Free Software Directory talk:Artificial Intelligence Project Team
- 1 Free software replacements that are missing
- 2 Potential Freedom issues
- 3 Testing model viability
- 4 Stable Diffusion
- 5 Large Language Models
- 6 External links
Free software replacements that are missing
- AI Research Assistant
- https://elicit.org/ - Elicit uses language models to help you automate research workflows, like parts of literature review.
- Voice to instrument: Tone Transfer-like
- Pl@ntNet for Android - Pl@ntNet is a citizen science project for automatic plant identification through photographs and based on machine learning. "The observations shared by the community are published with the associated images under a Creative Common CC-BY-SA license (visible author name)." - https://plantnet.org/en/2020/08/06/your-plntnet-data-integrated-into-gbif/
- Shazam: Shazam is an application that can identify music, movies, advertising, and television shows, based on a short sample played and using the microphone on the device.
- A free app that functions like midomi.com -- "You can find songs with midomi and your own voice. Forgot the name of a song? Heard a bit of one on the radio? All you need is your computer's microphone."
Potential Freedom issues
- Dependencies need to be checked.
- Verify whether a workflow requires non-free GPU or if CPU can be used.
- The training data often contains non-free licensed material.
Purely generated AI content is not copyrightable
"For example, when an AI technology receives solely a prompt from a human and produces complex written, visual, or musical works in response, the “traditional elements of authorship” are determined and executed by the technology—not the human user."
Only the human-generated elements of modifying/arranging AI output are copyrightable
"a human may select or arrange AI-generated material in a sufficiently creative way that “the resulting work as a whole constitutes an original work of authorship.” Or an artist may modify material originally generated by AI technology to such a degree that the modifications meet the standard for copyright protection. In these cases, copyright will only protect the human-authored aspects of the work, which are “independent of ” and do “not affect” the copyright status of the AI-generated material itself."
There appears to be a swath of custom model licenses being used independent of the more standardized software licenses used to interact with models. This presents a conflict as to what license is deemed applicable to the files contained in any repo.
This video (starting at 16:50) illustrates a good argument that model checkpoints may not fall under copyright protection so traditional software licenses that depend on copyright law would be invalid. The video does illustrate that contract law may try to be used it place of copyright. I would advise not using YouTube directly and instead using yt-dl or Invidious.
Testing model viability
Tools are needed to assess the pros/cons of each model.
Due to the issue of merely training a model to become good at whatever tests are on a leaderboard, multiple leaderboards are preferential (hence not putting HuggingFace on the main page). A more comprehensive evaluation would be a meta-analysis of existing leaderboards.
Ordinal value scales could exist for
- indistinguishability from human creations - e.g. Human or Not? social turing chat game
- inference speed
- length of memory
- trivia accurateness
- computation costs: cpu/vram/ram mhz
Source of model training data
- amount of data
- date range (e.g. distinguishing old science from new science for smaller scale models)
- level of censorship (important to make personal+research use distinct from business use)
- creative problem solving (there exists methodology for testing this in humans)
- Larger models are more prone to human superstition, but also generate more human-like readability.
- Quantization (a la GPT-Q) allows consumer hardware to run large models.
Stable Diffusion model files (.ckpt) are released under a non-free license.
Here's the stable diffusion beginning point: https://huggingface.co/CompVis/stable-diffusion-v1-4 https://huggingface.co/spaces/CompVis/stable-diffusion-license
Large Language Models
A guide to decensoring models; I would exercise caution, as it stands to reason an inherently uncensored model would perform better than needing the legwork of decensoring one (and then making mistakes + missing some of the censorship)
Unknown license but still noteworthy
Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the page “GNU Free Documentation License”.
The copyright and license notices on this page only apply to the text on this page. Any software or copyright-licenses or other similar notices described in this text has its own copyright notice and license, which can usually be found in the distribution or license text itself.