability is interesting not only because it is so fundamental to human decision-making, but also because so often it is counter to intuition.

    The classic example is possibly the monty hall problem and the Ask Marilyn column that led to such controversy. I remember reading that column as a kid.

    Here is a nice article summarizing the result:

    1. Thanks for the thought-provoking comment - For the longest time, I've had this gnawing feeling that the study of probability has much to offer; perhaps my initial encounters (during my undergraduate years in Houston) with deadly boring books on the subject had scared me away ;)

      Anyhow, reading this book—Probability Theory—is changing my entire conceptualization of this subject. Probability, as I'm learning, is deep, much as it is wide at the same time.

      Aha, good old Monty Hall problems, and the "Ask Marilyn" column... Thanks, also, for sharing the link @ priceonomics.