He hallucinated. This is how you verify non AI nowadays.

AI is so good that if you see an hallucination as obviously wrong like this one it’s a sign it’s written by a human.

Yeah. Ironically, if he used an LLM to proofread his work, it would have told him that GAN's are generative not "generalized". That LLMs are primarily built on Transformers rather than GANs. And that they're more famous for image generation rather than image recognition.

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bonoboTP 21 hours ago | parent | next [-]

Transformers are an architecture and GANs are a training method for the architecture. There are GAN Transformers.

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andy99 19 hours ago | parent | next [-]

Do you believe it was this edge case he was referring to in making his point?

	▲	bonoboTP 18 hours ago \| parent [-]
		No, I simply think the person is not familiar with these concepts, which is not a crime, it's okay to be wrong, I didn't want to banish that person from here, just corrected the statement. But might have been also a brain fart. Happens. Just wanted to correct it in the interest of beginner reading this. Once Musk said he believes in Transformers instead of diffusion (or the other way around). When actually diffusion transformers are very popular and mainstream. What he meant was autoregressive inference vs diffusion. People like to use buzzwords while not knowing them. Old issue, it was the same decades ago. To make it even more confusing, there are also diffusion LLMs, which typically but not necessary, use transformers also. And independently of diffusion and transformer and llm or image generator, you can optionally put a GAN discriminator adversarial loss on any of them.

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nullstyle 20 hours ago | parent | prev [-]

Examples for us less educated folk?

	▲	bonoboTP 19 hours ago \| parent \| next [-]
		Analogy "it's not GAN, it's transformer!" - - "it's not a recursive implementation, it's object oriented!" if you are more familiar with CS. Or "it's not 4-wheel-drive, it's diesel!", if familiar with cars.
	▲	Alpha3031 19 hours ago \| parent \| prev [-]
		Jiang et al. (2021) TransGAN: https://dl.acm.org/doi/10.5555/3540261.3541391 In general probably not much of a stretch to get rid of convolutions or recurrence by replacing with attention and see if it works hence the title of the original transformers paper.

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FeepingCreature 21 hours ago | parent | prev [-]

And that GANs haven't been relevant in image generation since 2021.

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bryanrasmussen 20 hours ago | parent | prev [-]

sorry but I see incorrect AI responses almost every day in things I query on Google, generally because I am often querying on something I am an expert in and I just want a linking source. There will be a good link in the result, generally, but the AI almost always messes up some elemental fact, sometimes it is because the AI has "misunderstood" what I asked which is reasonable, but most of the time the result shows that it has "understood" what I said but there will be a statement of fact that is just wrong.

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red75prime 10 hours ago | parent | next [-]

The AI you are talking about is a low-inference-cost model optimized for massive throughput.

It's not like every model has the same amount of those shortcomings.

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bryanrasmussen 9 hours ago | parent [-]

I apologize for having generalized from a specific AI to all, given that the comment I replied to made a claim regarding all AI.

	▲	red75prime 8 hours ago \| parent [-]
		Someone says "cars are so comfortable now." Someone else responds "In my experience they are no better than in the 80s." Isn't it prudent to point that the second person drives Mirage or something like that? The first sentence seems to imply the average state. No?

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threethirtytwo 18 hours ago | parent | prev [-]

Of course but for obvious mistakes like this one it’s more likely a human hallucinated than an LLM