🧠 AI’s Ancient Origins

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Welcome back AI prodigies!

In today’s Sunday Special:

• ⚙️Machines Do as They're Told

• 🔮Blast to the Past

• 🦾Turing Gets an Assist

• 💥Reality Check

Read Time: 6 minutes

🎓Key Terms

• Artificial Intelligence (AI): when machines perform human cognitive functions such as learning, perceiving, reasoning, predicting, and decision-making.

• Turing’s Universal Machine: a simple, abstract computational device intended to investigate the extent and limitations of programming.

• Automaton: a moving mechanical device made to imitate a human being.

• Set Theory: a theory that helps explain what a number fundamentally means.

⚙️MACHINES DO AS THEY’RE TOLD

In this week’s Sunday Special, we venture beyond today’s apps, chatbots, and interfaces to uncover yesterday’s Artificial Intelligence (AI). Most AI recaps begin with the groundbreaking contributions of a British mathematician and computer scientist, Alan Turing. In 1936, Turing introduced the concept of a “universal machine.” As described in this 8-minute explanation, Turing discovered that machines could rely on written instructions to perform actions. This discovery stood in direct contrast to conventional wisdom, which dictated that physical switches and plugs instruct computers. To bring this universal machine to life, Turing drew upon a metaphor of a person sitting in an enclosed box holding 3 objects, each representing a computer component.

• Pencil → Read-Write Memory

• Stack of Blank Paper → RAM

• Book of Instructions → Program

In this hypothetical, the only way to change the human’s behavior, or what they write on paper, is to alter the instructions. Now, let’s assume the instructions are a sequence of positive numbers. If the first digit in the series is 8, then the human will find the action corresponding to 8; this is the basis of “IF” statements in programming. After providing a direction (e.g., move left 3 units), the instructions direct the human to another page in the book, or what Turing called another “state” of the machine. This process continues until the book directs the person to “stop.” Thus, Turing described the abilities of computers as the following: to accept, store, manipulate, and process information to produce answers that are readable to humans.

🔮BLAST TO THE PAST

Theoretical breakthroughs tend to precede physical innovations by wide margins. In aviation, theories of fluid dynamics, motion, and modern aerodynamics had to be sorted out before the Wright brothers took flight. Computing and AI were no different. The theoretical origins of AI stretch back to Greek mythology and, more directly, a mini-mathematical revolution in the early 20th century.

Greco-Roman historians often evoke tales about a giant named Talos, made entirely of bronze, who served as the guardian of Crete. His job was to protect the island by hurling large rocks at invading ships and patrolling its perimeter three times a day. According to legend, the god Hephaestus created Talos, an automaton, with the help of a cyclops, and gifted this automaton to Minos, the ruler of Crete. The earliest automata on record were the sacred statues in ancient Greece. At the time, worshippers believed that skilled artisans had endowed these figures with human intelligence, enabling them to display wisdom and emotions. Additionally, an English scholar named Alexander Beckham claimed the ancient Roman poet Virgil had constructed a palace containing statues with automaton-like qualities.

Following their forefathers, alchemists, regardless of creed or civilization, conceived of artificial beings during medieval times. In Of the Nature of Things, Swiss scientist Paracelsus claimed that an evil concoction of bodily fluids could create an “artificial man.” Eleazar of Worms, a 13th-century Jewish polymath, shared the formula for creating a golem—insert a piece of paper with a god’s name into the mouth of a clay figure. In Islamic tradition, takwin, or creating synthetic life in a laboratory, was a primary goal of Muslim alchemists.

Human conceptions of artificial creatures continued into contemporary times, with Mary Shelley’s legendary novel Frankenstein taking center stage in the early 1800s. Well-written fantasies captured the public imagination, but building AI systems seemed eons away. A priceless question remained: is it possible to use the most objective, truth-based tools known to man (i.e., mathematics) to produce something magical?

🦾TURING GETS AN ASSIST

Though mathematics was the purest science at the turn of the 20th century, it was riddled with inconsistencies. For instance, Russell’s paradox left the brightest minds scratching their heads, and mathematicians had seemingly tacked calculus and set theory without demonstrating interrelatedness. Before AI-based systems could be conceived, mathematics had to be reexamined and rebuilt. As tantalizing as it may seem, this project can be summed up as follows: prove that one plus one equals two. In 1910, two mathematician-philosophers, Bertrand Russell and Alfred North Whitehead, set out to execute this Orwellian proposition.

Though they failed to prove simple addition, Russell and Whitehead made an AI-relevant discovery. Through their seminal piece, Principia Mathematica, they concluded that logical reasoning was more pure than mathematical reasoning, as math was based on rules determined by humans, whereas logic was rooted in irrefutable truth. Because math is essentially a collection of arbitrarily selected symbols and numbers, any form of mathematical reasoning could be mechanized. Sound familiar? Turing’s universal machine operationalized the findings of Russell and Whitehead.

In the decades following Turing’s discovery, experiments led to the development of the first public machine translation demonstration of a Russian-English machine translation system in 1954. Although the machine could only translate about 250 words, the successful experiment was an influential demonstration of machine translation. It inspired the Dartmouth AI Workshop of 1956, where the term AI was coined.

💥REALITY CHECK

Since the 1950s, AI has come to life! Recent advances in mathematics, computational efficiency, and data science have led to the proliferation of AI systems, showcasing predictive, analytical, and generative capabilities. Daily news cycles reporting innovation and iteration make it easy to forget the distinctive imagination that made it all possible. Without dreamers, doers do in vain.

📒FINAL NOTE

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