What we, as a species, did with the stars tens of thousands of years ago is what we’re doing with AI today. We’re looking for patterns and trying to leverage them to make meaning. Now, as then, it’s going to take time to figure out which patterns are meaningful and which are noise.
At least ten thousand years ago, human beings were using the sky to predict the seasons. Knowing how to read the sun, moon, and stars let ancient hunter-gatherer people know when game animals would migrate and when foraged plants would be in season. Later, agricultural peoples would develop sophisticated astronomy to track seasons and anticipate when to plant crops, when to harvest, etc.
While some broad patterns were certainly useful, like the phases of the moon, being able to delve deeper into more subtle patterns clearly had payoffs for this sort of prediction. For example, by 1600 BCE Bronze Age people in what’s now Germany were using this device to remember when to add a leap month to their lunar calendar to bring it back into sync with the solar year. When the star cluster we know as the Pleiades aligns with a spring crescent moon, as depicted, time for a leap month!
Of course, ancient peoples did not limit their astronomy-based predictions to agricultural purposes. They extended them into almost everything. Fortune-telling based on the stars is a long-running practice. Astrology doesn’t work, of course, but one can understand why people might have believed that it would. If understanding patterns in the sky helps to predict the future in the sense of predicting seasons, and more detailed study of more subtle patterns improves the predictive power of these patterns, then if you look close enough and discern still more subtle patterns then perhaps you can predict other things too.
Today the sorts of subtle and sophisticated astronomical calculation that would have taken the ancients decades of work and careful study can be done in fractions of a second by even the simplest computer. You can download a friendly python package and, with just a modest amount of programming skill, predict eclipses or planetary positions into the distant future. If you’re not a coder, that’s OK, there’s a free astronomy website that’s just as good.
The reason computers are so good at astronomical prediction is that stars and planets are quite mathematically simple, all told. In space, a smidgen of Newtonian Physics goes a long way. Orbits whirl on in the serene order of gravity and momentum. The small wobbles of the precession of the Earth’s axis and the proper motion of stars are easy enough to account for. The future can be known for a very long time indeed without even having to resort to Einstein’s more sophisticated model of gravity (and, once you account for that, you can know the astronomical future almost forever).
Here on Earth, however, things are not so predictable. Piloting spacecraft has been a task we could relegate almost entirely to automated systems, even when those automated systems were less capable than a graphing calculator, but getting a computer to drive a car on a human highway is something that increasingly looks to be like nuclear fusion, always just a few more years away.
But while getting computers to understand and predict our messy, Earthbound world remains a work in progress, it is true that machines have gotten much better at this lately. Computer vision was once so bad that you could safely use a simple image recognition task as a CAPTCHA to prevent automated systems from buying up movie tickets or submitting online reviews. Now I can grab a free image recognition library from huggingface, cobble together a few lines of Python (mostly by modifying the sample code provided with the library) that ask it to scan an image for “bees” and get results like this, which show where it detected “bees” (just described exactly so, with one plain English word) in an image of my hive entrances:
It’s not perfect, of course, it misses the bee in the dark corner and conflates two bees near each other into one big mega bee, but it’s pretty good! Especially since this is an image captured of bees just going about their day, wandering by the camera at various angles and speeds.
The messiness of human language is also increasingly something machines can parse. My example above demonstrates a small piece of that, since the library I used was capable of connecting the English word “bee” to the bees in my image without having to be told what a “bee” was. Large Language Models, like the (infamous) ChatGPT build on this, recognizing/predicting appropriate written responses to natural language prompts:
A lot of folks will immediately notice that the poem above is not terribly good, but that’s not the point. The point is that the language model was able to parse “educational rhyming poem about bees” and respond in an appropriate way.
Frankly, I think demonstrating that ability of the model to parse and respond to language is all LLM chatbots were ever meant to do. They are a technology demo, and the large numbers of attempts we see to use them as ersatz persons because they generate sort of human-like language are doomed to fail.
But that argument will have to wait for another post, for now its that predictive ability that’s interesting. Just as the ancients found stars could predict the seasons, so too the deep learning/machine learning/artificial intelligence techniques responsible for both the image detection and language generation methods I demonstrate above find predictive patterns in more earthly sets of data. This is already happening. Researchers are using these techniques to discover new methods for treating cancer. Digital humanists are using them to explore the idea of suspense in literature (among other things).
But of course, not all predictions are meaningful. The predictions of astrology take the correct observation that stars predict seasons and draw from it the incorrect inference that, if we just learn EVEN MORE about the motions of stars and planets we can predict the events of our daily lives. So too, the attempts to use machine learning techniques to predict anything and everything often veer into futile attempts to make conclusions from insufficient data (as in the attempt to use quasi-magic AI weapons detection tech on the Philly SEPTA) or to use the “scientific” reasoning of AI to justify inhuman decisions made by people (as in the IDF’s use of the so-called LAVENDER system to target thousands of supposed combatants in Gaza).
It’s not necessarily going to be immediately clear in every case what’s a genuine, meaningful machine learning prediction, and what’s not. I would, as an initial instinct, suggest that meaningful predictions are more likely to be limited in scope and tied closely to well formed and carefully constructed research questions. These are exactly the sorts of predictions that don’t always get press! The tech sphere loves “scale” and it loves everything machines.
Still, over the next few years, we’re going to have to work out how to distinguish between astronomy and astrology in AI.
