We know this, but it’s kind of weird when you think about it. We had a solid half-century or more of computers not making things up, their sophistic

Hallucinations, Errors, and Dreams

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2024-10-11 22:00:03

We know this, but it’s kind of weird when you think about it. We had a solid half-century or more of computers not making things up, their sophistication and accuracy only improving over time. But in 2024, although you can trust a pocket calculator to give you correct answers to the math problems that you input into it, you can’t trust the world’s most sophisticated artificial intelligence with those very same problems.

I think it’s a very important and multi-faceted question, and in this piece I want to investigate it in some detail. One aspect of the problem involves a major shift over the last 30 years or so in what exactly is meant by “AI”. For a long time, most of what we did when we programmed computers involved finding ways to solve problems exactly. A pocket calculator uses these kinds of methods to produce solutions to math problems which are provably correct. In the past, we thought of the automated application of these precise methods as a form of artificial intelligence. But nowadays, most of what we describe as “AI” refers to applications of Machine Learning. Machine Learning is a paradigm of computer programming where, rather than applying deductive logic to produce output that is known to be correct like a pocket calculator, programs are designed to produce predictions, which are expected to be occasionally wrong. In the first major section of the essay I’ll give an overview of what this means, going over the basic difference between machine learning and older kinds of AI from an extremely high level to see why we expect these kinds of systems to produce errors where more classical computer programs did not.

So one answer to the question of hallucination seems simple: generative AI is machine learning, machine learning is known to produce errors, and a hallucination is an error. This view implies some things about how the hallucination problem may progress in the future: historically we’ve seen that machine learning models make fewer errors as we collect more data and build bigger models. We can expect chat bots and other generative AI systems to become more accurate over time in exactly the same way. But I don’t think that this view is actually correct; hallucinations are, in my view, distinct from errors in the classical machine learning sense. I’m more partial to a view that says that all generative AI output is a hallucination. I’ll explain exactly what I mean by all of this in the second section.