Artificial Intelligence

An Introduction for the Inquisitive Reader

Robert H. Chen, Chelsea C. Chen

• Publisher: Chapman and Hall/CRC
• Year: 2022
• ISBN:
  9781000577396
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Artificial Intelligence: An Introduction for the Inquisitive Reader guides readers through the history and development of AI, from its early mathematical beginnings through to the exciting possibilities of its potential future applications. To make this journey as accessible as possible, the authors build their narrative around accounts of some of the more popular and well-known demonstrations of artificial intelligence including Deep Blue, AlphaGo and even Texas Hold'em, followed by their historical background, so that AI can be seen as a natural development of mathematics and computer science. As the book moves forward, more technical descriptions are presented at a pace that should be suitable for all levels of readers, gradually building a broad and reasonably deep understanding and appreciation for the basic mathematics, physics, and computer science that is rapidly developing artificial intelligence as it is today.

Features:

• Only mathematical prerequisite is an elementary knowledge of calculus
• Accessible to anyone with an interest in AI and its mathematics and computer science
• Suitable as a supplementary reading for a course in AI or the History of Mathematics and Computer Science in regard to artificial intelligence.

I The Arrival of AI in the Human World

CHAPTER 1 Game-Playing Machines

DOI: 10.1201/9781003214892-2

He was a man with two common first names, Arthur Samuel, born and raised in mid-America Emporia Kansas in a middle-class family, a graduate of the local College of Emporia, he appeared to be an ordinary young man brought up in a traditional American society. However, the genial but inherently cautious Arthur Samuel was soon to known as a man of great distinction in an unusual new realm. For from Emporia, his special talents allowed him to enter the citadel of engineering education MIT which provided him a sound electrical engineering basis, and thence to the fount of technical innovation AT&T Bell Labs where he worked on the telecommunications systems that transformed the whole world, and the radar technology critical to victory in World War II.

His contributions were significant, but he was not done, for after the War, closely tracking the locus of new technology, Samuel arrived at the University of Illinois to work on the giant ILLIAC scientific computer, and after being recruited by IBM, he was immersed in the design the world's first commercial mainframe computers that were soon to disrupt industry and education all over the world. But it was then that he came up with an audacious idea that would be the precursor of a transformative technology that would further disrupt industry and the lives of almost all the peoples of the world. His idea was inspired by a return to his everyman roots as the conjurer of a “thinking machine” playing a trivial game known and played by almost everyone in America.

The IBM 701

Working in Poughkeepsie in 1949, Arthur Samuel noted that the storage and display matrices of the new IBM 701 computers, looked just like a checkerboard, and like almost everybody in America at the time, he played that common game of jumping about a board to capture an opponents’ pieces and “kinging” your own pieces.

In his uncommon mind however, Samuel saw a clever way to create an image for IBM as the creator of a wondrous checkers-playing computer that could challenge and defeat humans at their own game, garnering publicity for the marvelous capabilities of IBM computers.

It was clearly a creative marketing scheme, but his company was not in the least supportive, for its venerable chairman Thomas Watson, Sr., like Samuel was conscious of IBM's image, but in the opposite sense of avoiding the spectre of a menacing “IBM thinking machine” going around beating up on humans all over the country.

The idea of the homey IBM Selectric typewriters and office machines souped-up like Frankenstein's monster to triumph over small-town residents at checkers was anathema to the friendly helpmate that was Watson Sr.'s marketing vision for IBM products.

Like Victor Frankenstein the earnest scientist, the dogged Samuel followed his checkered muse, jumping over the IBM powers by developing the 701 checkers machine on his own time. He encoded the rules of the game and programmed a decision tree move generator, evaluating moves by attaching numerical values to the move options that would most likely lead to a desired advantage. Included in the evaluations were nuggets of checkers wisdom familiar to any checkers player, for instance depleting the opponent's checkers by even challenges when ahead, simple gambits, and guile that today are called heuristics.

But this rote learning from rules, simple decision evaluators, and well-known heuristics would take the 701 checkers machine only to the level of its creator, which in Samuel's case, despite his technical brilliance, was below average, and so he consulted expert players for more advanced winning strategies and tactics.

Samuel found it difficult however to incorporate their checkers skill, being mostly based on knowledge of the proclivities and idiosyncrasies of opponents, and no little hard-to-codify “feel” and outright guessing.

Since this “top-down” encoding of rules, tree search results, heuristics, and learning from some expert players, although now “expert”, the 701 could only as well as those players, it was clear to Samuel that the 701 expert system needed to learn from playing against other expert players, and like a human player, 701 could accumulate playing skills “bottom-up” and from its own actual game experiences, gain knowledge on how to win by reinforcing its good move choices and degrading the bad moves.

Samuel now evaluated moves based upon their ultimate success or failure in training sessions based on the recorded matches of expert players, and the reward and punishment of good and bad moves in actual games against human players, and even in games played against itself. These were three learning schemes that would later be called respectively supervised learning on training sets, reinforcement learning in game situations, and unsupervised learning by playing progressively improving versions of itself, the basic methods of today's “machine learning”, a term Samuel himself coined.

By means of machine learning, the IBM 701 checkers machine slowly improved, and in 1962 after 13 years of part-time development, Samuel's machine challenged and easily defeated the Connecticut state champion Robert W. Nealey.

After the match, the former champion said he had not had such competition from anyone since 1954, when he lost his last game, but in the rueful pride characteristic of accomplished human beings, he also circumspectly exhibited a clear approbation of the IBM 701's “intelligence”.

Despite its successes, the checkers-playing IBM 701 had no such baneful pride, much less circumspection, but if pride instigates the will to succeed, thereby producing greater effort, a machine lacking such pride might make it inferior to humans in determination, but it has no equal in effort as it needs only electricity to practice tirelessly 24/7 against not only humans but other machines and itself.

And pride although motivating, if once broken can devolve to paranoia, as the great chess champion Garry Kasparov would later reveal in his acrimonious duel with IBM's Deep Blue.

Samuel's cool and collected IBM 701 in the ensuing years would have every reason to be proud, for it remained undefeated for 15 years until 1977 when it finally lost, not to a human, but to a rival checkers program developed at Duke University.

How prideful humans internalize defeat has been analyzed, but how a machine internalizes a defeat may never be known, for deep within the hidden layers of the artificial neural networks of today's AI machines, the germination and processing of a “thought” is largely unfathomable even to the learning algorithm's creator, and that unknown, contrary to the coldly logical computer ethos that usually makes it superior to an emotional human being, could conceivably leave room for machine attitude, willfulness, and even emotion.

The blessings of computers and artificial intelligence have been the machine's potential to increase production while freeing people from the drudgery of everyday work, allowing them to think about the work rather than just enduring the tedium of doing it, thus improving efficiency and leaving more time to pursue lofty goals and enjoy life. The bane of the machine is its potential to take over almost all human occupations, relegating humans’ activities to the care and feeding of the machines.

Game-Playing Machines

Possibly at odds with humanity's long term self-interest, organizations of humans for intellectual or commercial gain have initiated Man vs. Machine fair matches of supreme mental combat in the arenas of two of the primary indicia of human intelligence, IBM's Grand Challenges in Western chess and Google's DeepMind foray into the ancient Eastern game of Go.

Intelligence can be manifested in contests of strategic and tactical thinking within a game's metes and bounds, with superiority demonstrated by the rationality and creativity of moves that produce successful outcomes.

IBM Deep Blue

From Watson Senior to Watson Junior, IBM's attitude towards thinking machines reversed; for under Thomas Watson, Jr., the natural next step for IBM's electronic computers was to extend the machines’ ken from simple checkers to sophisticated chess; engendering fear among the populace was not a concern to him, seeking admiration and subsequent income for IBM was the goal.

In 1996, the chess computer Deep Thought designed by Carnegie Mellon University graduate student F.H. Hsu, and further developed by his team at IBM, grand challenged Garry Kasparov, generally acknowledged as the greatest player in the history of the game, to a six-game championship challenge match.¹

¹ Kasparov was the youngest world champion at 22, and the reigning champion for an unprecedented 225/228 months from 1986 to 2005. In 1999, Kasparov's FIDE Elo ranking of 2851 was the highest in history until Magnus Carlsen, a Grandmaster at 13 scored 2882 in 2014, the highest ranking to date.

Kasparov won the first match against Deep Thought 4-2, but the next year in New York City in May 1997, arrayed against Kasparov was the upgraded IBM massively-parallel RS/6000 SP Super Workstation Deep Blue chess-playing machine, replete with newly-developed accelerator chip sets.²

² Thin 30 node, 120 MHz P2SC microprocessors at each node and 480 specifically-designed VLSI chess accelerator chip cards running C language software under the AIX operating system on a 32-bit microchannel bus.

Deep Blue's specifically designed high-performance hardware and software could minimax tree-search 50 billion possible positions at a rate of 200 million moves per second. After alpha-beta pruning of the search tree, a tree-depth of six to eight moves was searched to select optimum moves.³

³ See Chapter 8 for a description of the basic technology and key points in the match. Minimax and alpha-beta pruning are discussed in that and later Chapters.

Kasparov (playing white) won the first game with an “anti-computer” strategy where deliberately suboptimal moves are made to confuse the rationally-wired computer; this seemed to work in the first game which he won with white advantage, but his confidence was shaken upon a devastating second game loss, after which he accused the Deep Blue team of illegal in-game human intervention. IBM denied this, saying that adjustments by humans were made only between the games, in accord with the rules. Deep Blue won the Match 3½-2½, the first time in history that a machine had defeated a Grandmaster in a Championship competition.

Afterwards, Deep Blue's game logs did reveal a random error in Game 1, and commentators speculated that Kasparov interpreted the subsequent fixes instead as Game 2 in-game changes by the Deep Blue team; in other words, Kasparov would not accept that he could be beaten by a machine.

The closeness of the match was not definitive of the superiority of machine over man, but Kasparov's paranoia throughout, and his abysmal resignation in Game 6 could at least establish that Deep Blue's cold logic could triumph over the warm frailty of human emotion and the pride of extremely self-aware human beings.

After its stunning victory, could Deep Blue the machine likewise be self-aware of its superiority? It will never be known because the fate of most innovative research devices is dissection; the RS/6000 SP was sent back to IBM's test floor and the shell returned, but two cards went to IBM headquarters in Armonk for visitor demonstrations, and the rest were inserted into the older version RS/6000 SP and dispersed to various workstations and parts shelves.

In the press conference after the match, Kasparov was cheered and heartily encouraged by an audience including many chess masters, expert commentators, the press, and the general public, but when IBM's Deep Blue team assembled on the stage, their notable technical achievement notwithstanding, they were met with thinly-veiled disdainful murmuring.

There is no sin in standing up for humankind against a machine, but Deep Blue's victory evoked unease, fear, even hostility, and the audience apparently sensed menace rather than hope. Perhaps Watson Senior was right after all.

After this challenge, the score was Machine 2, Humans 0, and after Nealey's prideful but acknowledged defeat came Kasparov's arrogance-fed emotional collapse, presaging psychological frailties that may or may not ever manifest themselves in a machine.

Google AlphaGo

The West's explicit “kill the King” chess...