Media Title Clearance KNN reverse restriction technology
Attn new film makers in AI space using Disney model. Please be aware that market forces may be testing legality of produce generated out of ml trained data to own produce of text to image when using owned large image data sets. Please be sure to cite percentages of creator owned art if your intention to change source film footage of your own includes this Disney ml model.
Current projects that explore ml data set market spaces as in game industry for generative art are emergent and I do hope artist and uses of reverse knn technologies are not carefully cultivated for exploitation by large data clearing houses. Meaning Laion and Disney ml data set differences in owned data used to train on are proving the duality of market included both generator and discriminator technology.
By using Disney ml model you empower the knn reverse search discriminator market at same level of generator market. Very simply put the Disney ml data set and industry copyright and media title chain clearance insurance for films using generative technologies are using bound development process to pair both discriminator/generator technology in synchronous agile DevOps space within the larger censor and copyright goal to regulate ml data sets of this nature (lest free viral marketing).
As pioneers in this industry it is upon you and other users to be aware of the cost of exploring a large Disney copyright and reverse search weighting technology in synchronous format to be certain not to enable (unwittingly) the impending closing of the fences on this amazing new technology. It is one thing to make a 3 year long film and train a data set for ml generator purposes but all together another to scrape and acquire data to materialize the real market Disney may wants under Marvel-Disney leadership which is to prevent training data on IP holdings and to potentially induce us copyright office "data base copyright laws," with engineering of a regulatory scheme to prejudice and be alerted when social media post using Disney ml watermarked or other methods exist to track content across diverse users.
Be attentive to bound state development for generator/discriminator technology as mutually consistent DevOps pipelines exist and the two working in tandem to both generate and discriminate equate to a provincial or epistemological method of a economic constraint on others seeking to materialize large public domain data sets as well creator royalty driven spaces such as art station ml keyword linked artist.
If anyone would like to discuss thoughts on these issues of corporate large image data sets being used as "bantha fodder," to convince markets of potential lost goods and to invoke ml and ai pipeline in divisions of Disney studios that have stop gap or loss evaluators working right now to asses market for per film ml data set commodity then please do continue discussion below.
I lead efforts in a film production for curtailing in line labeling of film to ml training . My business is concerned with this first to market product only as materializes a larger "data base," copyright argument that is at the root of media title clearance insurance for production insurance and preparing to remove content from media using generative techniques when cease and desist are used. This has been a costly plan to make films secondary to ml data sets as a collective film for acquiring machine learning data that prepared for removal of images as a market space for discriminator technology in AI policy issues prevalent to the copyright cycle.
If you would like to review the work done for this market to succeeds without forerunners guiding us copyright office schemes then please review . https://titleixfromcyberspace.sitelio.me/ I hope film makers who begin their journey to use AI and ML take notice of the importance of a closed loop pipeline and make every effort to empower content collaboration and inline labeling methods that open this market to responsible film making to make generative films from the film production spaces. No one ever imagined making films would be secondary to marketing training data in a film production pipeline in order to make generative content. But alas here we are, the film production space is changing and artist who are marginalized in the process are seeking to make certain their skills are not undermined and thus as ML data scientist in the pipeline it is called upon your hard work to be respectful and to understand how to be leaders in the organizational culture of the production space. If you use cultural appropriation methods in a mass scale for large language models and large image data sets pleas be sure to include citations of artist used. Artist understand this is a double edged sword with software engineers and likewise seek to find the balance to advocate a mutually important and respectful relations in these exciting times of mass consciousnesses discovery modes of self expression and all the valuable understandings that are coming from the large data models as data scientist who now explore new modes of social understanding.
My project focused on the removal of a character from a story and AI film production to deliberate the mutually exclusive pace of discriminator and generator technology. Thank you for your concern in these issues when you download Disney diffusion models and as always pleas be sure to cite the Disney ownership for their hard work to contribute to this diffusion model. For more information on how to protect your proprietary methods of the data set please review. Noting as all the hard work you have done to make this ML data set available I feel maybe you should take time to think of the proprietary nature of the copyright of a data base and should review https://www.copyright.gov/reports/appendix.pdf