The Ultimate AI Primer Came From … Reader’s Digest!

 

As is typical with science (particularly Management Science) trends, Artificial Intelligence, or AI, has received a lot of attention and material, and a significant portion of it is bogus. Some of the material I’ve seen is straight up laughable, particularly the idea that AI will end up controlling humans like some silicon-based, unavoidable tyrant. In my next blog I might explore how a PM-specific AI-based tyranny might manifest (it may not be that different from the current guidance-generating industry), but for now I want to focus on what AI is at its fundamental level, and why I’m not in a hurry to purchase AI-generated-dystopia insurance.

The Reader’s Digest Treasury for Young Readers (Reader’s Digest Association, 1963) is truly a treasure. Published sixty years ago, it’s full of really cool pieces – it was in this book that I first read a Sherlock Holmes story (The Adventure of the Speckled Band) – including brain teasers, puzzles, games, and projects, one of which deals directly with Artificial Intelligence. It’s there on page 176, in an article entitled “How to Play ‘Hexapawn[i],’” with instructions on how to build HER, the Hexapawn Educational Robot. And make no mistake – even in 1963, with personal computers not even being conceived as a practical possibility, HER represented true Artificial Intelligence. Here’s how it works.

Hexapawn is a simplified derivative of chess, played on a three square by three square board, populated by three white pawns and three black ones. Only two types of moves are allowed: the pawns may either move one square straight ahead to an unoccupied square, or it may capture diagonally. There are three ways to win: (1) by advancing a pawn to the third row, (2) by capturing all of the opponent’s pawns, or (3) placing your opponent in a position where he cannot move. To construct HER, you will need twenty-four matchboxes and some colored beads. On page 177 there’s an illustration of each of the possible 24 scenarios, with black dots representing the black pawns, and circles representing the white ones, on the nine-square board. Possible moves in each scenario are shown by colored arrows, and the HER always moves second.

To construct HER, copy the scenarios from Page 177, colored arrows and all, and paste each of them on top of one of the 24 matchboxes. Also place a black, blue, red and (for just a couple of the scenarios) green bead inside. Then make a move, and find the diagram of the position from the top of the matchboxes. Without looking, pull a colored bead from the box, and make the indicated move. Continue until either you or the HER has won. If you win, go to the last move/scenario that HER made, and remove that colored bead from the corresponding matchbox, eliminating that move from the available pool. In this way the HER “learns.” In the version described in the Treasury, the robot became a perfect player after losing eleven games. And this, GTIM Nation, perfectly and simply illustrates what AI is all about.

Consider: a machine can no more “learn” than 24 bead-containing matchboxes, at least not in the conventional sense. Ultimately, machines can only execute prior instructions, try random actions from a previously-defined set and eliminate the choices that led directly to undesirable outcomes, or perform some combination of the two. In a YouTube video entitled ”Open AI Broke Hide and Seek[ii],” the narrator describes how a simple digital version of the children’s game Hide and Seek was set up, with two bots being the “hiders,” and two being the “seekers.” The environment was a square room, with a smaller room in one corner, and two openings. Prior to any relevant bot behavior being observed, there were literally millions of games, with the failure of the bots to do anything “intelligent” being attributed to their “random” behavior. But that’s the whole point. Absent anything resembling real intelligence, the only way these bots could “learn” would be by playing the game and initiating some random move, arriving at an undesirable outcome, and then removing the losing choice from the repertoire, like HER did. That’s why it took millions of iterations for the Open AI application to happen across a workable strategy that any five-year-old could have ascertained within the first few instances of the game.

Don’t misunderstand – much insight can be gleaned from setting up a digital environment, and then having a program execute random decisions across multiple iterations in pursuit of the stipulated goals. Almost invariably some strategy will succeed that the programmers/designers never considered viable. But we are talking about trial-and-error here. Digital errors may have no consequences, whereas using this approach in the real world often has significant ones. Also consider that Hexapawn only has twenty-four possible scenarios, whereas the PM environments’ possible situations are, well, endless.

I want to close by reiterating … wait. I need to find the matchbox with “closing paragraph strategies” from my GTIM Educational Robot, and pull out a bead to see what to write next.

[i] Gardner, Martin, “How To Play Hexapawn,” Reader’s Digest Treasury for Young Readers, pp. 176-177.

[ii] Retrieved from https://www.youtube.com/watch?v=rVxedkeOo7w on August 26, 2023, 20:12 MDT.