Information became a resource when minds began storing it externally. Exchange dispersed it into categories, while organization expanded them into concepts, languages, and cultures until about two and a half thousand years ago when it matured into its latent form: Intelligence. The mind credited for harnessing it professed all is water, provoking other inadequate guesses for a monistic substance, such as air or fire. These ancient minds are commonly cited under the mutual language they shared in the Mediterranean, although a simpler, childlike commonality was linking them across their disconnected city-states. They enjoyed inciting patterns, playing with sequences. Without the constraints of religion or mythology, selecting the ultimate substance that incites everything, by nature, is an unbeatable game. Perfect for insatiable players—the kind needed to push the paradoxical fate of Intelligence, discovering and/or/not creating itself, into perpetual motion. In the ancient world, a player with access to chemicals could use a catalyst. One acquainted with theatre actors used an inciting incident. One with a hydraulis used a musical tonic, triggered with a programmable console. This manifested into its simplest, most democratized form in the 6th century, an interface that would become epochal. One that allowed a player to be an emperor, a grifter, a filmmaker, or a kid in the Bronx orchestrating and inciting sequences in the silence of their bedroom…the players credited for elevating the resource of information until it defined an age.

The Information Age

As a resource develops, pedantic steps impatiently become leaps. Much like the ancient chemists bettering the metallurgists in creating gold, prescient leaps usually lack integrity, but they make up for it in influence. In the late 18th century, the excitement of machines given the power of man, like the Spinning Jenny—giving 2 hands the power of 10—was revolutionizing industries. Comparatively, machines with the power of our mind just weren't as practical as surefire machines made for drilling, milling, or refining. In 1770, an Austrian clerk named Wolfgang von Kempelen, undeterred by practicality, decided to create one. This automaton would grant him a new reputation in the royal court. But retrospectively, like the ancient “chemists” creating gold, it's fair to refer to them as what we can now see their actual achievement was: an excellent illusion. This late 18th-century illusionist created a life-size mechanical humanoid dressed in Turkish garments. The mechanical Turk was built atop the game designed in the 6th century for any kind of player, known as chess.

Artificial Artificial Intelligence

The chess-playing automaton—known as the Mechanical Turk—was beating esteemed figures no less recognizable than the legal tender they have since adorned. Optimists were enthralled, while skeptics were pushed to great lengths to subvert the greatest machine the world had seen. A young reporter ended up establishing a new literary genre altogether after(un)covering the story. The eminence grise behind(or rather, within) the automaton, which remained secret for long after Kempelen's time, was a man hiding within a compartment inside the base of the chess table. If not for a curious biographer half a century later, this man would have remained hidden in an unchecked compartment of history as well. During exhibitions, sections of the Turk were opened intermittently to reveal its faux autonomous clockwork. The hidden player used a variety of crawl spaces, levers, and magnets to operate the enchanting animatronics, all while being able to give the best chess player of the time (Philidor) “his most fatiguing game of chess ever”. There were practically no known records of this hidden player until the biographer found that he was Johann Baptist Allgaier of Vienna. An unassuming player held in high regard by the local chess cafes. The biographer found that Johann had been burdened with breathing problems after fighting in the Napoleonic wars, and in need of money later in life, was approached by an exhibitor hiring the brain for his chess machine. A lowly player, undocumented and forgotten to history, using crawl spaces and levers to play chess against one of the most polarizing, universally known megalomaniacs during the peak of his power. Just a few years before, these players faced each other in the Napoleonic War. This would be their second encounter, where one indirectly battles the other, conducting a methodically arranged fleet from out of sight.

Details vary (Napoleon requested it to be off record), but consistent accounts from a range of bystanders assemble the evening: Napoleon initially tries to cheat, as a keen assessment or perhaps just amusement, or both. After the Turk swiftly rejects the invalid moves, Napoleon persists in making the first move of the game. Of all the possible openings, he chooses one befitting of a boy born on an annexed island, with a desire to acquire power in meteoric fashion. The Scholar's Mate Opening is either the quickest way to leverage power against an opponent or the most impatient. The Scholar's Mate (now sometimes referred to as the Napoleon) is a tactic that immediately threatens checkmate within the first few moves of a game—obvious, yet effective against negligent opponents. The combative mastermind's impulsive attacks and reliance on tactics would inevitably lose to strategy. The Turk(Johann Allegier) had a chance to checkmate Napoleon towards the end of the game, but instead took Napoleon's most powerful pieces, drawing out a swaggering finish as flamboyantly dominant as the emperor himself. Whether the strategist—the one crammed inside the table—overlooked the possible earlier checkmate or elongated the game purposefully, cannot be known for sure.

The creator of the Turk wasn't in attendance that evening, in fact, he had attempted to disassemble it decades before. Kempelen resentfully shelved the hoax as its unexpected celebrity status began to eclipse his other automata. A “rude and churlish showman” (according to Beethoven) waited until Kempelen's death to resurrect the Turk, and re-exhibit it under his own name: “Maelzel's Chess Player”. This exhibitor has a paper trail of palimpsests (to this day, he is still credited for others' inventions, most notably the metronome). Maelzel added a few gimmicks to the Turk, including a crude speech device that announced when opponents were in “check!”. This further obscured Kempelen, whose other automata were the first to speak using anatomically accurate components. Kempelen may have despised the Turk, but its success gave him the creative freedom to compile his ideas and designs for over 20 years into a lesser-known book. A book that happened to leave an indelible mark on a particular family of linguists. The Bell family used a system called “Visual Speech”, devised by their father, to communicate with their deaf mother. The Bell children were taken to see reconstructions of Kempelen's automata and received a copy of his comprehensive book. Throughout their adolescence, they assembled the mechanical larynx, diaphragm, and other parts detailed and illustrated by Kempelen. The boys configured their first automaton to say “Mama”. Only one sibling, Alexander, survived into adulthood, marrying a deaf woman himself. Alexander refined these speech-producing contraptions with the advent of electricity, patenting his system in 1876, called the telephone.

Automata

Before Kempelen's automata was his posthumous liaison to a family of practitioners, Maelzel's sensational marketing was a conduit to other types of players. When exhibiting in England, a recent graduate struggling to find a purpose after attending Cambridge University sat across the updated Turk. Charles Babbage—like most 20-something-year-olds—was in search of where and what to aim themself and their new college degree towards. Soon after playing (losing twice) against the Turk, Babbage wouldn't just find an aim, but a trajectory toward becoming “The Father of the Computer”. Babbage set out to achieve what the Turk deceitfully claimed to have: the first machine that could possess our mind as well. In 1822, Babbage devised the plans for his “Difference Engine,” which he expanded upon for over a decade into the programmable “Analytical Engine”. With sufficient funding, this would have been the first machine capable of executing any general sequence or pattern. Babbage's assistant, a young unsung woman named Ada Lovelace, wrote the theoretical language this sort of machine would speak. Ada was the first computer programmer. Like Kempelen, Babbage and Lovelace died before either of their engines' full potential could be realized. However, the integrity and detailed plausibility of the plans were enough to push the next players to move.

The next player was made in Spain but was largely overshadowed by household names in the West. Nikola Tesla and Leonardo Torres Quevedo respectively introduced the world to remote control capabilities with a toy boat at the turn of the 20th century. Quevedo's vessel introduced the world to the most advanced remote control technology it had seen. His remote controls and interconnected transportation systems were foundational, but not popular enough to have his surname commemorated on automobiles a century later. In the 21st century, a man behind interconnected transportation systems and advanced remote control capabilities makes him the richest person in the world. When Elon Musk's company Neuralink introduced the world to the most advanced remote control technology it has seen, they happened to use the same game initially computerized by Leonardo Torres Quevedo. El Ajedrecista(The Chess Player) built in 1912, was the first electromechanical machine able to play an endgame of chess (and win every time), making Quevedo the rightful father of the first computer game. This chess-playing machine wasn't an illusionary hoax or theoretical plan, it was real, enough to intrigue established scientists. A professor from MIT who formed the field of “Cybernetics” (and now known as “The Father of Automation”) eventually came out to play against Quevedo's chess machine in 1951. Academia was following close behind, with many sensing paternal opportunities. A player named Claude Shannon gathered with a few colleagues in the summer of 1956 to coin an official term separate from the previously popularized “Automata” or “Cybernetics”. They came up with “Artificial Intelligence”.

Digital Paternity

While attending MIT, 22-year-old Claude Shannon wrote what is now considered the most significant master's degree thesis ever written. It devises the DNA for a digital world. He later downplayed it as pairing two existing concepts that others didn't notice a connection between at the time. Claude spent much of his youth on a chessboard, “pursuing interests without much regard for final value or value to the world, spending lots of time on totally useless things.” This was his explanation for why he entertained an obscure concept with no practical use, devised by a man a century before. Claude then proposed it as a solution to the question computer scientists of his own time couldn't formally answer.

Soon after we understood which materials the phenomena of lighting preferred—like we once did with fire—harnessing it meant switching it on or off. A switch that could direct electricity into reactive components that heat, illuminate, or spin. Connecting a few of these components could wash clothes, compress refrigerant, or a variety of other contraptions that turned on the Machine Age with a flip of a switch. The properties of electricity complemented aspects of physical procedures, but it made even more sense for mental ones. What happens when an electric switch isn't connected to heating, spinning, or illuminating…but to another switch? The Machine Age turns off as quickly as it turned on.

A switch that turns on from previous switches; Previous conditions determine which procedure to switch on, much like a thought process. With enough patience and trial and error, a “computer” with a basic hardwired function could be meticulously configured. But forget re-programming or recreating this pile of switches and wires, let alone the potential of scalability. Claude Shannon sensed that a formal structure, a methodology, was necessary. He found it in the rigidity of algebraic formulas devised a century before, by a relatively unknown mathematician attempting to be a philosopher, or vice versa. The formulas symbolized how a mind reasons, upon the basis that a logical decision depends on conditions that are either true or false: Room on fire AND Front door blocked = Use back door. Using a “1” to represent true and a “0” for false, a logical procedure can be written symbolically, as a+b=1. In some situations, only one variable needs to be true: Opponent moves chess piece OR you're put into check = Your turn to move…a*b=1. It is also logical to consider what not being true can decide: King is simultaneously threatened while NOT having any safe squares to move to = Checkmate. This became valuable when Claude assigned to electric switches, turning On(1) or Off(0). This had no prior application, as logic was exclusively used by those who didn't need math to employ it. Simplifying the indefinable “Intelligence” into a mutually agreeable term like “logic” made employing it in machines—limited to exclusively using math—viable, at least artificially.

Parts & Paradigms

Claude Shannon formalized 1(true) or 0(false) to represent the state of a given switch in a circuit. These binary digits (“bits” for short) could be rearranged into coded patterns to represent any scripted information. Now that both man and machine had a respective native language, we could begin introducing ourselves. Coders came up with ways to translate between these two languages to make instructing machine during its infancy easier for both of us. Processing 64 switches at a time or 8 rows of 8 bits, became the standard. Any amount could have worked, but 8 x 8 could be argued as an amount not too small yet sufficient. Whatever the motivation was, a row of 8 bits either on or off was established to make up a “byte”. Whether it's a text from a friend, a precious home video, a GPS signaling where to go, or a street light signaling when to stop…Daily life is now made up of these bytes. A million bytes is a megabyte, a thousand megabytes is a gigabyte…Claude contemplated how many would be needed for every possible chess game (known as the “Shannon Number”), which ended up exceeding the amount of atoms in the observable universe. Computers began with large physical switches, later swapped for smaller vacuum tubes, then even smaller microscopic transistors, and even smaller nanoscopic semiconductors…and will keep getting smaller because Claude devised what can be applied to a “switch” of any scale: a philosophy. Unless we can make a “switch” smaller than an atom, then all the space in the universe taken up by matter (and more) would need to be filled with switches to compute every possible chess game.

According to a colleague, “Claude played so much chess” while working for Bell Labs(named after the family of linguists) that supervisors expressed concern. His resulting Programming a Computer for Playing Chess published in 1950 likely made not playing chess more of a company concern. In it, he acknowledges that a chess-playing computer might seem “perhaps of no practical importance”, but it begs a theoretical question that a solution for “acts as a wedge in attacking other problems of a similar nature and of greater significance”. Like Ada Lovelace, Claude also imagined a machine of greater significance being one "capable of orchestrating a melody." Claude credits the designs of El Ajedrecista as a source for this paper: Chess “involves general principles, something of the nature of judgment, rather than a strict, unalterable computing process. Not being merely right or wrong, but having a continuous range of quality from the best down to the worst…perhaps a computer that designs good filters even though they were not always the best possible [would be satisfactory]”. Claude confronts the counter-intuitive nature of intelligence, adding, “there isn't a practical method to fully determine if a given chess position results in a win, draw, or loss, otherwise chess would be determined from the first move and lose most of its interest as a game. Our understanding of best and worst is from previous games won or lost”. Even if there is a method of perfection, it would be impossible for a man or a machine to compute, especially as new contributions redefine the criteria. Today, this is the foundation of contemporary "Ai”, which believes in dimmer switches; Neurons that fire exclusively at certain thresholds aren't only conditional, but biased too.

Bell Labs employees went on to create the first chess computer able to play at a “Master” level, just a few tiers below the most revered “Grandmaster” level of play. They called their artificial chess player "Belle". This project was led by Bell Labs employee Ken Thompson, whose own fascination with chess was amplified in middle school when he saw a teenager the same age as him on the cover of Look magazine while he was still finding what he wanted to do. The player on the cover was a 14-year-old player named Bobby Fischer. Thompson decided to buy some books on chess theory and continue pursuing what eventually got him hired. Belle was unveiled in competition in 1972 but was largely overshadowed that same month with Bobby Fischer on the cover of Look Magazine, again(See Flanks in Opposition). Although Alan Turing, whose algorithmic work helped end World War II, hand-wrote the first chess algorithm a few years prior, it wasn't until Claude published the subject that it became formative to what is now regarded as the most consequential practical laboratory in history.

/Home/User/Files/Ranks

Of all the renowned aspects of its design, it's usually the symbolic hierarchy of chess that permeates far-reaching zeitgeists. In the 1470s, after the first attempt at mass-produced information, the very next book printed in English was about the game's characters. Whether you see yourself as a pawn or a king, or not caring as every piece is put in the same box in the end, the symbolism carved into each piece is one of the more easily appreciated aspects. Information on computers was similar to a pile of identical checkers pieces before Ken Thompson and Bell employees introduced hierarchical operating systems. Many of Claude's protégés shared this mutual affection: Operating interfaces using less code and more hierarchical objects. In a 1963 doctoral thesis supervised by Claude Shannon, Ivan Sutherland envisioned a new medium: Man interacts with objects without needing to know at the outset what their problem is or how to solve it, while Machine facilitates a proactive cooperation of manipulable ideas. Before this, using a computer meant meticulously organizing your problem into worked-out steps, with Machine calculating what is already understood by Man. While others in the 1960s were hoping a computer could be smaller than a truck, Claude's protégé and his student Alan Kay were conceptualizing the unfathomable: Hand-held tablets for children, Head-mounted “virtual reality”, volumetric displays, and intuitive interfaces. Users arranging objects that interact characteristically was the key. While Alan Kay was designing prototypes with these principles, a young visitor in their 20s, named Steve Jobs, had an impressionable tour. A few months later, this visitor shifted their focus towards developing what became the Mac. Alan Kay was the first to detail the concept of a laptop-like computer, suggesting the ideal computer is one in the hands of a child:

“A child is a "verb" rather than a "noun".[...] An environment which allows many perspectives to be taken is very much in tune with the differentiating, abstracting and integrative activities of the child.[...] We do not feel that a [carry-anywhere device] is a necessary constituent for this process any more than is the book. It may, however, provide us with a better "book", one which is active (like the child)rather than passive.[...] Just as with the book, it brings a new set of horizons and a new set of problems. The book did, however, allow centuries of human knowledge to be encapsulated and transmitted to everybody; perhaps an active medium can also convey some of the excitement of thought and Creation."

He postulated the socio-economic factors as well: “Just as easy xerography has enhanced publishing (rather than hurting it as some predicted), and as tapes have not damaged the LP record business but have provided a way to organize one's own music. Most people are not interested in acting as a source or bootlegger; rather, they like to permute and play with what they own.”

Kings, Castles, and Canons

Considering the arbitrary rules of most games, few feel as inevitable as the design of chess. A symmetrical plane of light and dark. Checkered squares delineate 8 x 8 subdivisions. Arrangeable objects interact characteristically atop the checkered squares. The board could be subdivided into any amount, but a row of 8 light or dark pieces was not too few, yet sufficient. Whatever the reason, 64 was established as the number of squares a player considers per operation. The pawn ♙ moves forward one square at a time, the King ♔ does the same except in any direction, while the Queen ♕ does that as far as it wants. The bishop ♗ travels diagonally, Rook ♖ orthogonally, and the seemingly arbitrary Knight ♘ , when explained as an “L-movement,” feels less so when considering it hops/skips over a square, with the choice of landing on either square directly perpendicular to that hop. Moving one of these characters can incite at least 1,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000 different stories (at least according to Claude Shannon). A few other rules have been introduced since 600 AD, but this constitutes most of the game. We can revisit these past games for the same reason we can still listen to what pianists played hundreds of years ago: a universal notation. A record of the exact moves of a given chess game from start to finish. Chess notation uses algebraic symbols, in turn, entitled “Algebraic Notation”. On a chessboard, “e4” signifies the square where file “e” and rank “4” intersect. A player tabulates this data with a chess piece. The first few moves of Napoleon vs the Turk is compressed as: 1. e4 e5 2. Qf3 Nc6 3. Bc4 Nf6 4. Ne2 Bc5 5. a3 d6…When players revise and add to this with alternative sequences, it becomes a refined code, one for achieving checkmate. In chess books predating computer science books by hundreds of years, you'll find this algebraic code with directions explained in a “if this, then do this” format. This is the essence of an algorithm.

“On the fourteenth move white moves their queen horizontally 5 squares putting the opposing king into check” is instead written as 14.Qe8+. This allowed entire games to be transcribed onto a paper card in real time. Patterns and sequences from the best players from different local cafes, cultures, or periods could now be recognized, refuted, and analyzed as a whole. Universal data for reinforced learning. This was the key to unlocking the highest chess level humanly possible…and then higher. As the Machine Age materialized, many of the first machines considered the first “computers” were the ones that relied on tabular data on paper cards to compute data. Leonardo Torres Quevedo's El Ajedrecista machine would have likely used them, except there was no need, as it used a graphical user interface of objects for inputting and arranging data.

A man working in the US Census Bureau saw the potential in processing and handling data this way. In 1911, he established a company for machines that used these paper cards, called “Computing-Tabulating-Recording Company”, subsequently renamed to International Business Machines (IBM). While Bell Labs was leading pioneering research, it was IBM that began largely turning it into products. While these titans were nurturing Ai in its altricial infancy, dramatic minds were imagining Her potential. The illusionists, who always held the mirror up to ourselves, were in the golden age of a relatively new medium. One that enabled them to hold the mirror at a fantastical angle, letting us collectively see an imagined reflection. A young player from New York saved up enough money in chess winnings to kickstart their mastery of this medium. This player, named Stanley Kubrick, angled the mirror to an imagined space odyssey in the year 2001. Peculiarly, the odyssey starts long before, in a period rarely shown in cinema—the dawn of intelligence. The 1968 film 2001: A Space Odyssey begins with primitive humans wandering a desolate earthly landscape before willing their ideas into existence, or apparently the other way around. Then, a miraculous occurrence foreign to the organic formations initiates a new landscape: a tool. Kubrick cuts the 65mm film stock here and splices it seamlessly to 2001, an effect to a cause a few epochs before. A novel form of intelligence wielding a bone tool launches it into outer space, transporting us to a metal container wielded by an Ai super-computer named HAL. Whether HAL is juxtaposed as the novel tool or the novel form of intelligence depends on whether there was a distinction to begin with. Aboard the spaceship in what may have felt like a throwaway scene in 1968 for those expecting a ticket's worth of space wars, ended up as the most prescient.

The scene captures the reality of co-existing and co-piloting in the deep expanse of nothingness. Kubrick keeps the camera in the same spot without a single cut: Intelligent Dr. Frank Poole and artificially intelligent HAL take turns announcing chess moves on a shared graphical user interface. Depictions of sentient computers in science fiction usually relied on vicious humanoids sounding the alarm with robotic brute strength, but HAL lurked like carbon monoxide. When HAL breaks his silence, he speaks with calm indifference, an intonation much more honest than Siri or Alexa's artificial enthusiasm. There is no suspense during the chess game; HAL effortlessly defeats Dr. Poole. This was a bold sentiment, but one that foresaw a very real chess match involving IBM soon after. Kubrick dismissed being referential to IBM, despite fortuitous allusions such as each letter in H.A.L. preceding I, B, and M in the alphabet. A private message written during production suggests Kubrick's vigilance: "Does IBM know that one of the main themes of the story is a psychotic computer? I don't want to get anyone in trouble, and I don't want them to feel they have been swindled. Please give the exact status of things with I.B.M. Best Regards, Stanley." IBM is credited as an advisor on the film, although only accepted under the condition of having no association with the equipment failure of HAL. This refers to the scenes following HAL playing chess with faultless functionality. Perhaps HAL's "equipment failures" weren't what IBM wanted to disassociate from, but instead a capable machine assessing terminating us as indifferently as it does checkmating us.

Flanks in Opposition

Around the same time, efforts to establish general criteria to evaluate intelligence, of any kind, were becoming prevalent, especially between the US and the Soviet Union. Any and all demonstrations of intelligence became a foot race between the two nations. There proved to be no clearer display than a face-off where a king has to fall, and a handshake of defeat becomes a better photo op than a flag on a moon. The World Chess Championship Match between Bobby Fischer and Boris Spassky was headlined as “US vs USSR”. A chess game between apocalyptic arsenals may seem redundant, until considering there wasn't anything at the finish line in space either. Much like the initial lead in the space race, the USSR at this point was untouchable in chess. The Soviets were unbeaten for an uninterrupted 24 years, the same number of games(coincidentally) that Fischer would need to win the majority of in the match.

For many people, this was a first impression of the kinds of players who played this game. Early in the match, Fischer's infamous bouts of paranoia were immediately a factor, showing up late and resigning games before they started. Before the third game, now up 2-0, Spassky sympathetically obliged to meet the unusual demands made by Fischer about the lighting, cameras, chairs, and everything in between. During this chaos, everyone from President Nixon's closest advisors to Soviet facilitators were trying to keep the match on the rails. Fischer was finding solace in his bible, a worn-in red book cataloging every recorded chess game played by Spassky. The third game of the match was the first glimpse of Fischer's combustibility as horsepower. Looking at photographs of Fischer carrying his red book around, or any photograph of him for that matter, there's a pattern. Not just under the pieces he's usually looming over, but in the stillness that is captured. Almost like a nuclear reactor. Not so much the fission, but the walls burdened with containing it. Bobby Fischer beats Boris Spassky for his first time in game 3.

Spouses have yet to carry a book around of each other's every move, but competitors at the highest level remain exceptionally intimate collaborators. For Spassky, co-creating excellence on the board meant collaborating with Fischer's madness off of it. After his first loss, Spassky was having his own chair X-rayed and chess pieces inspected by his team. The 4th game was a draw, and now the world was tuning in to see if the 29-year-old phenom from the Bronx could surmount the Soviets in the 5th game of the tied match. On the 27th move of game 5, Fischer sacrifices a bishop to gain a positional lead. Spassky considered the caustic implications of this move before resigning, making it the shortest game of the entire match. Fischer won 4 more decisive games, maintaining a comfortable lead until the end. The match, politicized as another proxy battlefront between the world's leading hegemonies, catapulted the young Bobby Fischer into an icon as the first and only US-born chess champion. A reclusive game from the 6th century, effectively used as a drosophila for information science, was becoming equally as affective for mass media, just in time for the next chapters.

Technological breakthrough divides the constant of time. There was daily life before agriculture, and then after. Our chapter using stone tools, ended with metal tools. As technology accelerated, so did the potential for fewer people to understand it. Inventors initially grasp it, but those who tame it into the right presentation, become the authors of the ensuing chapters: lightbulbs(invented: 1870s), motion picture cameras (invented: 1880s), touchscreens (invented: 1960s), or the internet (invented: 1980s)...remained footnotes until the right demonstration. In the 1990s, IBM wanted to author the next chapter. However, demonstrating Artificial Intelligence wasn’t like Thomas Edison demonstrating candle-like light, produced artificially. Intelligence isn't as obvious, regardless if it's artificial or not. IBM began organizing an undeniable watershed moment: An IBM machine autonomously beating the best human chess player, Garry Kasparov. This rivalry began in 1989, when a computer named Deep Thought beat a human chess grandmaster for the first time, earning itself a chance to play against Kasparov.

Gary Kasparov had already beaten 32 of the best chess computers, simultaneously, but Deep Thought offered a new threshold. Deep Thought lost both times against Kasparov in 1989, but IBM saw the potential to invest in the prospective machine. After years of upgrades and extensive refinements, a match was set up in 1996 between Kasparov(the reigning undefeated chess champion for over a decade) and IBM's re-branded machine, “Deep Blue”. The machine shockingly beat Kasparov in their first game. Kasparov corrected his complacency and won the rest of the match without losing another game. Deep Blue lost, but that first game was enough for IBM to initiate a blockbuster publicity campaign around a rematch. For over a year, the Deep Blue team at IBM “hired additional chess grandmasters to improve their endgame databases, evaluation accuracy, and methods to disguise the computer's strategies”. They announced the prize fund for the winner contained over a million dollars, which largely favored Kasparov in pre-match predictions. Kasparov’s in-match fortitude was known to be second to none; Kasparov had yet to lose a chess match in his professional career. In 1997, a small eight-by-eight checkered dance floor for intelligence gained the gravity of a colosseum: Man vs Machine, the seminal demonstration of artificial intelligence vs organic intelligence. With increased tensions, Ken Thompson was brought in to monitor the match as an unbiased third party. This was needed to appease the usual suspicions of a computer cheating by using a man. After the first game, Kasparov happily joined the hundreds of reporters and television crews waiting outside for results. During a tense second game, Deep Blue and Kasparov were taking longer to make their moves. Towards the end, Deep Blue took over 15 minutes to make the infamous 36th move. A move that made the best human player resign a few moves later and leave without addressing the press.

With mounting pressure, Kasparov opened the next games with unusual moves, intentionally playing in a way the machine wouldn't have sufficient data on. This led to hard-earned draws, keeping the match tied heading into the last game. Behind the scenes, there were talks of Deep Blue needing reboots. Kasparov felt that a player short-circuiting shouldn’t warrant in-match corrections, and began questioning the fairness of the ordeal. Going into the last game, Kasparov was overwhelmed, but unlike the usual intimacy of competitors at the highest level, this wasn’t a collaboration. There was no faithful book of each other's moves, at least not for Kasaparov. He was guessing what style of play the machine might conjure next, while Deep Blue was analyzing 200 million moves a second. In the last game, Deep Blue tactically sacrificed a piece early on—an unexpected style of playing for computers at the time. Kasaprov couldn't find a direction for the rest of the game, losing the shortest, most decisive game of the match. Twenty years after the world was introduced to the fictitious HAL, IBM's Artificial Intelligence was on a press tour after finally beating Man at his own game. But amongst the scientific community, IBM was dissociating its accomplishment from “Artificial Intelligence”, which had become a vague, almost disreputable field. DeepBlue possessed no autonomous “learning” apart from simply outputting what intelligent humans exhaustively input into it. In Academia, Deep Blue's greatest achievement was perhaps an etymological one; Deep Blue raised the defining bar of “Ai” until it disqualified itself underneath. Thereafter, a machine merely appearing intelligent within the confines of a narrow task wasn't “artificially intelligent”. At least not to the captain of the Cambridge University chess team, who was watching the televised match from England.

Alpha Beta Cuda Data

This chess player would have graduated from Cambridge the year prior, in 1996, but admissions suggested a gap year because he was too young when he was initially accepted. Demis Hassabis started playing chess when he was 4 years old after seeing his dad and uncle play. Within a year he was beating them and most others across the country. By the time he was 8, he had enough in competition earnings to buy himself his first computer (a ZX Spectrum designed by Clive Sinclair), which kindled the programming of custom games. At 13, Demis was ranked the 2nd highest chess player in the world under 14 years old, only behind Judit Polgár: the daughter of a psychologist attempting to prove his theories on nurtured intelligence (all of his offspring became chess grandmasters, with Judit becoming the best female chess player of all time). Demis's aspiration towards professional chess shifted while looking up from the board during a particularly grueling tournament. He recalls looking up at the brilliant minds around him toiling over marginal wins, all looking down instead of at each other. After winning a programming competition to work for an esteemed video game company, he designed and programmed games until he was of age for Cambridge.

During his gap year, he most notably developed a game called “Theme Park”. The game consisted of non-playable characters dynamically reacting to any uniquely configured theme park. This was extraordinary for the time, lauded by players and critics as an early form of Ai. Theme Park sold over 15 million copies, becoming one of the top-selling games of the era. Demis explains the relative uniqueness of the art form, “You as the player aren't passively consuming the entertainment, you're actually actively involved as an agent”. To compute the countless amount of possibilities granted to an active player, the burgeoning gaming industry at this time laid the foundation Ai stands on today. Natural language processing chatbots, like ChatGPT, can parallel process the entire context of a query or prompt, because of the parallel processing units made for gameplay in the '90s. While Demis was studying computer science in college, Deep Blue was studying human chess players, both of them graduating in 1997. Demis reflects, “I was more impressed with Kasparov's mind, that he could play chess to this level where he could compete on an equal footing with the brute of a machine. But of course, Kasparov can do everything else humans can do too. It was a huge achievement, but the truth of the matter was Deep Blue could only play chess. What we would regard as Intelligence was missing from that system. This idea of generality and also learning.”

The following four years after graduating, Demis competed in the annual Mind Sports Olympiad, winning each time, setting the record. While finishing his PhD in Neuroscience in 2009, Demis was fascinated with the idea of a generalized artificial intelligence that could (learn to) play a similar range of games as himself. The next year, Demis, along with two friends, co-founded a company called Deepmind to solve this kind of intelligence. Deepmind's first goal was to create an Ai model that could learn to win classic Atari games without any human-inputted strategy. Without a commercial product or any revenue, Deepmind garnered early investments from a few prominent investors…and then Google, which decided to make it its largest European acquisition, for about $500,000,000. DeepMind decided their first assignment was to beat the best human Go player, a feat thought to be impossible at the time. The board game Go has been played in Eastern cultures for over 3,000 years, yet the best players still can't quite describe the gut feeling behind their best moves. Unlike beating the best chess player, beating the best Go player couldn't rely on as much human data, despite a game of Go having more possibilities than chess. DeepMind called this Ai Go player "AlphaGo", which learned to play Go similarly to how organically intelligent players must. By playing. AlphaGo taught itself how to play through trial and error, reinforced with rewards and penalties. The team explains that available data “may impose a ceiling on the performance of systems trained. In contrast, reinforcement learning systems are trained from their own experience, in principle allowing them to exceed human capabilities, and to operate in domains where human expertise is lacking.”

After beating the best human Go player in a match, DeepMind expanded upon these principles with AlphaZero, their Ai model that taught itself how to play chess starting from zero human input. After playing against itself for just a few hours, AlphaZero was able to beat Stockfish, the best chess computer ever(exponentially stronger than Deep Blue). After beating Stockfish 28 times without a loss, Demis Hassabis had made the greatest chess-playing entity in existence. AlphaZero approached searching through Shannon’s Number of possibilities differently. The best chess-playing machines, like Stockfish, essentially compete for how to not waste energy, rather than considering every possibilty. Every chess engine from Belle to DeepBlue to Stockfish had to figure out how to maximize the minimum gain by pruning or ignoring, like a gardener does with fruits on a tree. A machine storing every possibility was never the goal, it's teaching them how to be picky. AlphaZero was given only the rules of chess, and then established its own way of picking. In doing so, Kasparov and current best chess player player Magnus Carlson said it was like watching an alien reinvent a game previously thought to be solved. In a particular game against Stockfish, AlphaGo ignored what the best man or engine would have done and instead sacrificed pieces and positions to gradually corner its opponent into a rarified “Zugzwang”: a rare situation in chess that forces an opponent to destroy their own position. This is seldomly done to top players with many pieces(options) still on the board, let alone to the actual chess engine used to correct the best players’ games. AlphaZero was discontinued after its demonstration, but its principles have been subsumed by the best chess engines today. After playing increasingly difficult recreational games and establishing themselves by defeating humans, DeepMind decided to take on a game that achieved the opposite…perhaps the only playable game with an altruistic win.

Man vs* Machine

The game Foldit (2008) caught Demis Hassabis' attention during his postdoctoral years. The game's sole objective indirectly began in the 1950s, when biochemists successfully depicted the molecular animators of organic matter—proteins. Life is animate because of the ways amino acids “fold” into a 3-dimensional protein. The folds give them their unique function, such as Hemoglobin, the protein in our blood folded in a way that oxygen binds to. Scientists reliably captured these patterns by crystallizing a protein and then projecting a beam of electromagnetic waves through it. Film stock was used to capture how the waves diffracted throughout the crystal. The resulting pattern on film could then be meticulously deciphered as the structure of the protein. Much like a photographer using a lens to capture visible electromagnetic waves, a crystallographer uses invisible(smaller) electromagnetic waves to capture the lens itself (the crystalized protein). When scientists deciphered the crystalline structure of Insulin—a protein that triggers our cells to use sugar for energy—a synthetic version was able to be mass-produced, saving the lives of millions of diabetic people. By 1971, the structures of 7 unique proteins had been successfully solved and logged in the established Protein Data Bank (PDB). With each one, many of life's molecular calamities began to be understood, leading to treatments for otherwise fatal diseases such as AIDS. By the 1990s, digitization and computers were finally catching up, so a critical assessment of structure prediction (CASP) was established to test the accuracy of state-of-the-art methods, relative to the precise yet laborious method of analog crystallography. An algorithm called Rosetta was among the best computational methods tested. Created by biochemist David Baker in 2005, Rosetta distributed the processing power of idle computers from volunteers. While using volunteers' computers, the Rossetta@home program showed its protein iterations as a screensaver, which intrigued those who watched it long enough to want to play it themself.

The ensuing game, Foldit, allowed volunteers to attempt folding proteins within Rosetta's scoring system. By the time Demis Hassabis approached this game with his team at DeepMind, the Protein Data Base had approximately 136,000 proteins modeled. In 2017, theoretical chemist John Jumper joined Demis Hassabis to help the team at DeepMind understand the game they were playing. They called their protein-solving Ai model, AlphaFold. In its first year attempting the CASP, AlphaFold scored first place at around 68% accuracy, 10% higher than the previous best. Two years later, at the next CASP, Alphafold2 shattered the record it previously set, with 92% accuracy. This served to validate what AlphaFold2 was about to do next. Doubling or tripling the 136,000 solved proteins would have been groundbreaking, but Alphafold2 went on to solve over 200 million…and then released the models for free as an open-source database. Demis Hassabis and John Jumper shared the 2024 Nobel Prize in Chemistry with David Baker. Millions of researchers across over 190 countries have already begun using it for critical work in understanding neglected diseases, cancer research, solutions for agriculture and plastic pollution, and more. Many of the next innovative biological breakthroughs will be a direct result of this accomplishment. The same year they won the Nobel Prize, “Artificial Intelligence” was going public.