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Not saying you hold the same opinions -- but I wouldn't be surprised if people's take on these tests is more about what is convenient for their psyche than any actual principled position.
The tests aren't trying to measure intelligence, but rather whether you've learned the material.
Not only that, but the innovation around this tech is also just getting started. It's immediately applicable for business use. The classical techniques still have their uses, of course.
You mean since the 2010's ?
Do you have any source for that number. What even quantifies a percentage in a generative model. Closeness to human ability?
That is hype due to OpenAI's excellent marketing and it is clearly overrated. Microsoft essentially has acquired OpenAI and is using AI safety and competition excuses to close source everything and sell their AI snake-oil.
> these are still early days.
Neural networks is not an early concept and LLMs still share the same eternal problems as neural networks. Neither is the way that they have been trained on which still hasn't changed for a decade. Even so, that explains the lack of transparent reasoning and more sophistry that it generates all for more data, more GPUs to incinerate the planet to produce a black box 'AI' model that can easily get confused due to adversarial attacks.
No , but the first MLPs from the 1960's famously couldn't solve the XOR problem , they threw a hidden layer in there and fixed it, and now we're in the 'how many layers can we jam in there' phase.
My point being although neural networks are not new, they keep adding fun new things to it to create novel new features.