AI in the 80s? How a Simple Animal Guessing Game Pioneered Machine Learning

I believe that the intention was to say "No ability to save progress _between sessions_" in the original program, whereas the C++ implementation saves to text files.

Another portion of the article says more explicitly:

  Limitations:
    Maximum number of nodes: 200.
    The structure is stored only in memory (no disk saving).

True. For example, the Apple ][+ came with a demo disk full of programs for the then-new Applesoft BASIC language, and this was one of them. The questions were saved by POKEing them into arrays hard-coded into the program, allowing you to SAVE the modified program after running it.

It seemed like a neat trick at the time. There was also a crude CRUD database that worked the same way, retaining up to 50 names and phone numbers.

Actually that disk had a lot of really cool programs, now that I think about it. A biorhythm plotter, Woz's "Little Brick Out" Breakout clone, and a few other demos by luminaries like Bruce Tognazzini and Bob Bishop. And of course, ELIZA, the mother of all LLMs... only it was called FREUD for some reason.

Further suggested by the imagery used being AI generated

I read about this idea and actually built a variant in college for the Washington University ACM (our CS student club). We ran a demonstration at one of the engineering school events and ended up refilling the matchboxes multiple times that day - it was a hit!

If I recall, there is a way to significantly reduce the number of matchboxes needed by taking advantage of mirrored board conditions. Somewhere I have a Perl script that generated HTML for all the matchbox covers and possible next states.

That's immensely more interesting than a program that plays 20 Questions. I'm amazed I've never heard of it!

True. The program in the article is an expert system, not a decision tree learner. The confusion probably stems from the fact that decision tree learners are (still?) a very popular machine learning algorithm; and because most people who are into AI and ML today don't know anything about its past.

And what happens to those who don't remember the past? Ask George Santayana.

There is a very important reason why "decision tree" is both a deductive inference procedure and the "model" (really, a propositional logic theory) learned by decision tree learners.

The reason is that the way decision trees, well, make decisions, is the way that expert systems of the '80s made decisions. The first expert system was called DENDRAL (capitals mandatory), derived from the Greek word for "tree", "dendron" (also see: "dendrite"; little tree). DENDRAL and similar systems implement deductive inference procedures that traverse a tree (either bottom-up or top-down, with backward or forward chaining). In traditional expert systems the edges in the tree are production rules of the form IF-THEN-ELSE and the nodes are intermediary goals of what is essentially a proof of a propositional logic formula (the decision tree; it's a boolean formula).

Expert systems' Achilles heel was the so-called "knowledge acquisition bottleneck" which refers, in short, to the difficulty of constructing and maintaining a huge rule-base as needed for an expert system that can do something more interesting than play 20 questions like the animal identification system above. Because of this difficulty, expert systems never quite fulfilled their promises and they suffered from the brittleness that is one of the first thing anyone hears about expert systems today.

And what did people come up with in order to overcome the knowledge acquisition bottleneck? Why, machine learning, of course. The early days of machine learning as a field --remember, we're back in the '80s, a bit before the AI winter hits-- were exactly systems that tried to learn the IF-THEN-ELSE rules for expert systems' rule bases.

Decision tree learners, like ID3 and C4.5 by J. Ross Quinlan, and similar algorithms, that were wildly popular even well after the ImageNet moment of deep neural nets come from that era. Instead of learning only the rules though, decision tree learners, learn an entire decision tree; in short, an entire expert system, from top to bottom.

Well, dear Peggy, the program in question is, in fact, known to have existed in 1974 on Multics (https://multicians.org/terminals.html), so you're not half wrong.

The other major issue is that it isn't machine learning—it's a classic example of an expert system, even if there is a bit of a gray area around whether a binary decision tree qualifies as ML.

The worst part, of course, is that it takes less time to slap "GUESS THE ANIMAL" on a stock image of a glass terminal than it does to prompt a diffusion model to generate the same thing with passable fidelity... and it still wouldn't be an accurate depiction of what the program actually did.

I cried "LLM garbage" too. Catchy title, article full of bizarre errors and repetition.

It would be a prank--LLM writing about AI, badly.

But the Medium account behind it has only articles like this.

It can be difficult to find hero images for articles like this, so people generate them. Why is that bad to you?

The Apple //e version absolutely saved and loaded the tree. I played it just last month with my niece and nephew and it loaded the same ridiculous questions my sister and I had inputted as children 30 years ago.

Rebalancing is not possible not due to code restrictions but due to the nature of the game. How could the program know what new labels to invent for the inner nodes of the rebalanced tree? Unlike integers, animals are not totally ordered.

File IO wasn't portable.

Consider the book where this Animal appeared: David H Ahl was aiming at a wide audience. The content was previously in Creative Computing Magazine.

It was up to the reader to adapt the BASIC programs to work on their machine.

The robot illustrations are the unforgettable charm of that book. It's on the net.

I had some version of Animal on our Apple IIe in the 80s, and I'm pretty sure it had the ability to save the tree. I have vague memories of the disk drive whirring after it asked you to provide a differentiating question. Unfortunately I don't have my later Apple IIGS up and running to prove it.

But I did find this BASIC version for Atari computers, from Antic magazine in April 1986. The article specifies:

"It's vital to save your computer's knowledge base to disk or tape - this knowledge is what makes the game fun. To save, type [CONTROL] [S] and you'll be prompted for a device to save the file to, and a filename. Cassette users should simply type C: at the prompt. Disk drive owners type D: and then the filename. To LOAD a previously saved knowledge base, press [CONTROL] [L] and respond to the prompt as explained just above."

The .BAS file at the end doesn't seem to be a plain-text listing, it's probably a saved Atari file. But viewing it in a hex editor, I see strings in the binary file like "Shall I SAVE this data?"

https://www.atarimagazines.com/v4n12/Animal.html

I remember having this book or similar (at least I remember the robot and the person from the cover) and recall that some things just aren’t portable across all BASIC implementations.

I think ZX Spectrum was what I saw most often, and there would be a couple of things that just didn’t work in CP/M basic, gwbasic or whatever I was using at the time - I imagine file manipulation was avoided for that reason also.

> File IO was extremely hard on the early home computers

Speaking as someone who was there at the time: no, it wasn't. Opening a file, reading/writing data to it, and closing it was a straightforward set of operations on the C64 I had. The I/O was slower, of course.

It didn’t. I implemented this program in BASIC as a teen. The brilliance is that, when it isn’t able to guess your animal, it asks the user for the question to ask, as well as the correct answer. All “intelligence” and grammmar comes from the user, but for someone playing it for the first time on a well-populated database, it looks impressively smart.