“I’m Sorry, Dave. I’m Afraid I Can’t Do That.”

 

I once worked with a Vice President who had a Ph.D. in statistics. On this one occasion we were attending a PM workshop of some kind, and, at the end of the day, were in the hotel/conference center’s lounge, having adult beverages and comparing notes on the paper presentations we had attended. This fellow – I’ll call him “Dave,” because that was his name – told me about his dissertation and its subsequent defense. He had developed it in the 1970s, well before personal computers were available, and mainframe computers were just being introduced to college campuses. Dave had taken a couple of computer courses, but was by no means a competent programmer. He did, however, know how to use the text editor program that the real programmers used, and how to send output to a dot-matrix printer, the kind with a tractor feed and green-and-white shaded paper. Since Dave didn’t have access to a working typewriter, he simply typed out his dissertation on the text editor attached to his college’s mainframe, and printed out the results on the dot-matrix printer.

When Dave presented his text to his faculty sponsor, the professor was awe-struck.

“You wrote your thesis on a computer!?” the professor asked.

“Yeah, I made use of the mainframe in the computer science department.”

Dave confided in me that his review committee never asked for any corrections or comment resolution. The paper sailed right through the review, and the defense was unexpectedly light on questions. It was rather plain that the aura surrounding computers of that time – fueled, no doubt, by the HAL 9000 computer from 2001: A Space Odyssey, known for having “never made a mistake, or distorted information” – had so cowed the review committee that they assumed that the resulting text was flawless.

I was reminded of this story when I saw a paper on an OpenAI project to teach a machine how to play a virtual version of hide-and-seek.[i] Once the “room” and “teams” had been set up, several million game iterations were spent rather chaotically, with iterations 0 – 2.69 million spent on seekers “learning” to chase hiders. Episodes 2.69 to 8.62 million saw the hiders using two virtual cubes to block off the two doors in the play area, and 8.62 million to 14.5 million episodes needed for the seekers to utilize an available ramp. Episodes 14.5 million to 43.4 million saw the hiders learn to stow the ramp prior to blocking the doors, rendering them inaccessible to the seekers.[ii] The AI went on to develop surprising behaviors from both the hiders and the seekers, but the above-referenced progress will do for this blog.

Now consider what would happen if a real-life version of the game environment had been created, and the two hiders and two seekers were fourth-graders (typically around ten years old). Let’s further posit that an average game of hide-and-seek would last around two minutes in such a basic setup. Even if we had fourth graders who could play this game non-stop, it would take them over 165 years to perform this number of “episodes.” I’m fairly confident that four typical fourth-graders could discover the door-blocking and ramp-utilization strategies within one day, or even one hour.

So, why is everyone so in awe of artificial intelligence? Have people in general, like Dave’s thesis review committee, become so over-impressed with the implications of AI’s capabilities that any idea that even has the trappings of being associated with it is given deference with respect to its validity?

In my book Game Theory In Management[iii], one of the “games” I evaluated is known as the Ultimatum Game. In this game, the researcher approaches two random people and informs them that he will give them $100 (USD) if Player A can propose its distribution among the two of them, and have Player B accept those terms on the first iteration. Game Theorists (not me) had “calculated” how to maximize Player A’s payoff: by proposing $99 for Player A, and $1 for Player B. The thinking was that Player B would be presented with the choice of receiving $1, or nothing at all, and would always agree with Player A’s distribution scheme.

A funny thing happened on the way to the actual distribution of the $100, however. This strategy almost never worked when tried in real-life. Perhaps put off by such an unfair distribution of unearned largess, or by other factors, the 99 – 1 strategy was almost always rejected. When confronted with the real-life results indicating a broad-based refutation of the Game Theorists’ calculated optimal strategy, many of them blamed “cultural influences” for the discrepancy. But, by pointing to something as inchoate as “cultural influences,” these Game Theorists were essentially admitting that attempting to calculate a specific strategy, even within the confines of something as basic as the Ultimatum Game, was next to impossible due to the number of contributing factors that couldn’t possibly be recognized, let alone quantified.

And so it is, I believe, with much of what presents itself as artificial intelligence. Sure, it’s fun to see how AI can generate graphic images, or even text (I won’t say literature, at least not yet), but when it comes to creating usable strategies in the Project Management world? Its proper response ought to be “I’m sorry, Dave. I’m afraid I can’t do that.”

[i] Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, Igor Mordatch, “Emergent Tool Use From Multi-Agent Autocurricula,” 17 September 2019, retrieved from https://arxiv.org/abs/1909.07528 on August 12, 2024, 19:45 MDT.

[ii] Ibid.

[iii] Hatfield, Michael, Game Theory In Management, Gower Publishing, 2012.