Generative AI Imagery (GAI)
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Version 3.0
Friday, July 30th
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Artificial Intelligence (AI) generative imagery: understanding, detection and capabilities are imperative. The fast moving developments of generative artificial intelligent imagery, including multimedia (Still, video & audio). Can be beneficial or detrimental.
Benefits for individuals, small businesses and others are tremendous with grasping Artificial Intelligence (AI) generative imagery: equally not grasping and protecting again Artificial Intelligence (AI) generative imagery, could be devastating.
It’s imperative to have comprehensive understanding and capabilities with generative AI imagery. Plan supportive AI imagery for
participants.
Coming soon everyone with smartphone and Internet connection will be able to have AI generation creation and dissemination capabilities.
Discerning the difference between reality and techno generated multimedia images will be nearly impossible.
Cyber security for each individual will be necessary scanning the internet with artificial intelligence (AI) for erroneous content will be imperative for protecting individuals and entities. The threat is real must be properly technologically addressed. (Fighting Fire with Fire).
Publicly trade companies during trading hours, especially could be extremely vulnerable to false and misleading information particularly when it’s multimedia information presented from authoritative source.
The plan is to protect and detect falsely created negative AI generated content. Using Artificial Intelligence with supercomputer services 24,/7/365 often the entire Internet to be search. Plus and any content, including news events to be screened for authenticity.
Significant Artificial Intelligence (AI) generative imagery, development by technology avengers, suppliers and community are expected to occur rapidly immediately.
Artificial Intelligence (AI) generative imagery, awkward exclusively to participants.
In a blog post, Stability AI, which popularized the Stable Diffusion image generator, calls the new model SDXL 0.9, short for Stable Diffusion XL. Stability says the model can create images in response to text-based prompts that are better looking.
Disclaimer
No information provided to be relied upon for decision making.
Forward-looking statements
Information provided contains significant amount of forward-looking statements.
Military, law enforcement & others
It is not planned to provide Artificial Intelligence (AI) generative imagery services or support to military branches, law enforcement agencies or other institutions. Some limited coaching is expected to occur. Individuals with the military law enforcement and others to be provided Support Services.
Open source
All information publisher provided is open source not proprietary and can be used by anyone for any useful purposes at any time.
Background
Generative artificial intelligence Article;
In recent months, generative artificial intelligence has created a lot of excitement with its ability to create unique text, sounds, and images. But the power of generative AI is not limited to creating new data.
The underlying technology of generative AI, including transformer and diffusion models, can power many other applications. Among them is the search and discovery of information. In particular, generative AI can revolutionize image search and enable us to browse visual information in ways that were previously impossible.
Here is what you need to know about how generative AI is redefining the image search experience.
Image and text embeddings
Classic image search relies on textual descriptions, tags, and other metadata that accompany images. This limits your search options to information that has been explicitly registered with the images. People who upload images must think hard about the kind of search queries users will enter to make sure their images are discovered. And when searching for images, the user who is writing the query must try to imagine what kind of description the uploader might have added to the photo.
However, as the saying goes, an image is worth a thousand words. There is only so much that can be written about an image, and depending on how you look at a photo, you can describe it in many different ways. Sometimes, you are interested in objects. Other times, you want to search images based on style, lighting, location, etc. Unfortunately, such rich information rarely accompanies images. And many images are just uploaded with little or no information, making it very difficult to discover them.
Article with redactions possible
Artificial Intelligence (AI) imagine a generator. Can amazingly creative and stunningly beautiful.
Artificial intelligence (AI) can take massive amounts of information and generate new content. While this could have industry-changing implications in terms of efficiency and productivity, it can also be put to nefarious purposes if AI “deepfakes” spread potentially harmful disinformation, indistinguishable from reputable content.
Fortunately, the cause of the problem may also be the source of the cure: The same generative AI that churns out phony videos can also be trained to help separate the real from the fake in a deluge of derivative content.
“While generative technology is abused to commit fraud, spread fake news and execute cyberattacks on private and public organizations, it can also help AI systems identify and flag deepfakes themselves,” says Ed Stanley, Morgan Stanley’s head of thematic research in Europe. “Software that can achieve this will have an especially important role in the online reality of the future.”
Fighting Fire with Fire
Deepfakes—digitally manipulated images, audio or video intended to represent real people or situations—aren’t new. But the ease, speed and quality with which they can be created has elevated the urgency to enact safeguards and devise smart countermeasures.
Though clearly doctored celebrity videos were among the first generation of deepfakes, more recent examples reveal two critical shifts: First, today’s deepfakes can be created in real time, which presents problems for businesses whose data security depends on facial or voice biometrics. Second, hyper realistic facial movements are making AI-created characters indistinguishable from the people they are attempting to mimic.
“Traditional cybersecurity software is likely to become increasingly challenged by AI systems,” Stanley says, “so there could be strong investment plays in AI technology directed as training tools to help employees and consumers better decipher misleading versus authentic content.”
For example, some companies specialize in both creating and detecting deepfakes using large, multi-language datasets. Others use troves of data to create deepfake detectors for faces, voices and even aerial imagery, training their models by developing advanced deepfakes and feeding them into the models’ database. Such AI-driven forensics analyze facial features, voices, background noise and other perceptible characteristics, while also mining file metadata to determine if algorithms created it and, in some cases, find links to the source material.
“Some of the best opportunities seem to lie in applications for safety and content moderation,” says Stanley, “especially as valuations for large-language models and leading players in the space have priced out some participants.”
Stable Diffusion image generator. Planning on using Stable Diffusion technology for Projects (forprofit) and Programs (nonprofit) to make a better world. Electric Vehicle (EV) substantable transportation of people and products focused.
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Hyper-realistic creations for films, television, music, and instructional videos, as well as offering advancements for design and industrial use, places SDXL at the forefront of real world applications for AI imagery.
Artificial intelligence startup Stability AI is releasing a new model for generating images that it says can produce pictures that look more realistic than past efforts.
In a blog post, Stability AI, which popularized the Stable Diffusion image generator, calls the new model SDXL 0.9, short for Stable Diffusion XL. Stability says the model can create images in response to text-based prompts that are better looking.
The SDXL series also offers a range of functionalities that extend beyond basic text prompting. These include image-to-image prompting (inputting one image to get variations of that image), inpainting (reconstructing missing parts of an image), and outpainting (constructing a seamless extension of an existing image).
System requirements
Despite its powerful output and advanced model architecture, SDXL 0.9 is able to be run on a modern consumer GPU, needing only a Windows 10 or 11, or Linux operating system, with 16GB RAM, an Nvidia GeForce RTX 20 graphics card (equivalent or higher standard) equipped with a minimum of 8GB of VRAM. Linux users are also able to use a compatible AMD card with 16GB VRAM.
Beta launch statistics
Since SDXL’s beta launch on April 13, we’ve had great responses from our Discord community of users numbering nearly 7,000. These users have generated more than 700,000 images, averaging more than 20,000 per day. More than 54,000 images have been entered into Discord community ‘Showdowns’ with 3,521 SDXL images nominated as winners.
Stable Diffusion image generator, Artificial intelligence (AI) images: