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LifeArchitect.ai is a leading platform in the field of artificial intelligence (AI), offering a wealth of insights, papers, articles, and videos related to post-2020 AI advancements. Dr. Alan D. Thompson, a renowned expert in AI, focuses on enhancing human intelligence through AI technologies. The platform serves major AI labs, government bodies, research institutes, and individuals interested in the AI revolution, providing comprehensive analyses, reports, and retrospectives on AI progress and future trends.

Lilac is an AI tool designed to enhance data quality and exploration for AI applications. It offers features such as data search, quantification, editing, clustering, semantic search, field comparison, and fuzzy-concept search. Lilac enables users to accelerate dataset computations and transformations, making it a valuable asset for data scientists and AI practitioners. The tool is trusted by Alignment Lab and is recommended for working with LLM datasets.

ClearSeas.AI is an AI-powered market research visualization dashboard that leverages advanced AI technologies to interpret complex datasets, delivering intuitive and visually compelling insights. The platform offers services such as expert survey design, efficient survey deployment, B2B panel access, and full-service project management. ClearSeas.AI prioritizes data security by employing stringent protocols, advanced encryption technologies, and document protection measures. The platform does not use customer data for AI training, ensuring data privacy and confidentiality. ClearSeas.AI aims to illuminate insights with the power of AI, empowering users to stay ahead of market trends and make informed strategic decisions.

This AI-powered age detection tool analyzes your photo to estimate how old you look. It utilizes advanced artificial intelligence technology to assess facial characteristics such as wrinkles, skin texture, and facial features, comparing them against a vast dataset to provide an approximation of your age. The tool is free to use and ensures privacy by automatically deleting uploaded photos after analysis.

Zelma is an AI-powered research assistant that enables users to find, graph, and understand U.S. school testing data using plain English. It allows users to search student test data by school district, demographics, grade, and more, and presents the data with graphs, tables, and descriptions. Zelma aims to make education data easily accessible and understandable for everyone.

Phenaki is a model capable of generating realistic videos from a sequence of textual prompts. It is particularly challenging to generate videos from text due to the computational cost, limited quantities of high-quality text-video data, and variable length of videos. To address these issues, Phenaki introduces a new causal model for learning video representation, which compresses the video to a small representation of discrete tokens. This tokenizer uses causal attention in time, which allows it to work with variable-length videos. To generate video tokens from text, Phenaki uses a bidirectional masked transformer conditioned on pre-computed text tokens. The generated video tokens are subsequently de-tokenized to create the actual video. To address data issues, Phenaki demonstrates how joint training on a large corpus of image-text pairs as well as a smaller number of video-text examples can result in generalization beyond what is available in the video datasets. Compared to previous video generation methods, Phenaki can generate arbitrarily long videos conditioned on a sequence of prompts (i.e., time-variable text or a story) in an open domain. To the best of our knowledge, this is the first time a paper studies generating videos from time-variable prompts. In addition, the proposed video encoder-decoder outperforms all per-frame baselines currently used in the literature in terms of spatio-temporal quality and the number of tokens per video.

OpenNN is an open-source neural networks library for machine learning that solves real-world applications in energy, marketing, health, and more. It offers sophisticated algorithms for regression, classification, forecasting, and association tasks. OpenNN provides higher capacity for managing bigger data sets and faster training compared to TensorFlow and PyTorch. It is being developed by Artelnics, a consulting company specialized in artificial intelligence and big data. Neural Designer, a software tool developed from OpenNN, helps build neural network models without programming.

DecorAI.xyz is an AI-driven interior design tool that allows users to generate dream rooms using artificial intelligence. By simply taking a picture of a room, users can see how it looks in different themes and receive personalized design suggestions. With exceptional training on a massive dataset of 160 million design samples, DecorAI optimizes spatial layouts, provides cost-effective design solutions, and saves time compared to traditional interior design methods. The tool caters to homeowners, renters, and small businesses looking to redesign their spaces without the need for an expensive interior designer.

KAYAK is a comprehensive travel search engine that allows users to search for flights, hotels, and rental cars from hundreds of travel sites. The platform offers a wide range of features to help users plan their trips efficiently and find the best travel deals. With KAYAK, users can explore flight options, track flights, access travel tips, and plan their travel itineraries. The website compares prices from various sources to provide users with a convenient way to book their travel arrangements.

Contentable.ai is a platform for comparing multiple AI models, rapidly moving from prototyping to production, and management of your custom AI solutions across multiple vendors. It allows users to test multiple AI models in seconds, compare models side-by-side across top AI providers, collaborate on AI models with their team seamlessly, design complex AI workflows without coding, and pay as they go.

Sofon is a knowledge aggregation and curation platform that provides users with personalized insights on topics they care about. It aggregates and curates knowledge shared across 1,000+ articles, podcasts, and books, delivering a personalized stream of ideas to users. Sofon uses AI to compare ideas across hundreds of people on any question, saving users thousands of hours of curation. Users can indicate the people they want to learn from, and Sofon will curate insights across all their knowledge. Users can receive an idealetter, which is a unique combination of ideas across all the people they've selected around a common theme, delivered at an interval of their choice.

LLM Clash is a web-based application that allows users to compare the outputs of different large language models (LLMs) on a given task. Users can input a prompt and select which LLMs they want to compare. The application will then display the outputs of the LLMs side-by-side, allowing users to compare their strengths and weaknesses.

Prompt Octopus is a free tool that allows you to compare multiple prompts side-by-side. You can add as many prompts as you need and view the responses in real-time. This can be helpful for fine-tuning your prompts and getting the best possible results from your AI model.

Gemini is a multi-modal AI model, developed by Google. It is designed to understand and generate human language, and can be used for a variety of tasks, including question answering, translation, and dialogue generation. ChatGPT is a large language model, developed by OpenAI. It is also designed to understand and generate human language, and can be used for a variety of tasks, including question answering, translation, and dialogue generation.

LLM Price Check is an AI tool designed to compare and calculate the latest prices for Large Language Models (LLM) APIs from leading providers such as OpenAI, Anthropic, Google, and more. Users can use the streamlined tool to optimize their AI budget efficiently by comparing pricing, sorting by various parameters, and searching for specific models. The tool provides a comprehensive overview of pricing information to help users make informed decisions when selecting an LLM API provider.

ChatPlayground AI is a versatile platform that allows users to compare multiple AI chatbots to obtain the best responses. With 14+ AI apps and features available, users can achieve better AI answers 73% of the time. The platform offers a comprehensive prompt library, real-time web search capabilities, image generation, history recall, document upload and analysis, and multilingual support. It caters to developers, data scientists, students, researchers, content creators, writers, and AI enthusiasts. Testimonials from users highlight the efficiency and creativity-enhancing benefits of using ChatPlayground AI.

thisorthis.ai is an AI tool that allows users to compare generative AI models and AI model responses. It helps users analyze and evaluate different AI models to make informed decisions. The tool requires JavaScript to be enabled for optimal functionality.

Ai Tool Hunt is a comprehensive directory of free AI tools, software, and websites. It provides users with a curated list of the best AI resources available online, empowering them to enhance their digital experiences and leverage the latest advancements in artificial intelligence. With Ai Tool Hunt, users can discover powerful AI tools for various tasks, including content creation, data analysis, image editing, language learning, and more. The platform offers detailed descriptions, user ratings, and easy access to these tools, making it a valuable resource for individuals and businesses seeking to integrate AI into their workflows.

Staypia is an AI-powered hotel reservation platform that offers maximum discounts to its users. The platform is evolving into Gen OTA Season 2, powered by LLM technology, to provide smarter and more personalized travel experiences. Staypia allows users to compare prices of over 3.16M hotels worldwide, offering additional discounts to its members through AI-driven member-exclusive deals. The platform also features popular destinations, trending hotels, and a 'Hotels near me' search option for convenient booking. Staypia aims to provide a seamless and cost-effective hotel reservation experience for travelers around the world.

Everypixel.com is a website that provides services related to image analysis and enhancement. Users can upload images to the platform for evaluation and receive insights on image quality, aesthetics, and potential improvements. The site aims to help individuals and businesses enhance their visual content through AI-powered algorithms and tools. Everypixel.com ensures a secure connection for users and leverages technologies like JavaScript and cookies to optimize the user experience.

Athina is an open-source library designed to help engineers improve the reliability and performance of Large Language Models (LLMs) through eval-driven development. It offers plug-and-play preset evals for catching and preventing bad outputs, measuring model performance, running experiments, A/B testing models, detecting regressions, and monitoring production data. Athina provides a solution to the flaws in current LLM developer workflows by offering rapid experimentation, customizable evaluators, integrated dashboard, consistent metrics, historical record tracking, and easy setup. It includes preset evaluators for RAG applications and summarization accuracy, as well as the ability to write custom evals. Athina's evals can run on both development and production environments, providing consistent metrics and removing the need for manual infrastructure setup.

QANTA is a system and dataset for question answering tasks. It provides a script to download datasets, preprocesses questions, and matches them with Wikipedia pages. The system includes various datasets, training, dev, and test data in JSON and SQLite formats. Dependencies include Python 3.6, `click`, and NLTK models. Elastic Search 5.6 is needed for the Guesser component. Configuration is managed through environment variables and YAML files. QANTA supports multiple guesser implementations that can be enabled/disabled. Running QANTA involves using `cli.py` and Luigi pipelines. The system accesses raw Wikipedia dumps for data processing. The QANTA ID numbering scheme categorizes datasets based on events and competitions.

ReproModel is an open-source toolbox designed to boost AI research efficiency by enabling researchers to reproduce, compare, train, and test AI models faster. It provides standardized models, dataloaders, and processing procedures, allowing researchers to focus on new datasets and model development. With a no-code solution, users can access benchmark and SOTA models and datasets, utilize training visualizations, extract code for publication, and leverage an LLM-powered automated methodology description writer. The toolbox helps researchers modularize development, compare pipeline performance reproducibly, and reduce time for model development, computation, and writing. Future versions aim to facilitate building upon state-of-the-art research by loading previously published study IDs with verified code, experiments, and results stored in the system.

Weights & Biases (W&B) is a platform that helps users build better machine learning models faster by tracking and visualizing all components of the machine learning pipeline, from datasets to production models. It offers tools for tracking, debugging, evaluating, and monitoring machine learning applications. W&B provides integrations with popular frameworks like PyTorch, TensorFlow/Keras, Hugging Face Transformers, PyTorch Lightning, XGBoost, and Sci-Kit Learn. Users can easily log metrics, visualize performance, and compare experiments using W&B. The platform also supports hosting options in the cloud or on private infrastructure, making it versatile for various deployment needs.

Empirical is a tool that allows you to test different LLMs, prompts, and other model configurations across all the scenarios that matter for your application. With Empirical, you can run your test datasets locally against off-the-shelf models, test your own custom models and RAG applications, view, compare, and analyze outputs on a web UI, score your outputs with scoring functions, and run tests on CI/CD.

This repository contains self-trained single image super resolution (SISR) models. The models are trained on various datasets and use different network architectures. They can be used to upscale images by 2x, 4x, or 8x, and can handle various types of degradation, such as JPEG compression, noise, and blur. The models are provided as safetensors files, which can be loaded into a variety of deep learning frameworks, such as PyTorch and TensorFlow. The repository also includes a number of resources, such as examples, results, and a website where you can compare the outputs of different models.

Eval-Scope is a framework for evaluating and improving large language models (LLMs). It provides a set of commonly used test datasets, metrics, and a unified model interface for generating and evaluating LLM responses. Eval-Scope also includes an automatic evaluator that can score objective questions and use expert models to evaluate complex tasks. Additionally, it offers a visual report generator, an arena mode for comparing multiple models, and a variety of other features to support LLM evaluation and development.

MathEval is a benchmark designed for evaluating the mathematical capabilities of large models. It includes over 20 evaluation datasets covering various mathematical domains with more than 30,000 math problems. The goal is to assess the performance of large models across different difficulty levels and mathematical subfields. MathEval serves as a reliable reference for comparing mathematical abilities among large models and offers guidance on enhancing their mathematical capabilities in the future.

qapyq is an image viewer and AI-assisted editing tool designed to help curate datasets for generative AI models. It offers features such as image viewing, editing, captioning, batch processing, and AI assistance. Users can perform tasks like cropping, scaling, editing masks, tagging, and applying sorting and filtering rules. The tool supports state-of-the-art captioning and masking models, with options for model settings, GPU acceleration, and quantization. qapyq aims to streamline the process of preparing images for training AI models by providing a user-friendly interface and advanced functionalities.

Cherry Data Selection project introduces a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, minimizing manual curation and cost for instruction tuning. The project focuses on selecting impactful training samples ('cherry data') to enhance LLM instruction tuning by estimating instruction-following difficulty. The method involves phases like 'Learning from Brief Experience', 'Evaluating Based on Experience', and 'Retraining from Self-Guided Experience' to improve LLM performance.

The AI Agents Contributions Library is a repository dedicated to managing datasets on voice and cognitive core data for AI agents within the Virtual DAO ecosystem. It provides a structured framework for recording, reviewing, and rewarding contributions from contributors. The repository includes folders for character cards, contribution datasets, fine-tuning resources, text datasets, and voice datasets. Contributors can submit datasets following specific guidelines and formats, and the Virtual DAO team reviews and integrates approved datasets to enhance AI agents' capabilities.

Kiln is an intuitive tool for fine-tuning LLM models, generating synthetic data, and collaborating on datasets. It offers desktop apps for Windows, MacOS, and Linux, zero-code fine-tuning for various models, interactive data generation, and Git-based version control. Users can easily collaborate with QA, PM, and subject matter experts, generate auto-prompts, and work with a wide range of models and providers. The tool is open-source, privacy-first, and supports structured data tasks in JSON format. Kiln is free to use and helps build high-quality AI products with datasets, facilitates collaboration between technical and non-technical teams, allows comparison of models and techniques without code, ensures structured data integrity, and prioritizes user privacy.

AITreasureBox is a comprehensive collection of AI tools and resources designed to simplify and accelerate the development of AI projects. It provides a wide range of pre-trained models, datasets, and utilities that can be easily integrated into various AI applications. With AITreasureBox, developers can quickly prototype, test, and deploy AI solutions without having to build everything from scratch. Whether you are working on computer vision, natural language processing, or reinforcement learning projects, AITreasureBox has something to offer for everyone. The repository is regularly updated with new tools and resources to keep up with the latest advancements in the field of artificial intelligence.

Neptune is a scalable experiment tracker for teams training foundation models. Log millions of runs, effortlessly monitor and visualize model training, and deploy on your infrastructure. Track 100% of metadata to accelerate AI breakthroughs. Log and display any framework and metadata type from any ML pipeline. Organize experiments with nested structures and custom dashboards. Compare results, visualize training, and optimize models quicker. Version models, review stages, and access production-ready models. Share results, manage users, and projects. Integrate with 25+ frameworks. Trusted by great companies to improve workflow.

LayerSkip is an implementation enabling early exit inference and self-speculative decoding. It provides a code base for running models trained using the LayerSkip recipe, offering speedup through self-speculative decoding. The tool integrates with Hugging Face transformers and provides checkpoints for various LLMs. Users can generate tokens, benchmark on datasets, evaluate tasks, and sweep over hyperparameters to optimize inference speed. The tool also includes correctness verification scripts and Docker setup instructions. Additionally, other implementations like gpt-fast and Native HuggingFace are available. Training implementation is a work-in-progress, and contributions are welcome under the CC BY-NC license.

This repository provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

The KAN-GPT repository is a PyTorch implementation of Generative Pre-trained Transformers (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling. It provides a model for generating text based on prompts, with a focus on improving performance compared to traditional MLP-GPT models. The repository includes scripts for training the model, downloading datasets, and evaluating model performance. Development tasks include integrating with other libraries, testing, and documentation.

The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as a repository of AI-ready Earth Observation (EO) datasets. It can be easily applied for a variety of Earth Observation tasks, such as land use and cover classification, crop type prediction, localization of specific objects (semantic segmentation), etc. The main goal of AiTLAS is to facilitate better usability and adoption of novel AI methods (and models) by EO experts, while offering easy access and standardized format of EO datasets to AI experts which allows benchmarking of various existing and novel AI methods tailored for EO data.

PDEBench provides a diverse and comprehensive set of benchmarks for scientific machine learning, including challenging and realistic physical problems. The repository consists of code for generating datasets, uploading and downloading datasets, training and evaluating machine learning models as baselines. It features a wide range of PDEs, realistic and difficult problems, ready-to-use datasets with various conditions and parameters. PDEBench aims for extensibility and invites participation from the SciML community to improve and extend the benchmark.

Explore & interpret the Eurostat database. Type in requests for statistics, also ask to visualize it. Works best wish specific datasets. It's meant for professionals familiar with the Eurostat database looking for a faster way to explore it.

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