

File Size: 1077 KB
Print Length: 268 pages
Page Numbers Source ISBN: 1591844924
Publisher: Portfolio (August 30, 2012)
Publication Date: August 30, 2012
Sold by: Digital Services LLC
Language: English
ASIN: B0064W5UAS
Text-to-Speech: Enabled
X-Ray: Enabled
Word Wise: Enabled
Lending: Not Enabled
Enhanced Typesetting: Enabled
Best Sellers Rank: #335,080 Paid in Kindle Store (See Top 100 Paid in Kindle Store) #95 in Books > Computers & Technology > Computer Science > Computer Simulation #273 in Books > Computers & Technology > Programming > Algorithms #561 in Kindle Store > Kindle eBooks > Nonfiction > Science > Technology > General & Reference

For a book that is heavily publicized and garnered reviews in major business magazines, this book flatters only to deceive. Unless you are a total novice in this space, a reader is unlikely to find any new examples or insights from the author's treatment of algorithms. Most examples have been discussed ( in terms of technical content and impact on business models, society, behaviors) in magazines like Wired, PopSci and NYT technology pages many years ago. The dated references to recommendation engines like dating websites, those focused on music (Pandora, etc) are all superficial and provide no new insights or a critical appraisal of where those technologies are headed.The author also overly focuses on Wall Street based scenarios to explain algorithms - he does a particularly bad job in representing algorithms as nothing more than fast calculators - that too, with a fundamentally flawed example based on option trading (I sincerely hope that the author never tried the trade he has mentioned in the book). That misguided example reflects poorly on author's understanding of algorithms and inadvertently proves one thing - algorithms are only as good as the thought that went behind its design.Despite the superficial treatment, the author makes a few important points in the last two chapters on the need for more skill development in "STEM" disciplines and makes an argument that medical diagnostics is the next main area where algorithms are poised to expand. The discussion is very rushed and provides no meaningful action plan. Moreover, the author fails to acknowledge the vast amount of data that an individual is generating on a daily basis - and concepts of "big data" that could shape how new avenues for algorithms can evolve.
Length: ~220ppContents:Introduction1. Wall Street, The First Domino - this chapter tells the story of Thomas Peterffy, who was apparently the major innovator in the last 40 years in algorithmic trading. The guy is now a billionaire. It's a VERY interesting story.2. A Brief History of Man and Algorithms - This spends a lot of time discussing mathematicians of the past, and how their innovations led to3. The Bot Top 40 - Talks about how algorithms can be used to detect which songs are likely to be hits. Some great stories.4. The Secret Highways of Bots - The main idea of this chapter is that the SPEED of algorithms is what makes them so valuable. The majority of the chapter is spent telling the story of how two guys spent $200 million building a new communications line between Chicago and NYC so that they could shave 4 milliseconds off the amount of time it took to communicate between the two cities, which gave a HUGE advantage in algorithmic trading. The plan worked and the guys made a ton of money off it.5. Gaming the System - Algorithms in gaming (poker, etc.)6. Paging Dr. Bot - Gives examples of companies that are using computers to replace a LOT of the work now done by doctors.7. Categorizing Humankind - Tells the story of how NASA used algorithms to detect which astronauts would work well together during the 1960s/70s missions, and how this same idea is now being used to create algorithms that can detect your personality over the phone and connect you with a customer service representative whose method of communication matches yours. Very interesting.8. Wall Street Versus Silicon Valley - Talks about how Silicon Valley and Wall St. compete for talent9. Wall Street's Loss is a Gain for the Rest of Us10.
Steiner's approach to this topic is excellent, taking us through the widely acknowledged but little understood algorithms of Wall Street, to the Mathematical foundations of computer programming, and then to areas more likely to impact the lives of average readers, from commercial uses and finally to algorithms' potential uses in diagnostic medicine for both body and mind.Unfortunately, the end result is a jumble of hyperbole, gaps in reasoning, outright plugs for certain companies, outdated examples, and just plain inaccuracies. Several readers, for instance, have commented on the confounding explanation of a delta neutral trade. I'm pretty confident that even the meatiest of the "meatheads" (Steiner's term) in the pit were competent enough to lock in a conversion or reversal (something that eludes Mr. Steiner). His explanation isn't just wrong, it entirely misses the concept of delta neutral, and so simultaneously denigrates both the conventional traders and the brilliance of Mr. Peterffy's arbitrage. This sad theme is repeated throughout the book. Steiner's world is one in which a handful of shining pillars of genius wade through a sea of crusty, intransigent morons, which although possessing a kernel of truth, grossly oversimplifies and thus does no justice to the push for and against the expanded use of algorithms.I was also dismayed that although Steiner acknowledges on a number of occasions the dangers of runaway algorithms, he entirely avoids the far more subtle ethical questions of control. "No willy-nilly tests, no gut feelings, just data in, data out" says Steiner of a rather aggressively imagined Dr. Algorithm. Unfortunately, there is no such thing as neutral data. Which data are being used? How are they being interpreted? Who is making these decisions?
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