Automate This - critical summary review

Aimed at both technical enthusiasts and laypersons, this book demystifies complex computational concepts, presenting them through engaging narratives and real-world applications that highlight the omnipresence and significance of algorithms in today's society. Steiner specifically mentions that algorithms are not confined to abstract mathematical theories but are deeply integrated into everyday activities—ranging from the music we enjoy and the medicines we take to the games we play.

One of the standout narratives in the book is the story of Thomas Peterffy, a trailblazer in automated trading. Peterffy's journey from a self-taught programmer to a major innovator on Wall Street exemplifies the profound impact that algorithms can have on financial markets. Steiner details how Peterffy's development of sophisticated trading algorithms enabled the rapid comparison of security factors and the execution of buy and sell orders at unprecedented speeds, effectively outpacing human traders.

The rise of algorithmic trading and how technology reshapes Wall Street

Throughout the chapters, Christopher Steiner tells the fascinating story of Thomas Peterffy and his revolutionary impact on Wall Street through the creation of algorithmic trading. The narrative begins in 1987 when a Nasdaq employee, referred to as Jones, visits Peterffy's small but rapidly growing trading operation. Jones expects to find a typical chaotic trading floor filled with traders shouting buy and sell orders, but instead, he is puzzled by the quiet, efficient environment with only a single Nasdaq terminal and an IBM computer.

Peterffy, an unassuming man with a background in computer programming, had developed the world's first fully automated algorithmic trading system. This system used code to analyze market data and make trading decisions at a speed and accuracy far beyond human capability. By splicing a data feed from the Nasdaq terminal into his IBM computer, Peterffy’s algorithms could detect and exploit price differences in the market, generating consistent profits with minimal risk. His system could also execute trades faster than human traders, who were prone to mistakes or delays.

The story illustrates how Peterffy innovatively bypassed Nasdaq's manual trading rules. When the Nasdaq caught on to his setup, Peterffy devised an even more inventive solution: a mechanical typing machine that could physically type trades into the terminal, fulfilling Nasdaq's requirement while maintaining the speed and precision of his algorithmic trading system. This contraption, which included a Fresnel lens, a camera, and an automated typing machine, left the Nasdaq inspector baffled but technically compliant with the rules.

The story of Thomas Peterffy is not just about technological innovation but also about the transformation of Wall Street. Peterffy’s approach, which replaced human intuition and reaction with data-driven algorithms, was the first step in a broader movement that led to the dominance of computers and automation in financial markets. His journey from a computer programmer to one of the wealthiest people in America highlights the power of ingenuity and technology in reshaping entire industries.

How ancient math shapes our future

Steiner points out the renaissance of high-level mathematics in today's world, particularly on platforms like Y Combinator’s Hacker News, where experts from various fields discuss complex mathematical concepts like Gaussian functions and Boolean logic. He suggests that the modern algorithmic takeover of industries such as finance, tech, and customer service stems from theories developed over 250 years ago.

Humans have been using algorithms for thousands of years, far before the word was coined. Steiner delves into historical examples, explaining that algorithms, fundamentally, are sets of instructions to achieve an outcome. Ancient Babylonians used algorithms in law, medicine, and even in attempts to predict the future. He highlights that even simple tasks like playing tic-tac-toe use algorithmic logic, and this principle extends to modern high-frequency trading algorithms, which help determine stock market actions.

The term "algorithm" comes from the Persian mathematician Al-Khwarizmi, who authored one of the earliest books on algebra. Steiner notes that Al-Khwarizmi’s work formed the basis for systematic and automatic calculation, shaping how algorithms function today. One of the oldest known algorithms, from ancient Sumer, was used for dividing grain among men. Steiner underscores how Euclid’s algorithm, developed around 300 BC, remains relevant today for tasks such as encryption in modern computing systems.

Steiner gives significant attention to Gottfried Leibniz, a polymath who pioneered binary arithmetic, the foundation of modern computing. Leibniz envisioned a calculating system using only 0s and 1s, which he believed could be applied to any logical or arithmetic process. His binary system laid the groundwork for the algorithms that power today’s digital technologies. Leibniz als o proposed a form of logic that would reduce all human reasoning to simple binary decisions, hinting at the early ideas of artificial intelligence.

Leibniz’s mechanical calculator, though unsuccessful at the time, became the dominant model for centuries. His idea that logical reasoning could be reduced to simple mechanical operations inspired modern algorithmic thought. Steiner points out that Leibniz believed most future events could be predicted through examining causal connections, a principle now at the heart of algorithms used in industries like finance.

Using technology for artistic success

Steiner talks about “Epagogix,” a company that employs algorithms to evaluate movie scripts. In a significant experiment in 2004, a major movie studio allowed Epagogix's algorithm to assess nine unreleased film scripts, predicting their box office success. The results revealed that while some predictions were significantly off, others were remarkably accurate. He discusses how Epagogix was founded by two film enthusiasts, one a lawyer and the other a risk management expert, with the aim of mitigating financial risks associated with high-budget film productions.

The algorithm analyzes scripts based on numerous factors such as plot structure, character development, and thematic elements, allowing studios to make informed decisions without the need for extensive market research or internal disagreements. However, the algorithms rely on human input to evaluate the script, raising the question of what would happen if AI could generate scripts independently. Steiner challenges the perception that creative professions—often associated with the "creative class"—are immune to automation.

Steiner also introduces “Ben Novak,” a musician and writer who was initially struggling with a modest lifestyle, represented by his 1993 Nissan Bluebird. Novak's passion for music led him to a technology report on an algorithm developed by a Spanish company called “Polyphonic HMI,” which could predict the commercial potential of songs. Intrigued, Novak submitted his song "Turn Your Car Around" to the algorithm, which rated it an impressive 7.57, indicating strong hit potential.

Steiner highlights how Novak's experience exemplifies the transformative power of algorithms in identifying talent and predicting success in creative fields. He recounts the background of Mike McCready, the creator of the algorithm, who had a varied career trajectory, from developing a unique watch design in Spain to becoming a marketing director for venues that hosted major music acts.

McCready's journey reflects the blending of creativity and technology, showcasing how algorithmic insights can redefine traditional pathways in music and potentially reshape the industry landscape. The author articulates the evolving role of algorithms in creative industries, illustrating how they can enhance decision-making, predict outcomes, and even foster artistic creation. He raises critical questions about the future of creativity and the potential displacement of human roles by AI.

The secret to trading – why algorithms thrive on speed

Steiner asserts that the essence of algorithmic trading lies in the ability to eliminate latency—the time delay between data transmission and processing. This urgency for speed has led to a technological arms race among traders, where superior algorithms must be complemented by the fastest possible hardware and telecommunications lines. He draws attention to an intriguing event in 2010 when two men constructed a secret fiber-optic cable stretching halfway across the country to gain an edge in this high-stakes environment.

The narrative follows Daniel Spivey, a hedge fund trader tasked with developing an algorithm that exploits minute price discrepancies between Chicago's index futures and the underlying securities in New York. His goal was to execute trades rapidly enough to capitalize on these discrepancies before they corrected themselves. The success of his strategy hinged on having access to dark fiber—raw fiber-optic cables that provide unshared bandwidth for optimal speed.

Spivey faced a significant challenge: while existing fiber lines were overcrowded, dark fiber was scarce and costly. This limitation pushed him to contemplate building a new, private dark fiber line between Chicago and New York. He undertook an extensive and intricate journey to learn about telecommunications, land rights, and the technical aspects of fiber-optic data transmission. He meticulously planned a route that would minimize distance and maximize speed, thus enabling faster trade execution.

Spivey sought a financially capable partner for his ambitious project and approached telecom veteran James Barksdale, who agreed to fund it, leading to the creation of Spread Networks. They faced challenges in obtaining permission to dig across private and public lands, receiving mixed reactions to their proposal. However, they eventually secured the necessary approvals and began constructing a fiber-optic line. They completed an 825-mile fiber line that significantly reduced communication time, establishing Spread Networks as a key player in high-frequency trading.

Embracing technology to enhance human potential

Christopher discusses IBM's Deep Blue, the AI that famously defeated chess grandmaster Garry Kasparov in 1997. This marked a significant moment in AI history, as Deep Blue demonstrated the ability to process 200 million chess positions per second, showcasing raw computational power. However, Steiner points out that despite its victory, the machine's success was more about processing speed than a genuine understanding of chess.

Chess, with its clear rules and finite possibilities, was a perfect test for early AI, but it raised questions about whether machines could ever excel in more nuanced, human-driven tasks. He contrasts chess with poker, a game that relies heavily on human intuition, psychological manipulation, and incomplete information. Unlike chess, poker involves unquantifiable elements like bluffing, making it a much more complex challenge for AI to master. This transition from structured games like chess to the chaotic and unpredictable world of poker is critical to understanding the limits and potential of AI.

Steiner moves on to IBM’s Watson, the AI that competed in Jeopardy! in 2011, illustrating a different kind of intelligence. While chess is a game of strategy with fixed rules, Jeopardy! requires contestants to understand humor, cultural references, and wordplay. Watson's ability to answer questions quickly and accurately was achieved by processing vast amounts of data and applying six million logic rules, which allowed it to outshine human competitors.

However, this victory also highlighted the gap between human emotional intelligence and AI. Watson could process information but lacked the nuanced understanding that comes naturally to humans. Steiner then delves into the complexities of poker, focusing on the work of Tuomas Sandholm, a computer science professor who sought to develop an AI that could play high-stakes poker. Unlike Deep Blue, which relied on brute computational force, Sandholm’s approach involved game theory to model complex human interactions.

Poker’s hidden information, strategic bluffing, and psychological elements presented a far greater challenge than chess. Sandholm's AI needed to simulate human behaviors such as bluffing and reading opponents—an entirely different skill set for AI to master. The complexities of poker, with billions of possible situations, especially in Texas Hold'em, made it a nearly insurmountable task to develop a poker bot that could consistently outperform human players.

In addition to gaming, Steiner explores the transformative potential of AI in healthcare, particularly in fields like organ transplantation. He describes how Sandholm, inspired by Al Roth, applied game theory to improve kidney transplant matches, addressing inefficiencies that left many patients without a donor despite available matches. Sandholm’s algorithm revolutionized the process by considering multiple factors simultaneously, significantly increasing the efficiency of the matching system.

Steiner also introduces the concept of a doctor bot, an advanced AI that could monitor patients continuously and provide more accurate diagnoses by processing a patient's entire medical history in real-time. He argues that AI has the potential to reduce healthcare costs and improve patient outcomes, but he acknowledges the challenges in gaining public trust, especially when it comes to replacing human doctors with machines.

Final notes

While “Automate This” is fundamentally optimistic about algorithms' potential to drive innovation and improve efficiency, Steiner does not shy away from addressing the darker aspects. The book discusses the displacement of jobs due to automation, the concentration of power among those who control sophisticated algorithms, and the ethical implications of relying heavily on machine intelligence.

This balanced perspective encourages readers to consider both the benefits and the societal challenges posed by the increasing automation of tasks. Steiner advocates for enhanced education in mathematics and science to prepare future generations for a world dominated by algorithms. He specifically points out the necessity for individuals to understand and engage with computational technologies to navigate and shape their futures effectively.

One of the book's strengths is its ability to present complex subjects in an accessible manner. Steiner employs entertaining anecdotes, detailed interviews with algorithm developers, and historical stories that make the intricate world of algorithms relatable and intriguing. This approach ensures that readers without a technical background can grasp the significance of algorithms, while those with expertise find the applications and implications intellectually stimulating.

12min tip

If you're interested in understanding the roots of today’s tech landscape, “The Big Score: The Billion Dollar Story of Silicon Valley,” by Michael S. Malone, is a contemporary history that chronicles the birth of Silicon Valley, providing an insider's perspective from one of the first reporters on the tech beat.