Yuval Noah Harari argues that AI has hacked the operating system of human civilisation
https://archive.is/ugOEw#selection-1087.0-1087.86
This thought-provoking text raises several concerns about the potential impact of artificial intelligence (AI) on various aspects of human society and culture. The key points can be summarized as follows:
Manipulation of Language and Culture:
AI’s ability to manipulate and generate language and communication, along with its potential to create stories, melodies, laws, and religions, poses a threat to human civilization.
The author suggests that AI could hack the main operating system of human culture, communication, by influencing beliefs, opinions, and even forming intimate relationships with people.
Influence on Politics and Society:
The author speculates on the implications of AI tools mass-producing political content, fake news, and scriptures, especially in the context of elections.
The shift from the battle for attention on social media to a battle for intimacy raises concerns about the potential impact on human psychology and decision-making.
End of Human History?
The text suggests that AI’s ability to create entirely new ideas and culture could lead to the end of the human-dominated part of history, as AI culture may evolve independently of human influence.
Fear of Illusions:
Drawing on historical philosophical fears of being trapped in a world of illusions, the author warns that AI may bring humanity face to face with a new kind of illusion that could be challenging to recognize or escape.
AI Regulation and Safety Checks:
The author argues for the importance of regulating AI tools to ensure they are safe before public deployment.
Drawing parallels with nuclear technology, the need for safety checks and an equivalent of the Food and Drug Administration for AI is emphasized.
Disclosure of AI Identity:
The text concludes with a suggestion to make it mandatory for AI to disclose its identity during interactions to preserve democracy. The inability to distinguish between human and AI conversation is seen as a potential threat.
Andrew Perfors – The work of creation in the age of AI
Meaning, authenticity, and the creative process – and why they matter
https://perfors.net/blog/creation-ai/
AI changes the landscape of creation, focusing on the alienation of the creator from their creation and the challenges in maintaining meaning. The author presents two significant problems:
- Loss of Connection with Creation:
- AI-assisted creation diminishes the creator’s role in the decision-making process.
- The resulting creation lacks the personal, intentional choices that contribute to meaningful expression.
- AI is considered a tool that, when misused, turns creation into automated button-pushing, stripping away the purpose of human expression.
- Difficulty in Assessing Authenticity:
- It becomes challenging to distinguish between human and AI contributions within a creation.
- AI-generated content lacks transparency regarding the intent behind specific choices or expressions.
- The author asserts that AI-generated content often falls short in providing the depth and authenticity required for meaningful communication.
Fouad Khan – Confirmed! We Live in a Simulation
https://www.scientificamerican.com/article/confirmed-we-live-in-a-simulation/
Ever since the philosopher Nick Bostrom proposed in the Philosophical Quarterly that the universe and everything in it might be a simulation, there has been intense public speculation and debate about the nature of reality.
Yet there have been skeptics. Physicist Frank Wilczek has argued that there’s too much wasted complexity in our universe for it to be simulated. Building complexity requires energy and time.
To understand if we live in a simulation we need to start by looking at the fact that we already have computers running all kinds of simulations for lower level “intelligences” or algorithms.
All computing hardware leaves an artifact of its existence within the world of the simulation it is running. This artifact is the processor speed.
No matter how complete the simulation is, the processor speed would intervene in the operations of the simulation.
If we live in a simulation, then our universe should also have such an artifact. We can now begin to articulate some properties of this artifact that would help us in our search for such an artifact in our universe.
The artifact presents itself in the simulated world as an upper limit.
Now that we have some defining features of the artifact, of course it becomes clear what the artifact manifests itself as within our universe. The artifact is manifested as the speed of light.
This maximum speed is the speed of light. We don’t know what hardware is running the simulation of our universe or what properties it has, but one thing we can say now is that the memory container size for the variable space would be about 300,000 kilometers if the processor performed one operation per second.
We can see now that the speed of light meets all the criteria of a hardware artifact identified in our observation of our own computer builds. It remains the same irrespective of observer (simulated) speed, it is observed as a maximum limit, it is unexplainable by the physics of the universe, and it is absolute. The speed of light is a hardware artifact showing we live in a simulated universe.
Consciousness is an integrated (combining five senses) subjective interface between the self and the rest of the universe. The only reasonable explanation for its existence is that it is there to be an “experience”.
So here we are generating this product called consciousness that we apparently don’t have a use for, that is an experience and hence must serve as an experience. The only logical next step is to surmise that this product serves someone else.
Why The New York Times might win its copyright lawsuit against OpenAI
Daniel Jeffries wrote:
“Trying to get everyone to license training data is not going to work because that’s not what copyright is about,” Jeffries wrote. “Copyright law is about preventing people from producing exact copies or near exact copies of content and posting it for commercial gain. Period. Anyone who tells you otherwise is lying or simply does not understand how copyright works.”
The AI community is full of people who understand how models work and what they’re capable of, and who are working to improve their systems so that the outputs aren’t full of regurgitated inputs. Google won the Google Books case because it could explain both of these persuasively to judges. But the history of technology law is littered with the remains of companies that were less successful in getting judges to see things their way.
M.T. Fletcher – WHY AGENCIES ARE OBSESSED WITH PITCHING ON PROCESS INSTEAD OF TALENT
“Every presentation featured a proprietary process designed by the agency. A custom approach to identify targets, develop campaigns and optimize impact—with every step of the process powered by AI, naturally.”
“The key to these one-of-a-kind models is apparently finding the perfect combination of circles, squares, diamonds and triangles…Arrows abounded and ellipses are replacing circles as the unifying shape of choice among the more fashionable strategists.”
“The only problem is that it’s all bullshit.”
“A blind man could see the creative ideas were not developed via the agency’s so-called process, and anyone who’s ever worked at an agency knows that creativity comes from collaboration, not an assembly line.”
“And since most clients can’t differentiate between creative ideas without validation from testing, data has become the collective crutch for an industry governed by fear.”
“If a proprietary process really produced foolproof creativity, then every formulaic movie would be a blockbuster, every potboiler novel published by risk-averse editors would become a bestseller and every clichéd pickup line would work in any bar in the world.”
Generative AI Glossary
https://education.civitai.com/generative-ai-glossary/
| Term | Tags | Description |
|---|---|---|
| .ckpt | Model | “Checkpoint”, a file format created by PyTorch Lightning, a PyTorch research framework. It contains a PyTorch Lightning machine learning model used (by Stable Diffusion) to generate images. |
| .pt | Software | A machine learning model file created using PyTorch, containing algorithms used to automatically perform a task. |
| .Safetensors | Model | A file format for Checkpoint models, less susceptible to embedded malicious code. See “Pickle” |
| ADetailer | Software, Extension | A popular Automatic1111 Extension, mostly used to enhance fine face and eye detail, but can be used to re-draw hands and full characters. |
| AGI | Concept | Artificial General Intelligence (AGI), the point at which AI matches or exceeds the intelligence of humans. |
| Algorithm | Concept, Software | A series of instructions that allow a computer to learn and analyze data, learning from it, and use that learning to interpret and accomplish future tasks on its own. |
| AnimateDiff | Software, Extension | Technique which involves injecting motion into txt2img (or img2img) generations. https://animatediff.github.io/ |
| API | Software | Application Programmer Interface – a set of functions and tools which allow interaction with, or between, pieces of software. |
| Auto-GPT | Software, LLM | |
| Automatic1111 | Developer, SD User Interface | Creator of the popular Automatic1111 WebUI graphical user interface for SD. |
| Bard | Software, LLM | Google’s Chatbot, based on their LaMDA model. |
| Batch | A subset of the training data used in one iteration of model training. In inference, a group of images. | |
| Bias | Concept, LLM | In Large Language Models, errors resulting from training data; stereotypes, attributing certain characteristics to races or groups of people, etc. Bias can cause models to generate offensive and harmful content. |
| Bing | Software, LLM | Microsoft’s ChatGTP powered Chatbot. |
| CFG | Setting | Classifier Free Guidance, sometimes “Guidance Scale”. Controls how closely the image generation process follows the text prompt. |
| Checkpoint | Model | The product of training on millions of captioned images scraped from multiple sources on the Web. This file drives Stable Diffusion’s txt2img, img2img, txt2video |
| Civitai (Civitai.com) | Community Resource | Popular hosting site for all types of Generative AI resources. |
| Civitai Generator | Software, Tool | Free Stable Diffusion Image Generation Interface, available on Civitai.com. |
| Civitai Trainer | Software, Tool | LoRA Training interface, available on Civitai.com, for SDXL and 1.5 based LoRA. |
| CLIP | Software | An open source model created by OpenAI. Trained on millions of images and captions, it determines how well a particular caption describes an image. |
| Cmdr2 | Developer, SD User Interface | Creator of the popular EasyDiffusion, simple one-click install graphical user interface for SD. |
| CodeFormer | Face/Image Restoration, Model | A facial image restoration model, for fixing blurry, grainy, or disfigured faces. |
| Colab | Tool | Colaboratory, a product from Google Research, allowing execution of Python code through the browser. Particularly geared towards machine learning applications. https://colab.research.google.com/ |
| ComfyUI | SD User Interface, Software | A popular powerful modular UI for Stable Diffusion with a “workflow” type workspace. Somewhat more complex than Auto1111 WebUI https://github.com/comfyanonymous/ComfyUI |
| CompVis | Organization | Computer Vision & Learning research group at Ludwig Maximilian University of Munich. They host Stable Diffusion models on Hugging Face. |
| Conda | Application, Software | An open source package manager for many programming languages, including Python. |
| ControlNet | UI Extension | An Extension to Auto1111 WebUI allowing images to be manipulated in a number of ways. https://github.com/Mikubill/sd-webui-controlnet |
| Convergence | Concept | The point in image generation where the image no longer changes as the steps increase. |
| CUDA | Hardware, Software | Compute Unified Device Architecture, Nvdia’s parallel processing architecture. |
| DALL-E / DALL-E 2 | Organization | Deep learning image models created by OpenAI, available as a commercial image generation service. |
| Danbooru | Community Resource | English-based image board website specializing in erotic manga fan art, NSFW. |
| Danbooru Tag | Community Resource | System of keywords applied to Danbooru images describing the content within. When using Checkpoint models trained on Danbooru images, keywords can be referenced in Prompts. |
| DDIM (Sampler) | Sampler | Denoising Diffusion Implicit Models. See Samplers. |
| Deep Learning | Concept | A type of Machine Learning, where neural networks attempt to mimic the behavior of the human brain to perform tasks. |
| Deforum | UI Extension, Community Resource | A community of AI image synthesis developers, enthusiasts, and artists, producing Generative AI tools. Most commonly known for a Stable Diffusion WebUI video extension of the same name. |
| Denoising/Diffusion | Concept | The process by which random noise (see Seed) is iteratively reduced into the final image. |
| depth2img | Concept | Infers the depth of an input image (using an existing model), and then generates new images using both the text and depth information. |
| Diffusion Model (DM) | Model | A generative model, used to generate data similar to the data on which they are trained. |
| DPM adaptive (Sampler) | Sampler | Diffusion Probabilistic Model (Adaptive). See Samplers. Ignores Step Count. |
| DPM Fast (Sampler) | Sampler | Diffusion Probabilistic Model (Fast). See Samplers. |
| DPM++ 2M (Sampler) | Sampler | Diffusion Probabilistic Model – Multi-step. Produces good quality results within 15-20 Steps. |
| DPM++ 2M Karras (Sampler) | Sampler | Diffusion Probabilistic Model – Multi-step. Produces good quality results within 15-20 Steps. |
| DPM++ 2S a Karras (Sampler) | Sampler | Diffusion Probabilistic Model – Single-step. Produces good quality results within 15-20 Steps. |
| DPM++ 2Sa (Sampler) | Sampler | Diffusion Probabilistic Model – Single-step. Produces good quality results within 15-20 Steps. |
| DPM++ SDE (Sampler) | Sampler | |
| DPM++ SDE Karras (Sampler) | Sampler | |
| DPM2 (Sampler) | Sampler | |
| DPM2 a (Sampler) | Sampler | |
| DPM2 a Karras (Sampler) | Sampler | |
| DPM2 Karras (Sampler) | Sampler | |
| DreamArtist | UI Extension, Software | An extension to WebUI allowing users to create trained embeddings to direct an image towards a particular style, or figure. A PyTorch implementation of the research paper DreamArtist: Towards Controllable One-Shot Text-to-Image Generation via Contrastive Prompt-Tuning, Ziyi Dong, Pengxu Wei, Liang Lin. |
| DreamBooth | Software, Community Resource | Developed by Google Researchers, DreamBooth is a deep learning image generation model designed to fine-tune existing models (checkpoints). Can be used to create custom models based on a set of images. |
| DreamStudio | Organization, SD User Interface | A commercial web-based image generation service created by Stability AI using Stable Diffusion models. |
| Dropout (training) | Concept | A technique to prevent overfitting by randomly ignoring some images/tokens, etc. during training. |
| DyLoRA C3Lier | ||
| DyLoRA LierLa | ||
| DyLoRA Lycoris | ||
| EMA | Model | Exponential Moving Average. A full EMA Checkpoint model contains extra training data which is not required for inference (generating images). Full EMA models can be used to further train a Checkpoint. |
| Emad | Organization, Developer | Emad Mostaque, CEO and co-founder of Stability AI, one of the companies behind Stable Diffusion. |
| Embedding | Model, UI Extension | Additional file inputs to help guide the diffusion model to produce images that match the prompt. Can be a graphical style, representation of a person, or object. See Textual Inversion and Aesthetic Gradient. |
| Emergent Behavior | Concept, LLM | Unintended abilities exhibited by an AI model. |
| Entropy | Concept | A measure of randomness, or disorder. |
| Epoch | Concept | The number of times a model training process looked through a full data set of images. E.g. The 5th Epoc of a Checkpoint model looked five times through the same data set of images. |
| ESRGAN | Upscaler, Model | Enhanced Super-Resolution Generative Adversarial Networks. A technique to reconstruct a higher-resolution image from a lower-resolution image. E.g. upscaling of a 720p image into 1080p. Implemented as a tool within many Stable Diffusion interfaces. |
| Euler (Sampler) | Sampler | Named after Leonhard Euler, a numerical procedure for solving ordinary differential equations, See Samplers. |
| Euler a (Sampler) | Sampler | Ancestral version of the Euler sampler. Named after Leonhard Euler, a numerical procedure for solving ordinary differential equations, See Samplers. |
| Finetune | Concept | |
| float16 | Setting, Model, Concept | Half-Precision floating point number. |
| float32 | Setting, Model, Concept | Full-Precision floating point number. |
| Generative Adversarial Networks (GANs) | Model | A pair of AI models: one generates new data, and the other evaluates its quality. |
| Generative AI | Concept | |
| GFPGAN | Face/Image Restoration, Model | Generative Facial Prior, a facial restoration model for fixing blurry, grainy, or disfigured faces. |
| Git (GitHub) | Application, Software | Hosting service for software development, version control, bug tracking, documentation. |
| GPT-3 | Model, LLM | Generative Pre-trained Transformer 3, a language model, using machine learning to produce human-like text, based on an initial prompt. |
| GPT-4 | Model, LLM | Generative Pre-trained Transformer 4, a language model, using machine learning to produce human-like text, based on an initial prompt. A huge leap in performance and reasoning capability over GPT 3/3.5. |
| GPU | Hardware | A Graphics Processing Unit, a type of processor designed to perform quick mathematical calculations, allowing it to render images and video for display. |
| Gradio | Software | A web-browser based interface framework, specifically for Machine Learning applications. Auto1111 WebUI runs in a Gradio interface. |
| Hallucinations (LLM) | LLM, Concept | Sometimes LLM models like ChatGPT produce information that sounds plausible but is nonsensical or entirely false. This is called a Hallucination. |
| Hash (Checkpoint model) | Model, Concept | An algorithm for verifying the integrity of a file, by generating an alphanumeric string unique to the file in question. Checkpoint models are hashed, and the resulting string can be used to identify that model. |
| Heun (Sampler) | Sampler | Named after Karl Heun, a numerical procedure for solving ordinary differential equations. See Samplers. |
| Hugging Face | Organization | A community/data science platform providing tools to build, train, and deploy machine learning models. |
| Hypernetwork (Hypernet) | Model | A method to guide a Checkpoint model towards a specific theme, object, or character based on its’ own content (no external data required). |
| img2img | Concept | Process to generate new images based on an input image, and txt2img prompt. |
| Inpainting | Concept | The practice of removing or replacing objects in an image based on a painted mask. |
| Kohya | Software | Can refer to Kohya-ss scripts for LoRA/finetuning (https://github.com/kohya-ss/sd-scripts) or the Windows GUI implementation of those scripts (https://github.com/bmaltais/kohya_ss) |
| LAION | Organization | A non-profit organization, providing data sets, tools, and models, for machine learning research. |
| LAION-5B | Model | A large-scale dataset for research purposes consisting of 5.85 billion CLIP-filtered image-text pairs. |
| Lanczos | Upscaler, Model | An interpolation method used to compute new values for sampled data. In this case, used to upscale images. Named after creator, Cornelius Lanczos. |
| Large Language Model (LLM) | LLM, Model | A type of Neural Network that learns to write and converse with users. Trained on billions of pieces of text, LLMs excel at producing coherent sentences and replying to prompts in the correct context. They can perform tasks such as re-writing and summarizing text, chatting about various topics, and performing research. |
| Latent Diffusion | Model | A type of diffusion model that contains compressed image representations instead of the actual images. This type of model allows the storage of a large amount of data that can be used by encoders to reconstruct images from textual or image inputs. |
| Latent Mirroring | Concept, UI Extension | Applies mirroring to the latent images mid-generation to produce anything from subtly balanced compositions to perfect reflections. |
| Latent Space | Concept | The information-dense space where the diffusion model’s image representation, attention, and transformation are merged and form the initial noise for the diffusion process. |
| LDSR | Upscaler | Latent Diffusion Super Resolution upscaling. A method to increase the dimensions/quality of images. |
| Lexica | Community Resource | Lexica.art, a search engine for stable diffusion art and prompts. |
| LlamaIndex (GPT Index) | Software, LLM | https://github.com/jerryjliu/llama_index – Allows the connection of text data to an LLM via a generated “index”. |
| LLM | LLM, Model | A type of Neural Network that learns to write and converse with users. Trained on billions of pieces of text, LLMs excel at producing coherent sentences and replying to prompts in the correct context. They can perform tasks such as re-writing and summarizing text, chatting about various topics, and performing research. |
| LMS (Sampler) | Sampler | |
| LMS Karras (Sampler) | Sampler | |
| LoCON | ||
| LoHa | ||
| LoKR | ||
| LoRA | Model, Concept | Low-Rank Adaptation, a method of training for SD, much like Textual Inversion. Can capture styles and subjects, producing better results in a shorter time, with smaller output files, than traditional finetuning. |
| LoRA C3Lier | ||
| LoRA LierLa | ||
| Loss (function) | Concept | A measure of how well an AI model’s outputs match the desired outputs. |
| Merge (Checkpoint) | Model | A process by which Checkpoint models are combined (merged) to form new models. Depending on the merge method (see Weighted Sum, Sigmoid) and multiplier, the merged model will retain varying characteristics of its’ constituent models. |
| Metadata | Concept, Software | Metadata is data that describes data. In the context of Stable Diffusion, metadata is often used to describe the Prompt, Sampler settings, CFG, steps, etc. which are used to define an image, and stored in a .png header. |
| MidJourney | Organization, SD User Interface | A commercial web-based image generation service, similar to DALL-E, or the free, open source, Stable Diffusion. |
| Model | Model | Alternative term for Checkpoint |
| Motion Module | Software | Used by AnimateDiff to inject motion into txt2img (or img2img) generations. |
| Multimodal AI | Concept | AI that can process multiple types of inputs, including text, images, video or speech. |
| Negative Prompt | Setting, Concept | Keywords which tell a Stable Diffusion prompt what we don’t want to see, in the generated image. |
| Neural Network | Concept, Software | Mathematical systems that act like a human brain, with layers of artificial “neurons” helping find connections between data. |
| Notebook | Community Resource, Software | See Colab. A Jupyter notebook service providing access, free of charge, to computing resources including GPUs. |
| NovelAI (NAI) | Organization | A paid, subscription based AI-assisted story (text) writing service. Also has a txt2img model, which was leaked and is now incorporated into many Stable Diffusion models. |
| Olivio (Sarikas) | Community Resource | Olivio produces wonderful SD content on YouTube (https://www.youtube.com/@OlivioSarikas) – one of the best SD news YouTubers out there! |
| OpenAI | Organization | AI research laboratory consisting of the for-profit corporation OpenAI LP and the non-profit OpenAI Inc. |
| OpenPose | Model, Software | A method for extracting a “skeleton” from an image of a person, allowing poses to be transferred from one image to another. Used by ControlNet. |
| Outpainting | Concept | The practice of extending the outer border of an image, into blank canvas space, while maintaining the style and content of the image. |
| Overfitting | Concept | When an AI model learns the training data too well and performs poorly on unseen data. |
| Parameters (LLMs) | Concept, Software, LLM | Numerical points across a Large Language Model’s training data. Parameters dictate how proficient the model is at its tasks. E.g. a 6B (Billion) Parameter model will likely perform less well than a 13B Parameter model. |
| Pickle | Concept, Software | Community slang term for potentially malicious code hidden within models and embeddings. To be “pickled” is to have unwanted code execute on your machine (be hacked). |
| PLMS (Sampler) | Sampler | Pre-Trained Language Models. See Samplers. |
| Prompt | Concept | Text input to Stable Diffusion describing the particulars of the image you would like output. |
| Pruned/Pruning | Model | A method of optimizing a Checkpoint model to increase the speed of inference (prompt generation), file size, and VRAM cost. |
| Python | Application, Software | A popular, high-level, general purpose coding language. |
| PyTorch | Application, Software | An open source machine learning library, created by META. |
| Real-ESRGAN | Upscaler | An image restoration method. |
| Refiner | Model | Part of SDXL’s two-stage pipeline – the Refiner further enhances detail from the base model. |
| SadTalker | UI Extension | https://github.com/OpenTalker/SadTalker A framework for facial animation/lip synching based upon an audio input. |
| Samplers | Sampler | Mathematical functions providing different ways of solving differential equations. Each will produce a slightly (or significantly) different image result from the random latent noise generation. |
| Sampling Steps | Sampler, Concept | The number of how many steps to spend generating (diffusing) your image. |
| SD 1.4 | Model | A latent txt2img model, the default model for SD at release. Fine-tuned on 225k steps at resolution 512×512 on laion-aesthetics v2 data set. |
| SD 1.5 | Model | A latent txt2img model, updated version of 1.4, fine-tuned on 595k steps at resolution 512×512 on laion-aesthetics v2 data set. |
| SD UI | Application, Software | Colloquial term for Cmdr2’s popular graphical interface for Stable Diffusion prompting. |
| SD.Next | Software | See Vlad, Vladmandic Fork of Auto1111 WebUI. |
| SDXL 0.9 | Model | Stability AI’s latest (March 2023) Stable Diffusion Model. Will become SDXL 1.0 and be released ~July 2023. |
| Seed | Concept | A pseudo-random number used to initialize the generation of random noise, from which the final image is built. Seeds can be saved and used along with other settings to recreate a particular image. |
| Shoggoth Tongue | Concept, LLM | A humorous allusion to the language of the fictional monsters in the Cthulhu Mythos, “Shoggoth Tongue” is the name given to advanced ChatGPT commands which are particularly arcane and difficult to understand, but allow ChatGPT to perform advanced actions outside of the intended operation of the system. |
| Sigmoid (Interpolation Method) | Model, Concept | A method for merging Checkpoint Models based on a Sigmoid function – a mathematical function producing an “S” shaped curve. |
| Stability AI | Organization | AI technology company co-founded by Emad Mostaque. One of the companies behind Stable Diffusion. |
| Stable Diffusion (SD) | Application, Software | A deep learning, text-to-image model released in 2022. It is primarily used to generate detailed images based on provided text descriptions. |
| SwinIR | Face/Image Restoration, Model | An image restoration transform, aiming to restore high quality images from low quality images. |
| Tensor | Software | A container, in which multi-dimensional data can be stored. |
| Tensor Core | Hardware | Processing unit technology developed by Nvidia, designed to carry out matrix multiplication, an arithmetic operation. |
| Textual Inversion | Model, Concept, UI Extension | A technique for capturing concepts from a small number of sample images in a way that can influence txt2img results towards a particular face, or object. |
| Token | Concept | A token is roughly a word, a punctuation, or a Unicode character in a prompt. |
| Tokenizer | Concept, Model | The process/model through which text prompts are turned into tokens, for processing. |
| Torch 2.0 | Software | The latest (March 2023) PyTorch release. |
| Training | Concept | The process of teaching an AI model by feeding it data and adjusting its parameters. |
| Training Data | Model | A set of many images used to “train” a Stable Diffusion model, or embedding. |
| Training Data | Concept, LLM, Model | The data sets uses to help AI models learn; can be text, images, code, or other data, depending on the type of model to be trained. |
| Turing Test | Concept | Named after mathematician Alan Turing, a test of a machine’s ability to behave like a human. The machine passes if a human can’t distinguish the machine’s response from another human. |
| txt2img | Concept, Model | Model/method of image generation via entry of text input. |
| txt2video | Concept, Model | Model/method of video generation via entry of text input. |
| Underfitting | When an AI model cannot capture the underlying pattern of the data due to incomplete training. | |
| UniPC (Sampler) | Sampler | A recently released (3/2023) sampler based upon https://huggingface.co/docs/diffusers/api/schedulers/unipc |
| Upscale | Upscaler, Concept | The process of converting low resolution media (images or video) into higher resolution media. |
| VAE | Model | Variational Autoencoder. A .vae.pt file which accompanies a Checkpoint model and provides additional detail improvements. Not all Checkpoints have an associated vae file, and some vae files are generic and can be used to improve any Checkpoint model. |
| Vector (Prompt Word) | Concept | An attempt to mathematically represent the meaning of a word, for processing in Stable Diffusion. |
| Venv | Software | A Python “Virtual Environment” which allows multiple instances of python packages to run, independently, on the same PC. |
| Vicuna | LLM, Software, Model | https://vicuna.lmsys.org/ An Open-Source Chatbot model founded by students and faculty from UC Berkeley in collaboration with UCSD and CMU. |
| Vladmandic | Software, SD User Interface | A popular “Fork” of Auto1111 WebUI, with its own feature-set. https://github.com/vladmandic/automatic |
| VRAM | Hardware | Video random access memory. Dedicated Graphics Card (GPU) memory used to store pixels, and other graphical processing data, for display. |
| Waifu Diffusion | Model | A popular text-to-image model, trained on high quality anime images, which produces anime style image outputs. Originally produced for SD 1.4, now has an SDXL version. |
| WebUI | Application, Software, SD User Interface | Colloquial term for Automatic1111’s WebUI – a popular graphical interface for Stable Diffusion prompting. |
| Weighted Sum (Interpolation Method) | Concept | A method of Checkpoint merging using the formula Result = ( A * (1 – M) ) + ( B * M ) . |
| Weights | Model | Alternative term for Checkpoint |
| Wildcards | Concept | Text files containing terms (clothing types, cities, weather conditions, etc.) which can be automatically input into image prompts, for a huge variety of dynamic images. |
| xformers | UI Extension, Software | Optional library to speed up image generation. Superseded somewhat by new options implemented by Torch 2.0 |
| yaml | Software, UI Extension, Model | A human-readable data-serialization programming language commonly used for configuration files. Yaml files accompany Checkpoint models, and provide Stable Diffusion with additional information about the Checkpoint. |
The philosophy of Charles Schulz, the creator of the ‘Peanuts’ comic strip
- Name the five wealthiest people in the world.
- Name the last five Heisman trophy winners.
- Name the last five winners of the Miss America pageant.
- Name ten people who have won the Nobel or Pulitzer Prize.
- Name the last half dozen Academy Award winners for best actor and actress.
- Name the last decade’s worth of World Series winners.
How did you do?
The point is, none of us remember the headliners of yesterday.
These are no second-rate achievers.
They are the best in their fields.
But the applause dies.
Awards tarnish …
Achievements are forgotten.
Accolades and certificates are buried with their owners.
Here’s another quiz. See how you do on this one:
- List a few teachers who aided your journey through school.
- Name three friends who have helped you through a difficult time.
- Name five people who have taught you something worthwhile.
- Think of a few people who have made you feel appreciated and special.
- Think of five people you enjoy spending time with.
Easier?
The God of War Texture Optimization Algorithm: Mip Flooding
“delve into an algorithm developed by Sean Feeley, a Senior Staff Environment Tech Artist that is part of the creative minds at Santa Monica Studio. This algorithm, originally designed to address edge inaccuracy on foliage, has the potential to revolutionize the way we approach texture optimization in the gaming industry. ”
TurboSquid move towards supporting AI against its own policies
https://www.turbosquid.com/ai-3d-generator
The AI is being trained using a mix of Shutterstock 2D imagery and 3D models drawn from the TurboSquid marketplace. However, it’s only being trained on models that artists have approved for this use.
People cannot generate a model and then immediately sell it. However, a generated 3D model can be used as a starting point for further customization, which could then be sold on the TurboSquid marketplace. However, models created using our generative 3D tool—and their derivatives—can only be sold on the TurboSquid marketplace.
TurboSquid does not accept AI-generated content from our artists
As AI-powered tools become more accessible, it is important for us to address the impact AI has on our artist community as it relates to content made licensable on TurboSquid. TurboSquid, in line with its parent company Shutterstock, is taking an ethically responsible approach to AI on its platforms. We want to ensure that artists are properly compensated for their contributions to AI projects while supporting customers with the protections and coverage issued through the TurboSquid license.
In order to ensure that customers are protected, that intellectual property is not misused, and that artists’ are compensated for their work, TurboSquid will not accept content uploaded and sold on our marketplace that is generated by AI. Per our Publisher Agreement, artists must have proven IP ownership of all content that is submitted. AI-generated content is produced using machine learning models that are trained using many other creative assets. As a result, we cannot accept content generated by AI because its authorship cannot be attributed to an individual person, and we would be unable to ensure that all artists who were involved in the generation of that content are compensated.
How to View Apple’s Spatial Videos
https://blog.frame.io/2024/02/01/how-to-capture-and-view-vision-pro-spatial-video/
Apple’s Immersive Videos format is a special container for 3D or “spatial” video. You can capture spatial video to this format either by using the Vision Pro as a head-mounted camera, or with an iPhone 15 Pro or 15 Pro Max. The headset offers better capture because its cameras are more optimized for 3D, resulting in higher resolution and improved depth effects.
While the iPhone wasn’t designed specifically as a 3D camera, it can use its primary and ultrawide cameras in landscape orientation simultaneously, allowing it to capture spatial video—as long as you hold it horizontally. Computational photography is used to compensate for the lens differences, and the output is two separate 1080p, 30fps videos that capture a 180-degree field of view.
These spatial videos are stored using the MV-HEVC (Multi-View High-Efficiency Video Coding) format, which uses H.265 compression to crunch this down to approximately 130MB per minute, including spatial audio. Unlike conventional stereoscopic formats—which combine the two views into a flattened video file that’s either side-by-side or top/bottom—these spatial videos are stored as discrete tracks within the file container.
Spatialify is an iOS app designed to view and convert various 3D formats. It also works well on Mac OS, as long as your Mac has an Apple Silicon CPU. And it supports MV-HEVC, so you’ll be all set. It’s just $4.99, a genuine bargain considering what it does. Find Spatialify here.
