I paid my way through university partly by nursing. I worked in a hospital for a few years. All the staff in the hospital, and particularly those in the emergency ward, knew, for an absolute fact, that people went crazy on the night of the full moon. On the night of the full moon, all kinds of people did all kinds of weird things, and got themselves into trouble, and ended up in the emergency ward.
As I say, I was working my way through university. And one of the courses that I took was in statistics. I was interested to discover that there had been quite a number of studies that had been done on this issue of the full moon. And that every single one of the studies had determined exactly the same thing: there was absolutely no truth to the common perception that people went crazy on the night of the full moon.
As a matter of fact, this belief that everyone goes crazy on the night of the full moon is so deeply embedded into our culture that it is odd that, when you actually look at the statistics and the numbers, there isn't even a blip in regard to full moon nights. This belief is so deeply ingrained in our society that you would expect that some people would let themselves go a little crazy on the night of the full moon, expecting to be forgiven for any weirdness because of that cultural belief. But no, there isn't even a blip in the statistics around the night of the full moon.
So, why do so many hospital staff, and so many police officers, and so many people who work in emergency services, so strongly believe that people go crazy on the night of the full moon?
Well, there is a kind of observational bias that is at play here. If you work in an emergency ward, and you have a night where everything is going crazy, and you finally get five minutes to get yourself a breath of fresh air, and you walk out and look up into the night sky, and there is a full moon, you say to yourself, oh, of course. And that reinforces the belief. If the night is crazy and you go and look up into the sky and there is no full moon, you don't think anything of it. And on normal nights, when there is a full moon, you don't have any particular reason to pay attention to the full moon, and so that doesn't affect the belief either.
One of the other areas of study that I pursued was in psychology. Behavior modification was a pretty big deal at the time, and we knew that there were studies that confirmed how subjects form superstitions. If you gave random reinforcement to a subject, the subjects would associate the reward with whatever behavior that they had happened to be doing just before the reward appeared, and that behavior would be strengthened, and would occur more frequently. Because it would occur more frequently, when the next random reward happened, that behavior would likely have occurred recently, and so, once again, that behavior would be reinforced and become more frequent. In animal studies it was amazing how random reinforcement, presented over a few hours or a few days, would result in the most outrageous obsessive behavior on the part of the subjects.
This is, basically, how we form new superstitions. This is, basically, why sports celebrities have such weird superstitions. Whether they have a particularly good game, or winning streak, is, by and large, going to be random. But anything that they happen to notice that they did, just before or during that game, they are more likely to do again. Therefore they are more likely to do it on a future date when, again, they have a good game or win an important game. This is why athletes tend to have lucky socks, or lucky shirts, or lucky rituals. It's developed in the same way.
One of the other fields I worked and researched was, of course, information technology, and the subset known as artificial intelligence. Artificial intelligence is not, despite the current frenzy over generative artificial intelligence and large language models, a single entity, but rather a variety of approaches to the attempt to get computers to behave more intelligently, and become more useful in helping us with our tasks. One of the many fields of artificial intelligence is that of neural networks. This is based on a theory of how the brain works, that was proposed about eighty years ago, and, almost immediately, was found to be, at best, incomplete. The theory of neural networks though, did seem to present some interesting and useful approaches to trying to build artificial intelligence. As a biological or psychological model of the brain itself, it is now known to be sometimes woefully misleading. And one of the things that researchers found, when building computerized artificial intelligence models based on neural networks, was that neural networks are subject to the same type of superstitious learning to which we fall prey. Neural networks work by finding relations between facts or events, and, every time this relation is seen, the relation in the artificial intelligence model is strengthened. So it works in a way that's very similar to behavior modification, and leads, frequently, to the same superstitious behaviors.
The new generative artificial intelligence systems based on large language model are, basically, built on a variation of the old neural networks theory. So it is completely unsurprising to see one of the big problems that we find with generative artificial intelligence, is that it tends, when we ask it for research, to present complete fictions to us as established fact. When such a system presents us with a very questionable piece of research, and we ask it to justify the basis of this research, it will sometimes make up entirely fictional citations in order to support the proposal presented. This has become known as a "hallucination."
Calling these events "hallucinations" is misleading. Saying "hallucination" gives the impression that we think that there is an error in either perception or understanding. In actual fact, generative artificial intelligence has no understanding, at all, of what it is telling us. What is really going on here is that we have built a large language model, by feeding a system that is based on a neural network model a huge amount of text. We have asked the model to go through the text, find relationships, and build a statistical model of how to generate this kind of text. Because these systems can be forced to parrot back intellectual property that has been fed into them, in ways that are very problematic in terms of copyright law, we do, fairly often, get a somewhat reasonable, if very pedestrian, correct answer to a question. But, because of the superstitious learning that has always plagued neural networks, sometimes the systems find relationships that don't really relate to anything. Buried deep in the hugely complex statistical model that the large language models are built on, are unknown traps that can be sprung by a particular stream of text that we feed into the generative artificial intelligence as a prompt. So it's not that the genAI is lying to us, because it's only statistically creating a stream of text based on the statistical model that it has built with other text. It doesn't know what is true, or not true.
There is a joke, in the information technology industry, that asks what is the difference between a used car salesman, and a computer salesman. The answer is that he used car salesman knows when he is lying to you. The implication of course (and, in my five decades of working in the field I have found it is very true), is that computer salesman really don't know anything about the products that they are selling. They really don't know when they are lying to you. Generative artificial intelligence is basically the same.
AI topic and series
Introduction and ToC: https://fibrecookery.blogspot.com/2026/01/ai-000-intro-table-of-contents.html
No comments:
Post a Comment