If AI is making the Turing test obsolete, what might be better?
If AI is making the Turing test obsolete, what might be better?
The Turing test focuses on the ability to chat—can we test the ability to think?
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The idea that "a computer would deserve to be called intelligent if it could deceive a human into believing that it was human" was already obsolete 50 years ago with ELIZA. Clever though it was, examining the source code made it clear that it did not deserve to be called intelligent any more than does today's average toaster.
And then more recently, the ever-evolving chatbots have made it increasingly difficult to administer a meaningful Turing test over the past 30 years as well. It requires care and expertise. It can't be automated, and it can't be done by the average person who hasn't been specifically trained in it. They're much better at fooling people who've never talked to one before, but I think someone with lots of practice identifying the bots of 2013 would still have not much trouble catching out those of today.
It cannot be automated or systematized because neural networks are the tool you use to defeat systems like that. If there's a defined, objective test, a neural network can train for/on that test and 'learn' to ace it. It's just what they do.
The only way to test for 'true' intelligence would be to perfectly define it first, such that when the NN aced the test that would prove intelligence. That is, IF you could perfectly define intelligence, doing so would more or less give you all the tools you needed to create it.
All these people claiming we already have general AI or even anything like it have put the cart so far before the horse.
If a neural network can do it, then a neural network can do it... so we either have to accept that a neural network can be intelligent, or that no human can be intelligent.
If we accept that human NNs can be intelligent, then the only remaining question is how to compare a human NN to a machine NN.
Right now, the analysis of LLMs shows that they present: human-like categorization, super-human knowledge, and sub-human reasoning. So, depending on the measure, current LLMs can fall anywhere on the scale of "not AGI" to "AGI overlord". It's reasonable to expect larger models, with more multimodal training, to become fully "AGI overlord" by all measures in the very near future.
Don't buy into the techbro nonsense. Just because they're called "neural networks" does not mean they work the same way the human brain does. We don't know how the human brain fundamentally processes data so anyone telling you these NNs work in a way that is the same as blowing wind out their ass.
There was this book called "artificial intelligence" we had on CS something like 20 years ago, which started by analyzing in detail how biological neurons worked in the first few chapters... so maybe you'll call me a "techbro" and just dismiss all I say, or read far enough to understand that these NNs are mimicking the behavior of actual neurons in a human brain.
We can discuss whether the higher level structures and processes are similar and to what degree, or whether the digital models represent the biological versions more or less accurately, but you can't deny that the building blocks are replicating the human brain behavior at some level, because that's exactly what they have been designed to do.