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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 writt

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.

sxp 2mo ago
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.
- FeepingCreature 2mo ago
  And that GANs haven't been relevant in image generation since 2021.
- bonoboTP 2mo ago
  Transformers are an architecture and GANs are a training method for the architecture. There are GAN Transformers.
  - nullstyle 2mo ago
    Examples for us less educated folk?
    - Alpha3031 2mo ago
      Jiang et al. (2021) TransGAN: https://dl.acm.org/doi/10.5555/3540261.3541391 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.
    - bonoboTP 2mo ago
      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.
  - andy99 2mo ago
    Do you believe it was this edge case he was referring to in making his point?
    - bonoboTP 2mo ago
      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.
bryanrasmussen 2mo ago
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.
- threethirtytwo 2mo ago
  Of course but for obvious mistakes like this one it’s more likely a human hallucinated than an LLM
- red75prime 2mo ago
  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.
  - bryanrasmussen 2mo ago
    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 2mo ago
      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?
      - bryanrasmussen 2mo ago
        I guess, if a Mirage is one of the most common cars that everyone encounters multiple times in a day. The problem really is I haven't used these other AIs for finding links to things I am an expert in, so I cannot really say how likely they are to get things completely wrong, or at least have one wrong or misinterpreted fact per response.

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.

sxp 2mo ago

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.

FeepingCreature 2mo ago
And that GANs haven't been relevant in image generation since 2021.
bonoboTP 2mo ago
Transformers are an architecture and GANs are a training method for the architecture. There are GAN Transformers.
- nullstyle 2mo ago
  Examples for us less educated folk?
  - Alpha3031 2mo ago
    Jiang et al. (2021) TransGAN: https://dl.acm.org/doi/10.5555/3540261.3541391 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.
  - bonoboTP 2mo ago
    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.
- andy99 2mo ago
  Do you believe it was this edge case he was referring to in making his point?
  - bonoboTP 2mo ago
    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.

bryanrasmussen 2mo ago

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.

threethirtytwo 2mo ago
Of course but for obvious mistakes like this one it’s more likely a human hallucinated than an LLM
red75prime 2mo ago
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.
- bryanrasmussen 2mo ago
  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 2mo ago
    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?
    - bryanrasmussen 2mo ago
      I guess, if a Mirage is one of the most common cars that everyone encounters multiple times in a day. The problem really is I haven't used these other AIs for finding links to things I am an expert in, so I cannot really say how likely they are to get things completely wrong, or at least have one wrong or misinterpreted fact per response.