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Google Street View Hyperlapse
All Google Street View imagery captured using hyperlapse.tllabs.io. Source code available at github.com/TeehanLax/Hyperlapse.js.
Read the full story: teehanlax.com/labs/hyperlapse/
Google advanced search
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Microsoft Working on ‘Far Larger’ In-House AI Model
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.
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. |
DiffusionLight: HDRI Light Probes for Free by Painting a Chrome Ball
https://diffusionlight.github.io/
https://github.com/DiffusionLight/DiffusionLight
https://github.com/DiffusionLight/DiffusionLight?tab=MIT-1-ov-file#readme
https://colab.research.google.com/drive/15pC4qb9mEtRYsW3utXkk-jnaeVxUy-0S
“a simple yet effective technique to estimate lighting in a single input image. Current techniques rely heavily on HDR panorama datasets to train neural networks to regress an input with limited field-of-view to a full environment map. However, these approaches often struggle with real-world, uncontrolled settings due to the limited diversity and size of their datasets. To address this problem, we leverage diffusion models trained on billions of standard images to render a chrome ball into the input image. Despite its simplicity, this task remains challenging: the diffusion models often insert incorrect or inconsistent objects and cannot readily generate images in HDR format. Our research uncovers a surprising relationship between the appearance of chrome balls and the initial diffusion noise map, which we utilize to consistently generate high-quality chrome balls. We further fine-tune an LDR difusion model (Stable Diffusion XL) with LoRA, enabling it to perform exposure bracketing for HDR light estimation. Our method produces convincing light estimates across diverse settings and demonstrates superior generalization to in-the-wild scenarios.”
How OpenAI so royally screwed up the Sam Altman firing and joining Microsoft
https://edition.cnn.com/2023/11/19/tech/sam-altman-open-ai-firing-board/index.html
https://www.cnn.com/2023/11/18/tech/openai-sam-altman-shakeup-what-happened/index.html
https://edition.cnn.com/2023/11/20/tech/sam-altman-joins-microsoft/index.html
A company’s board of directors has an obligation, first and foremost, to its shareholders. OpenAI’s most important shareholder is Microsoft, the company that gave Altman & Co. $13 billion to help Bing, Office, Windows and Azure leapfrog Google and stay ahead of Amazon, IBM and other AI wannabes.
So a day later, the board reportedly asked for a mulligan and tried to woo Altman back. It was a shocking turn of events and an embarrassing self-own by a company that its widely regarded as the most promising producer of the most exciting new technology.
The board angered a powerful ally and could be forever changed because of the way it handled Altman’s ouster. It could end up with Altman back at the helm, a for-profit company on its nonprofit board – and a massive culture shift at OpenAI.
https://www.bbc.com/news/technology-67474879
But Microsoft, OpenAI’s biggest investor, has decided not to take a chance on Mr Altman taking this tech elsewhere. He will be joining the Seattle-based tech giant, it has been announced, to lead a yet-to-be-created AI research team. His co-founder Greg Brockman goes with him, and judging from the number of staff members posting on X today, it looks like he’ll be taking some of OpenAI’s top talent too.
Many OpenAI staff members are sharing the same post on X. It reads: “OpenAI is nothing without its people”.
Is that a warning to Mr Shear that he might have some hiring to do? A BBC colleague outside OpenAI’s headquarters just told me at 0930 in San Francisco, there were no signs of people arriving for work.
https://edition.cnn.com/2023/11/20/tech/openai-employees-quit-mira-murati-sam-altman/index.html
“Your actions have made it obvious that you are incapable of overseeing OpenAI,” wrote the employees. “We are unable to work for or with people that lack competence, judgement and care for our mission and employees.”
The employees also warned that they would “imminently” follow Altman to Microsoft unless the board resigns and reinstates Altman and Greg Brockman, the former OpenAI president who was also removed by the board on Friday.
Unity Presents New “Runtime Fees” Based on Game Installs and Revenue
https://80.lv/articles/unity-presents-new-fees-based-on-game-installs-and-revenue/
The new program is called the Unity Runtime Fee and the main principle is based on how often users install games. Unity thinks “an initial install-based fee allows creators to keep the ongoing financial gains from player engagement, unlike a revenue share”.
This is bound to kill all developers who count on free downloads but profitable venues of income like in-app purchase. Which count for a vast majority of the 30% of the market that Unity holds onto.
The extra bill will be estimated by Unity based on non-specific data.
Unity does not have a ‘known’ way to track installs. Likely due to privacy laws. Thus they will need to ‘estimate’ installs and bill clients based on that. … …. Data which is aggregated with no identifying features isn’t really prevented. Unity’s claim that they can’t distinguish between an install and reinstall or even a paid versus pirated copy actually reinforces the idea that they aren’t using any identifying information, so it would be compliant to privacy laws. … Assumption is that they will get some data from distributors like AppStore, GooglePlay, Valve, Sony, Microsoft, etc… and estimate from there.
“It hurts because we didn’t agree to this. We used the engine because you pay up front and then ship your product. We weren’t told this was going to happen. We weren’t warned. We weren’t consulted,” explained the Facepunch Studios founder. “We have spent 10 years making Rust on Unity’s engine. We’ve paid them every year. And now they changed the rules.”
“It’s our fault. All of our faults. We sleepwalked into it. We had a ton of warnings,” they added. “We should have been pressing the eject button when Unity IPO’d in 2020. Every single thing they’ve done since then has been the exact opposite of what was good for the engine.
Laurence Van Elegem – The era of gigantic AI models like GPT-4 is coming to an end
https://www.linkedin.com/feed/update/urn:li:activity:7061987804548870144
Sam Altman, CEO of OpenAI, dropped a 💣 at a recent MIT event, declaring that the era of gigantic AI models like GPT-4 is coming to an end. He believes that future progress in AI needs new ideas, not just bigger models.
So why is that revolutionary? Well, this is how OpenAI’s LLMs (the models that ‘feed’ chatbots like ChatGPT & Google Bard) grew exponentially over the years:
➡️GPT-2 (2019): 1.5 billion parameters
➡️GPT-3 (2020): 175 billion parameters
➡️GPT-4: (2023): amount undisclosed – but likely trillions of parameters
That kind of parameter growth is no longer tenable, feels Altman.
Why?:
➡️RETURNS: scaling up model size comes with diminishing returns.
➡️PHYSICAL LIMITS: there’s a limit to how many & how quickly data centers can be built.
➡️COST: ChatGPT cost over over 100 million dollars to develop.
What is he NOT saying? That access to data is becoming damned hard & expensive. So if you have a model that keeps needing more data to become better, that’s a problem.
Why is it becoming harder and more expensive to access data?
🎨Copyright conundrums: Getty Images, individual artists like Sarah Andersen, Kelly McKernan & Karloa Otiz are suing AI companies over unauthorized use of their content. Universal Music asked Spotify & Apple Music to stop AI companies from accessing their songs for training.
🔐Privacy matters & regulation: Italy banned ChatGPT over privacy concerns (now back after changes). Germany, France, Ireland, Canada, and Spain remain suspicious. Samsung even warned employees not to use AI tools like ChatGPT for security reasons.
💸Data monetization: Twitter, Reddit, Stack Overflow & others want AI companies to pay up for training on their data. Contrary to most artists, Grimes is allowing anyone to use her voice for AI-generated songs … for a 50% profit share.
🕸️Web3’s impact: If Web3 fulfills its promise, users could store data in personal vaults or cryptocurrency wallets, making it harder for LLMs to access the data they crave.
🌎Geopolitics: it’s increasingly difficult for data to cross country borders. Just think about China and TikTok.
😷Data contamination: We have this huge amount of ‘new’ – and sometimes hallucinated – data that is being generated by generative AI chatbots. What will happen if we feed that data back into their LLMs?
No wonder that people like Sam Altman are looking for ways to make the models better without having to use more data. If you want to know more, check our brand new Radar podcast episode (link in the comments), where I talked about this & more with Steven Van Belleghem, Peter Hinssen, Pascal Coppens & Julie Vens – De Vos. We also discussed Twitter, TikTok, Walmart, Amazon, Schmidt Futures, our Never Normal Tour with Mediafin in New York (link in the comments), the human energy crisis, Apple’s new high-yield savings account, the return of China, BYD, AI investment strategies, the power of proximity, the end of Buzzfeed news & much more.
A short 170 year history of Neural Radiance Fields (NeRF), Holograms, and Light Fields
https://neuralradiancefields.io/history-of-neural-radiance-fields/
“Lightfield and hologram capture started with a big theoretical idea 115 years ago and we have struggled to make them viable ever since. Neural Radiance fields aka NeRF along with gaming computers now for the first time provide a promising easy and low cost way for everybody to capture and display lightfields.”
“Neural Radiance fields (NeRF) recently had its third birthday but the technology is just the latest answer to a question people have been chasing since the 1860s: How do you capture and recreate space (from images)?”
“The plenoptic function measures physical light properties at every point in space and it describes how light transport occurs throughout a 3D volume.”
Google project Starline the latest in real time and compression image to 3D technology
mind-blowing ChatGPT extensions to use it anywhere
https://medium.com/geekculture/6-chatgpt-mind-blowing-extensions-to-use-it-anywhere-db6638640ec7
- Use ChatGPT anywhere — Google Chrome Extension https://github.com/gragland/chatgpt-chrome-extension
- Combining ChatGPT with search engines https://github.com/wong2/chatgpt-google-extension
- Using voice commands with ChatGTP https://chrome.google.com/webstore/detail/promptheus-converse-with/eipjdkbchadnamipponehljdnflolfki
- Integrating ChatGPT in Telegram and Whatsapp https://github.com/altryne/chatGPT-telegram-bot/
- Integrating ChatGPT in Google Docs or Microsoft Word https://github.com/cesarhuret/docGPT
- Save everything you have generated in ChatGPT https://github.com/liady/ChatGPT-pdf
- ChatGPT Writer: It uses ChatGPT/ATLAS to generate emails or replies based on your prompt https://chrome.google.com/webstore/detail/chatgpt-writer-write-mail/pdnenlnelpdomajfejgapbdpmjkfpjkp/related
- WebChatGPT gives you relevant results from the web https://chrome.google.com/webstore/detail/webchatgpt-chatgpt-with-i/lpfemeioodjbpieminkklglpmhlngfcn
- YouTube Summary with ChatGPT/ATLAS https://chrome.google.com/webstore/detail/youtube-summary-with-chat/nmmicjeknamkfloonkhhcjmomieiodli/related
- TweetGPT: It uses ChatGPT/ATLAS to write your tweets, reply, comment https://github.com/yaroslav-n/tweetGPT
What is the Light Field?
http://lightfield-forum.com/what-is-the-lightfield/
The light field consists of the total of all light rays in 3D space, flowing through every point and in every direction.
How to Record a Light Field
- a single, robotically controlled camera
- a rotating arc of cameras
- an array of cameras or camera modules
- a single camera or camera lens fitted with a microlens array
Remote working pros and cons
www.leforttalentgroup.com/business-blog/is-the-genie-out-forever
Cons of remote working:
- 1-Prefer 2 distinct locations in life — 1 for work, 1 for everything else
- 2-Being able to manage the group of employees in one location is preferable — Meetings, training, management of teams and personalities has been easier.
- 3-Confidentiality and Security — depending on the nature of the business, being able to lessen liabilities by containing the work location
- 4-Social community — Many fully enjoy the traditional work community and build life long connections
- 5-Love — A quick Google search shows various sources that cite anywhere from 20-33 percent of people met their spouse through work. What will those stats look like in a year or two from now?
- 6-Road Warriors with great sound systems in their cars — Some enjoy the commute to unwind after work cranking tunes or catch up with friends and family waiting for the gridlock to ease. Others to continue working from the car.
Pros of remote working:
- 1-The overhead costs — Keeping large commercial real estate holdings and related maintenance costs
- 2-Killer commutes — 5-20 hours/week per employee in lost time now potentially used for other purposes
- 3-Daily Daycare Scramble — Racing to drop them off or pick them up each day
- 4-Environmentally, a lower carbon footprint — Less traffic, less pollution
- 5-Quality Family time — Many parents are spending more time with their growing children
Some useful tips about working online:
- Clarify and focus on priorities.
- Define and manage expectations more explicitly than normal (give context to everything)
- Log all your working hours.
- Learn about and respect people’s boundaries.
- Pay attention to people’s verbal and physical cues.
- Pay attention to both people’s emotional, hidden and factual cues.
- Be wary about anticipating, judging, rationalizing, competing, defending, rebutting…
