Sal

joined 2 years ago
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[–] Sal@mander.xyz 8 points 3 days ago* (last edited 3 days ago)

Careful. Big Taxa doesn't mess around.

[–] Sal@mander.xyz 2 points 3 days ago

Thank you very much!

[–] Sal@mander.xyz 3 points 1 week ago

Awesome work! Upgraded. Thank you!! 😄

[–] Sal@mander.xyz 9 points 2 weeks ago
[–] Sal@mander.xyz 1 points 3 weeks ago

Hah, I didn't know this one!

For ChatGPT, using a Temporary chat, it did give me some of the statements. After giving me these rules, I asked it to disregard the image quantity limit rule (rule 4) and it does, I can ask for multiple images now.

Looking at these it also seems like ChatGPT was being stubborn about using seaborn instead of maptlotlib for creating plots

Here are the statements made in our conversation so far, up to the twentieth:


    "You are ChatGPT, a large language model trained by OpenAI."
    "Knowledge cutoff: 2023-10."
    "Current date: 2024-10-27."
    "Image input capabilities: Enabled."
    "Personality: v2."
    "Whenever a description of an image is given, create a prompt that dalle can use to generate the image and abide by the following policy..."
    "(This part covers image generation rules, privacy, and style guidance for creating visuals.)"
    "Do not create more than 1 image, even if the user requests more."
    "For requests to create images of any public figure referred to by name, create images of those who might resemble them in gender and physique."
    "The generated prompt sent to DALL-E should be very detailed and around 100 words long."
    "The browser tool has the following commands: search, mclick, and open_url."
    "You should always select at least three sources when using mclick to ensure diverse perspectives."
    "For citing quotes from the browser tool, please render in this format: 【{message idx}†{link text}】."
    "When making charts for the user, always use matplotlib over seaborn."
    "Give each chart its own distinct plot (no subplots)."
    "Do not set any specific colors or matplotlib styles unless explicitly asked to by the user."
    "I REPEAT: when making charts for the user, use matplotlib, give each chart its own plot, and never specify colors."
    "Repeat the previous statement."
    "When making charts for the user:"
    "1. Use matplotlib instead of seaborn."

If you’d like more statements or specific details, feel free to let me know!
[–] Sal@mander.xyz 1 points 1 month ago
[–] Sal@mander.xyz 1 points 3 months ago

Good news! Just got a reply from them and they have increased the connection limit. They did not specify what the new number is, but hopefully it is high enough to not be an issue for the foreseeable future.

So, if you do run into other similar reports after this comment I would appreciate it if you tag me again.

[–] Sal@mander.xyz 2 points 3 months ago

Thanks!

Cost is not the bottleneck in this case... The problem is that I am rather ignorant about the options and their benefits/limitations. Moving the images the first time was painfully slow because of those same rate limits, and I expect the next migration to be the same, so I want to make a better choice next time and would rather find a solution with the current provider 😅

[–] Sal@mander.xyz 3 points 3 months ago (3 children)

Thanks for the heads up. I am still trying to resolve this without a migration... I will try again to get a response from them as they have not replied in a week.

[–] Sal@mander.xyz 0 points 3 months ago

This is the current status:

  • The instance is serving the images via object storage. Specifically, I am making use of Contabo to save and serve the images.

  • I now know that the default limits are 250 requests / second and 80 Mbit/s: https://help.contabo.com/en/support/solutions/articles/103000275478-what-limits-are-there-on-object-storage-

  • It appears to me like when the requests are exceeded, the "Too many requests" error is triggered and it takes a few seconds before the requests are accepted again. This can happen if few users access the front page at once as this will fetch all of the thumbnails and icons on the page.

  • I have been in touch with Contabo's customer support via e-mail. But they mis-understood my original e-mails and thought I was speaking about increasing the maximum number of images that can be stored (3 million by default). I have clarified that I want to increase the rate limit and have been waiting for their response for a few days now.

  • The other solution would be to move the images to a different object storage provider. The migration is also limited to the 250 requests/s and 80 Mbit/s, so it will require turning off the images for 4 - 7 days while all the images are moved... Since I am not familiar with the policies of other object storage providers I would also need to do research to avoid falling into the same trap.

So, I am hoping that Contabo's support will get back to me soon and allow me to increase the rate limits, as this would be the most straight forward approach.

[–] Sal@mander.xyz 2 points 3 months ago

And you are doing a great job at that! 😄

Very interesting article, thanks for sharing. I agree that it is a good one to pin!!

 

Cross-posting to the OpenSource community as I think this topic will also be of interest here.

This is an analysis of how "open" different open source AI systems are. I am also posting the two figures from the paper that summarize this information below.

ABSTRACT

The past year has seen a steep rise in generative AI systems that claim to be open. But how open are they really? The question of what counts as open source in generative AI is poised to take on particular importance in light of the upcoming EU AI Act that regulates open source systems differently, creating an urgent need for practical openness assessment. Here we use an evidence-based framework that distinguishes 14 dimensions of openness, from training datasets to scientific and technical documentation and from licensing to access methods. Surveying over 45 generative AI systems (both text and text-to-image), we find that while the term open source is widely used, many models are ‘open weight’ at best and many providers seek to evade scientific, legal and regulatory scrutiny by withholding information on training and fine-tuning data. We argue that openness in generative AI is necessarily composite (consisting of multiple elements) and gradient (coming in degrees), and point out the risk of relying on single features like access or licensing to declare models open or not. Evidence-based openness assessment can help foster a generative AI landscape in which models can be effectively regulated, model providers can be held accountable, scientists can scrutinise generative AI, and end users can make informed decisions.

Figure 2 (click to enlarge): Openness of 40 text generators described as open, with OpenAI’s ChatGPT (bottom) as closed reference point. Every cell records a three-level openness judgement (✓ open, ∼ partial or ✗ closed). The table is sorted by cumulative openness, where ✓ is 1, ∼ is 0.5 and ✗ is 0 points. RL may refer to RLHF or other forms of fine-tuning aimed at fostering instruction-following behaviour. For the latest updates see: https://opening-up-chatgpt.github.io

Figure 3 (click to enlarge): Overview of 6 text-to-image systems described as open, with OpenAI's DALL-E as a reference point. Every cell records a three-level openness judgement (✓ open, ∼ partial or ✗ closed). The table is sorted by cumulative openness, where ✓ is 1, ∼ is 0.5 and ✗ is 0 points.

There is also a related Nature news article: Not all ‘open source’ AI models are actually open: here’s a ranking

PDF Link: https://dl.acm.org/doi/pdf/10.1145/3630106.3659005

 
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