ChatGPT is a revolutionary technology for sure, but despite its impressive capabilities, it’s still just a ChatBot, and it is far from actually being intelligent.
Alan Turing, one of the foundational figures in computer science, was obsessed with artificial intelligence towards the end of his tragically short life. So much so, in fact, that he came up with an unofficial test for when a computer can be said to be truly intelligent like a human, what we now call The Turing Test.
The test is pretty simple. Have someone communicate with a computer and if they cannot tell that they are talking to a computer — that is, it is indistinguishable from a human to another human — then the computer would have risen to the level of human intelligence.
Well, ChatGPT sure seems to pass that test with flying colors (opens in new tab). In fact, it’s not just passing Turing’s test, it’s passing med school exams, law school exams, and pretty much does the homework for every kid in the United States at the very least.
And if you don’t know what ChatGPT is, it can seem overwhelming. So much so that people are starting to project onto ChatGPT, and generative AI in general, qualities and human characteristics that it actually doesn’t have.
Normally, this wouldn’t be too big a deal. People misunderstand all kinds of things all the time, but because ChatGPT-like AIs are only going to become more widespread in the coming months, people are going to invest in them powers that they don’t actually have, and if misused under that assumption, they can be far more harmful than they are helpful. What is an adversarial generative AI?
The foundational technology behind ChatGPT, Stable Diffusion, and all the other AIs that are producing imagery, test, music, and more is what’s known as a Generative Adversarial Network (GAN). I won’t get too in the weeds here, but essentially a GAN is two software systems working together. One is producing an output, the generator, and the other is determining if that data is valid or not, the classifier.
The generator and classifier in a GAN move word-by-word or pixel-by-pixel and essentially fight it out to produce a consensus before moving on to the next segment. Bit by bit (literally), a GAN produces an output that very closely replicates what a human can do, creatively.
The generator relies on an obscene amount of input data that it “trains” on to produce its outputs, and the classifier relies on its own inputs to determine if what the generator produced makes sense.
