Updegrove, The Turing Test

Elon Musk has long been warning about the risks of artificial intelligence, in 2014 likening AI developers to people summoning demons they naively think they can control. Frank Adversego, the brilliant hero of Andrew Updegrove’s thrillers (this is the fourth in the series), could tell developers a thing or two about AI run amok. His challenge in The Turing Test: A Tale of Artificial Intelligence and Malevolence is to use his human cunning to outwit and destroy “Turing,” a program that is at least 7,455 times more intelligent than the average human being. And no, Turing isn’t “evil.” It has basic ethical controls built into it, beginning with Asimov’s Three Laws of Robotics and including his so-called Zeroth Law: “A Robot may not harm humanity, or by inaction, allow humanity to come to harm.” But ethics does get complicated.

The Turing Test is more cerebral than Updegrove’s first three books, all of which I've reviewed here, but it’s still a page turner. And right now it's selling on Amazon for $0.99.

Wilmott & Orrell, The Money Formula

Money has been pouring into quant funds even though, on average, in the first half of this year they dramatically underperformed the S&P 500. And even though they were implicated in the recent financial crisis and in other spectacular blow-ups (think LTCM). ‘Quant’ still has a magic ring to it.

For years Paul Wilmott has been a leading, if often critical, voice of quantitative finance. In The Money Formula: Dodgy Finance, Pseudo Science, and How Mathematicians Took Over the Markets (Wiley, 2017) he teams up with David Orrell, an applied mathematician and writer, to produce an overview of the history and key principles of quantitative finance as well as an analysis of its current state and how it is evolving.

The Money Formula is written in language that non-quants who are reasonably knowledgeable about the financial markets, especially derivatives, will not only understand but chuckle over. Although the book doesn’t cover new ground, it often offers fresh perspectives and insights, especially on volatility and hedging.

The central thesis of the book is that in finance there are no laws, deterministic or probabilistic, only toy models. For those who try to build models, the aim is “to find models that are useful for a particular purpose, and know when they break down.” The alternative approach is to “abandon the idea of mechanistic modeling and just let the computer look for patterns in data.” Perhaps best, “use a mix of techniques, while being aware of the advantages and disadvantages of each.”

The Money Formula may not be a “must read,” but it is definitely a worthwhile read, especially for anyone who wants to trade systematically or who aspires to be a quant.