Out of Book

In The Most Human Human, Brian Christian muses about his experience playing a “confederate” during the 2009 Loebner prize. It’s an annual Turing Test competition where entrants have designed artificial intelligence computer chatbots to mimic a human. A judge has a five-minute conversation over a computer terminal with a chatbot or an actual human being, and then has to decide which is which. As a “confederate” Brian is one of the humans. The chatbot that best fools the judges into thinking it’s a human is dubbed “The Most Human Computer”. The human that best convinces the judges of his or her humanity is “The most human human”.

 

Chapter 5 of Brian’s book, titled “Out of Book”, refers to where chess games at interesting. Chess openings and endings are often scripted. Over time a database builds up on effective opening moves and their variants. The same is true at the endgame where few pieces remain on the board and a brute-force analysis can determine who the winner will be. The Book refers to this wealth of knowledge in chess openings and endgames. It’s the midgame where things gets interesting, when players are forced into out-of-Book situations. Computer chess programs are loaded with The Book. All decent programs have them. What might make a program superior to others is how it handles the out-of-Book midgame. What makes a chess player a grandmaster is being able to successfully navigate the midgame.

 

At the novice level, memorizing more openings and endgames and practicing them a lot, often leads to victory over someone with a less prodigious memory. You get into a superior position by standing on the shoulders of the grandmaster giants who have gone before, and the game is about who blunders first by playing a known inferior move from the database. You don’t really need to understand the why behind the moves; you just need to execute them in the correct sequence. At the mastery level for humans, understanding the why is crucial to know when to effectively get out-of-Book and try to outplay your opponent with ingenuity. The challenge in playing against today’s computers is that they can store a huge Book and shrink the space between opening and ending. You might lose to a computer program before even getting out-of-Book.

 

All this makes me thinks of A.I. use in education. The majority of my students use chatbots to help them complete homework assignments and study for exams. They think it helps their learning, and that may be true some of the time, but I suspect it also leads to a dose of self-deception. They think they know something but when exam time comes (with no book or chatbot to consult), they don’t perform well. My exam questions should be no surprise based on what we cover in class and the study guides I provide, and the academically strong students have no problem doing well.

 

In contrast, I see a mishmash of nonsense written by the students who have no clue what’s going on.  They don’t know what they don’t know. If they made the effort to memorize the worked examples and explanations in class, they might do a tad better on the exams, but that’s not what’s happening. Instead, they cut-and-paste a question to pose to a chatbot, and read the answer thinking they understand it. Compounding the issue is that if you have little understanding of the subject matter, you are unable to tell if the chatbot answer is correct, wrong, or not-even-wrong. Large Language Model chatbots are designed to sound plausible. Their prodigious memory means they are likely to string together words that sound like the right answer. Maybe it is, maybe it isn’t, maybe it’s simply misleading and neither here-nor-there.

 

A.I. is hailed as a potential disruptor and savior of education. Its champions are the tech companies trotting out deals to hook the young early in the hopes that they will shell out money for premium access later. Schools and universities are stirred into a frenzy by FOMO vibes and guilt-tripped about not preparing their students for the upcoming A.I. revolution. You’ve gotta be able to access The Book. Everyone’s doing it! But like the novice chess players who consult a book to advance their moves without understanding the deeper reasons, novice chemistry students consult an A.I. chatbot and now think they have advanced their understanding of the subject matter.

 

I think that A.I. coupled with knowledge-expertise can be an excellent tool for discovery and pushing the frontiers of knowledge. There lies the out-of-Book realm, ripe for discovery. For novices, though, chatbot education only provides a shallow and self-deceptive “learning”. If the world is headed towards an idiocracy, this might not matter. What pains me is that the bifurcation between the haves and have-nots will continue to expand, and it may devolve into total anarchy or a totalitarian state of affairs.

 

Interspersed with the stories of computer chess, Brian concludes his chapter by thinking about the conversations between humans. There’s an opening and closing, mostly scripted, and a potential interesting middle where two people might learn something new about each other and themselves. In a brief chat with a stranger or an acquaintance, one might never stray out-of-Book. Many top chatbots in the Loebner prize were able to steer the conversation to stay in The Book, banal yet effective. It encouraged me to think about getting out-of-Book and become a better conversationalist. Maybe I can try out some new surprising out-of-Book lines with my students and make a better connection this new year!