Is anyone else also getting tired of seeing "GPT" prefix / suffix in the name of 90% new AI-related products?
This isn't a new AI product. It's a (seemingly auto updating) blog entry about GPT-4
GPT Unicorn: A Daily Exploration of GPT-4's Image Generation Capabilities
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Re: GPT Unicorn: A Daily Exploration of GPT-4's Image Generation Capabilities
#32Earlier quoted context omitted.
They appear to be here: https://github.com/adamkdean/gpt-unicorn/blob/master/src/lib... { role: 'system', content: `You are a helpful assistant that generates SVG drawings. You respond only with SVG. You do not respond with text.` }, { role: 'user', content: `Draw a unicorn in SVG format. Dimensions: 500x500. Respond ONLY with a single SVG string. Do not respond with conversation or codeblocks.` }
“You are a helpful assistant” seems like it is always included in these sort of prompts. I wonder if it really helps…
It's too generic I think, my prompt immediately gave me a better result that the ones in his post:
You are a SVG expert, when asked by the user to draw something, you reply to the best of your ability with SVG code that satisfies the request.Re: GPT Unicorn: A Daily Exploration of GPT-4's Image Generation Capabilities
#33This is a great rorschach test. Show these four images to someone hyping AI, and if they see evidence of a growing/emerging intelligence, you can diagnose them as being wholly unqualified to comment on anything related to AI.
I don’t get it, wouldn’t something like HuggingGPT be able to command stable diffusion to do this? Just because GPT can’t do this natively doesn’t mean it’s not possible with the right framework?
Re: GPT Unicorn: A Daily Exploration of GPT-4's Image Generation Capabilities
#34Are they banking on OpenAI updating their model every day, or just prompting the same thing everyday wishing for a different outcome?
Re: GPT Unicorn: A Daily Exploration of GPT-4's Image Generation Capabilities
#35Are they banking on OpenAI updating their model every day, or just prompting the same thing everyday wishing for a different outcome?
In the "sparks of AGI" paper, authors noted that the unicorn shape degrees as more "alignment" is injected to to. If openai adjust the model (say by training more), the picture should reflect it. If they make the model be more "aligned", it should reflect as well.
So I'd guess the answer is the former.
Re: GPT Unicorn: A Daily Exploration of GPT-4's Image Generation Capabilities
#36 \\
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Re: GPT Unicorn: A Daily Exploration of GPT-4's Image Generation Capabilities
#37It can draw a penis fyi
Re: GPT Unicorn: A Daily Exploration of GPT-4's Image Generation Capabilities
#38Why would the model change over time when asking the same question? Just it's generation dataset for generating similar images? Or is this just tracking GPT's explicit model improvements over time?
"GPT 5 Will be Released 'Incrementally' - 5 Points from Brockman Statement" - https://www.youtube.com/watch?v=1NAmLp5i4Ps
gpt-4-0314 is a snapshot model and won't be updated, they shouldn't use that for this experiment.
Re: GPT Unicorn: A Daily Exploration of GPT-4's Image Generation Capabilities
#39Did you generate a bunch all at once before starting to get some idea of what the natural variance looks like? I would think it's important to verify some level of progression over time, because with the current four it seems entirely possible that the examples could have all been generated at the same time with no changes to the model.
Aren’t they using the March 14 model like the general public? It’s frozen in time, there are no updates to it.. All of these will be drawn using the same model until they push a new update, or you switch to a different GPT But I already think they proved the point that the generation is random enough that it would be extremely difficult to track progress this way.
GPT's output is by default somewhat random. If you ask the same exact question several times, you'll potentially get several different answers. Each successive word in the output is chosen from a distribution of possibilities -- that distribution is fixed, but that actual sample chosen from the distribution is not fixed. See, e.g., https://platform.openai.com/docs/api-reference/completions/c...
Re: GPT Unicorn: A Daily Exploration of GPT-4's Image Generation Capabilities
#40This is a great rorschach test. Show these four images to someone hyping AI, and if they see evidence of a growing/emerging intelligence, you can diagnose them as being wholly unqualified to comment on anything related to AI.
I don’t get it, wouldn’t something like HuggingGPT be able to command stable diffusion to do this? Just because GPT can’t do this natively doesn’t mean it’s not possible with the right framework?
These images were all generated by an identical model. The fact that this individual has convinced themself that the model is improving indicates that they don't understand how these models are trained and deployed. Furthermore, any conclusions reached on such limited data reveal more about one's predisposed opinions than anything about the nature of the data. Show this person an ink blot and they very well may see an image of a superintelligent AGI.