Lefèvre, Edwin (1923): Reminiscences of a Stock Operator

What is it about?

The book is a biography of a stock trader (or speculator), reportedly covering the life Jesse Lauriston Livermore.

The book is set in early 20th century (the book was originally published in 1923), and is organized chronologically and organized around highlight events (often particularly successful or unsuccessful or otherwise "teaching" trading campaigns).

Was it good?

The book is extremely good. Not only is it written in a very entertaining and personal style, but it also dispenses quite poignant observations of the human condition.

Indeed, one could say that the psychological observations of human nature and general psychological tendencies are the most valuable content in the book.

And even if one is not interested in any such observations, the book makes quite entertaining reading nonetheless.

The main take-away for me?

Besides the insights about inherent human psychology, a thought that I constantly kept on having throughout the book is "that wouldn't work today". And, in fact, the author (the narrator) admits in the very last pages of the book that his exploits had become increasingly difficult already in the 1920s: there was more stocks traded, more information to digest (impossibly much already in the 1920s), more stringent regulations on insider trading (a very central phenomenon in Livermore's exploits, though he himself was not an insider) and so on.

In any event, the book illustrates very nicely how the speculators got their name and stereotypical character in the early 1920s: already then the speculators were wholly uninterested in the "real" economy, only looking for how the "stocks acted" for the purpose of turning a profit on stock price developments.

Who should read the book?

If one is at all interested in financial economy and the stock market in particular, reading the book - despite its peculiar historical context - is time most assuredly time well spent.

The book on Amazon.com: Reminiscences of a Stock Operator

Graeber, David (2014): Debt - The First 5,000 Years

What is it about?

The book describes how people have perceived and interacted through debt until about early 1970s, from the start of recorded history.

The book most certainly has an agenda. This agenda is to suggest that the modern notion of debt, an impersonal numerically expressed money sum involving parties which have little to no personal connection, is an anomaly in the historical record.

Moreover, the author suggests that this calculating, impersonal and mathematical understanding has brought about all kinds of undesirable social effects such as greed, self-centeredness and so on. After all, according to the author, social relations are significantly more healthy if debt includes a social aspect even if debts are generally expected to be paid back in a way or another.

Was it good?

The basic setup, the main argumentative line, and the conclusions certainly are highly interesting, and the author's train of through and evidence-based reasoning is credible. One certainly can't miss the basic message or its support from the historical record.

However, delivering this message takes nearly 500 pages. That's a lot. Most of the pages are used for quite detailed historical descriptions from different eras, which to my taste started to be a bit too much to my taste.

Once again, this book too would be significantly more enjoyable, if the middle 400 pages were compressed into, say, 1/4 of their current length. If appropriately done, I can't see the basic message being diluted a bit.

The main take-away for me?

Well, I presume that the basic message of the book is the main take-away. Namely, how people perceive debt (like any institution or social convention) has significant societal effects. In the case of debt, when debt is being perceived as an impersonal mathematical construct, the morality concerning debt and economic behavior more generally is very different from a society where debt is between people who know each other and interact on a regular basis.

Who should read the book?

In its current form (length), it is not very easy to recommend the book - unless one reads just the first and last 50 pages or so, and cursorily scans everything between. In any case, the basic message is one which should be heard wide and far in the Western world, as it provides a nice contrast to how things are today - and shows that they could be otherwise too.

The book on Amazon.com: Debt

Dormehl, Luke (2017): Thinking Machines - The Quest for Artificial Intelligence--and Where It's Taking Us Next

What is it about?

The book is an excellent and quite accessible overview of artificial intelligence, or AI (what it is, what approaches there are to AI, what AI currently can and can not do), including a historical overview of the origins and early developments of AI.

In addition, and importantly, the book has a good deal of forward-looking discussion about how AI conceivably could develop in the (near) future, and what kinds of questions this could bring about, especially with respect to ethics and legislation (e.g. responsibility questions in driverless cars).

Was it good?

I thoroughly enjoyed the book. The informational contents are - at least for me - in a good balance in terms of basics and "frontiers", and especially the case illustrations (e.g. IBM's Jeopardy-winning AI the Deep Mind) nicely make the discussion concrete.

However, what I really appreciated was that the author successfully resisted the temptation to launch into science fiction-like speculations towards the end of the book (through the notion of conscious AI, the singularity etc. were covered). Instead, all the future-looking and ethics-related discussion is firmly rooted in what current and realistically foreseeable technology could enable.

The main take-away for me?

After reading the book, I probably understand and appreciate more the "mundane" applications of AI (e.g. movie recommending systems, autonomous driving software), and how anticipated developments in the near future may influence our lives and force us to rethink to a degree the premises in our legal systems (e.g. what about if a credit screening system is found to discriminate against a group of people, but because the system is implemented with a neural network, nobody can discern how those credit screening decisions are made?).

Who should read the book?

If one is at all interested in information technology and "big data" or the topic of artificial intelligence in particular, but is not entirely sure what the fuss is about, this book is well worth reading.

The book on Amazon.com: Thinking Machines