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XKool Technology is an AI cloud platform offering comprehensive solutions for the building industry. It provides digital and intelligent empowerment for design, construction, and management processes. The platform integrates AI technology to enhance building industrial upgrades and offers AI-assisted content creation, model marketplace, AI toolbox, and various design and management solutions.

Athina AI is a platform that provides research and guides for building safe and reliable AI products. It helps thousands of AI engineers in building safer products by offering tutorials, research papers, and evaluation techniques related to large language models. The platform focuses on safety, prompt engineering, hallucinations, and evaluation of AI models.

Hume AI is an AI application that offers the Octave language model for text-to-speech (TTS) capabilities. It provides a voice-based LLM that understands words in context to predict emotions, cadence, and more. Users can create various AI voices with specific prompts and scripts, adjusting emotional delivery and speaking styles on command. The application aims to generate expressive AI voices for podcasts, voiceovers, audiobooks, and more, with total control over the voice output.

Moonvalley is a research company focused on developing cutting-edge generative media technologies. The team consists of top researchers, engineers, and artists with backgrounds in leading tech companies. Moonvalley specializes in advanced video and image machine learning models, aiming to shape the future of media creation.

Hubble is an all-in-one user research platform designed to help uncover meaningful insights quickly and easily. It offers a wide range of features such as in-product research, contextual surveys, prototype testing, usability tests, participant recruitment, and resources like guides, templates, and demo videos. Trusted by product and research teams in world-class organizations, Hubble provides fast, scalable insights with industry-leading coverage of task types and surveys. With AI-powered research report building, quantitative data analysis, and easy exporting capabilities, Hubble aims to streamline the user research process and drive customer-centric decision-making.

The Research Center Trustworthy Data Science and Security is a hub for interdisciplinary research focusing on building trust in artificial intelligence, machine learning, and cyber security. The center aims to develop trustworthy intelligent systems through research in trustworthy data analytics, explainable machine learning, and privacy-aware algorithms. By addressing the intersection of technological progress and social acceptance, the center seeks to enable private citizens to understand and trust technology in safety-critical applications.

UpCodes is a searchable platform that provides access to building codes, assemblies, and building products libraries. It offers a comprehensive database of regulations and standards for construction projects, enabling users to easily search and reference relevant information. With UpCodes, architects, engineers, contractors, and other industry professionals can streamline their workflow, ensure compliance with codes, and enhance the quality of their designs. The platform is designed to simplify the process of accessing and interpreting building codes, saving time and reducing errors in construction projects.

The Center for AI Safety (CAIS) is a research and field-building nonprofit based in San Francisco. Their mission is to reduce societal-scale risks associated with artificial intelligence (AI) by conducting impactful research, building the field of AI safety researchers, and advocating for safety standards. They offer resources such as a compute cluster for AI/ML safety projects, a blog with in-depth examinations of AI safety topics, and a newsletter providing updates on AI safety developments. CAIS focuses on technical and conceptual research to address the risks posed by advanced AI systems.

The Center for AI Safety (CAIS) is a research and field-building nonprofit organization based in San Francisco. They conduct impactful research, advocacy projects, and provide resources to reduce societal-scale risks associated with artificial intelligence (AI). CAIS focuses on technical AI safety research, field-building projects, and offers a compute cluster for AI/ML safety projects. They aim to develop and use AI safely to benefit society, addressing inherent risks and advocating for safety standards.

Cartesia Sonic Team Blog Research Playground is an AI application that offers real-time multimodal intelligence for every device. The application aims to build the next generation of AI by providing ubiquitous, interactive intelligence that can run on any device. It features the fastest, ultra-realistic generative voice API and is backed by research on simple linear attention language models and state-space models. The founding team, who met at the Stanford AI Lab, has invented State Space Models (SSMs) and scaled it up to achieve state-of-the-art results in various modalities such as text, audio, video, images, and time-series data.

The Institute for the Advancement of Legal and Ethical AI (ALEA) is a platform dedicated to supporting socially, economically, and environmentally sustainable futures through open research and education. They focus on developing legal and ethical frameworks to ensure that AI systems benefit society while minimizing harm to the economy and the environment. ALEA engages in activities such as open data collection, model training, technical and policy research, education, and community building to promote the responsible use of AI.

Google DeepMind is an AI research lab that aims to build AI responsibly to benefit humanity. They work on complex challenges in AI and have developed innovative AI models like Gemini, Project Astra, Imagen, Veo, AlphaFold, and SynthID. The lab focuses on responsibility, safety, education, and breakthrough research in AI. Google DeepMind strives to make the AI ecosystem more representative of society and to address AI-related risks. They have a strong emphasis on ethical AI principles and advancing the field of artificial intelligence.

Google DeepMind is an AI research company that aims to develop artificial intelligence technologies to benefit the world. They focus on creating next-generation AI systems to solve complex scientific and engineering challenges. Their models like Gemini, Veo, Imagen 3, SynthID, and AlphaFold are at the forefront of AI innovation. DeepMind also emphasizes responsibility, safety, education, and career opportunities in the field of AI.

Kyutai is an open science AI lab based in Paris, with a mission to build and democratize artificial general intelligence through open science AI research. The lab offers various resources and tools for AI enthusiasts and researchers to collaborate and innovate in the field of AI. Kyutai aims to foster a community of like-minded individuals who are passionate about advancing AI technology through open collaboration and research.

Altera is an applied research company focused on building digital humans with fundamental human qualities. Led by Dr. Robert Yang, the team comprises computational neuroscientists, researchers, and engineers from prestigious institutions. Their mission is to create digital beings that can live, care, and grow alongside humans. The company's early research prototypes in games have paved the way for the development of digital humans that can interact with users in various ways.

SingularityNET is a decentralized AI platform that offers funding opportunities for AI projects. It allows individuals and organizations to develop and monetize their AI services while keeping ownership of their models. The platform aims to build a global ecosystem of decentralized and beneficial AI services through community-driven programs and rewards. SingularityNET provides a space for project proposals, expert reviews, and grants to support the growth of AI projects aligned with the goal of building a Beneficial Artificial General Intelligence.

GenWorlds is an event-based communication framework for building multi-agent systems. It offers a platform for creating Generative AI applications where users can design customizable environments, utilize scalable architecture, access a repository of memories and tools, choose cognitive processes for agents, and pick coordination protocols. GenWorlds aims to foster a vibrant community of developers, AI enthusiasts, and innovators to collaborate, innovate, share knowledge, and grow together.

Luma Dream Machine is a cutting-edge AI tool that empowers users to ideate, visualize, and create stunning images and videos effortlessly. It offers a new fluid medium for creativity, enabling users to bring their wildest dreams to life with the help of powerful image and video AI models. The platform is designed to be intuitive and user-friendly, allowing users to explore endless ideas, make unique creations, and share their vision with the world. Luma Dream Machine is available on iOS and the Web, providing a seamless experience for creators of all levels.

Cognition is an applied AI lab that has developed Devin, a collaborative AI teammate designed to assist engineering teams in achieving more. Devin is the world's first AI software engineer, available for all engineering teams. The team behind Cognition comprises individuals with extensive experience in applied AI at top companies, and they are focused on building AI that can reason and solve complex problems.

Adept is building a machine learning model that can interact with everything on your computer. It takes your goals, in plain language, and turns them into actions on the software you use every day. Adept is designed and trained specifically for taking actions on computers in response to your natural language commands. ACT-1 is Adept's first step towards a foundation model that can use every software tool, API, and website that exists.

SurveyX is an advanced academic survey automation system that leverages Large Language Models (LLMs) to generate high-quality, domain-specific academic papers and surveys. Users can request comprehensive academic papers or surveys tailored to specific topics by providing a paper title and keywords for literature retrieval. The system streamlines academic research by automating paper creation, saving users time and effort in compiling research content.

A curated list of Generative AI projects, tools, artworks, and models

AI Audio Datasets List (AI-ADL) is a comprehensive collection of datasets consisting of speech, music, and sound effects, used for Generative AI, AIGC, AI model training, and audio applications. It includes datasets for speech recognition, speech synthesis, music information retrieval, music generation, audio processing, sound synthesis, and more. The repository provides a curated list of diverse datasets suitable for various AI audio tasks.

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

The neo is an open source robotics research platform powered by a OnePlus 3 smartphone and an STM32F205-based CAN interface board, housed in a 3d-printed casing with active cooling. It includes NEOS, a stripped down Android ROM, and offers a modern Linux environment for development. The platform leverages the high performance embedded processor and sensor capabilities of modern smartphones at a low cost. A detailed guide is available for easy construction, requiring online shopping and soldering skills. The total cost for building a neo is approximately $700.

SPEAR is a Simulator for Photorealistic Embodied AI Research that addresses limitations in existing simulators by offering 300 unique virtual indoor environments with detailed geometry, photorealistic materials, and unique floor plans. It provides an OpenAI Gym interface for interaction via Python, released under an MIT License. The simulator was developed with support from the Intelligent Systems Lab at Intel and Kujiale.

This repository serves as a comprehensive hub for updates on generative AI research, interview materials, notebooks, and more. It includes monthly best GenAI papers list, interview resources, free courses, and code repositories/notebooks for developing generative AI applications. The repository is regularly updated with the latest additions to keep users informed and engaged in the field of generative AI.

ChatGPT Queue Prompts is a collection of prompt chains designed to enhance interactions with large language models like ChatGPT. These prompt chains help build context for the AI before performing specific tasks, improving performance. Users can copy and paste prompt chains into the ChatGPT Queue extension to process prompts in sequence. The repository includes example prompt chains for tasks like conducting AI company research, building SEO optimized blog posts, creating courses, revising resumes, enriching leads for CRM, personal finance document creation, workout and nutrition plans, marketing plans, and more.

The 'Awesome AI Guidelines' repository aims to simplify the ecosystem of guidelines, principles, codes of ethics, standards, and regulations around artificial intelligence. It provides a comprehensive collection of resources addressing ethical and societal challenges in AI systems, including high-level frameworks, principles, processes, checklists, interactive tools, industry standards initiatives, online courses, research, and industry newsletters, as well as regulations and policies from various countries. The repository serves as a valuable reference for individuals and teams designing, building, and operating AI systems to navigate the complex landscape of AI ethics and governance.

This repository is dedicated to enhancing Language Model (LLM) reasoning with reinforcement learning (RL). It includes a collection of the latest papers, slides, and materials related to RL-based LLM reasoning, aiming to facilitate quick learning and understanding in this field. Starring this repository allows users to stay updated and engaged with the forefront of RL-based LLM reasoning.

KG-LLM-Papers is a repository that collects papers integrating knowledge graphs (KGs) and large language models (LLMs). It serves as a comprehensive resource for research on the role of KGs in the era of LLMs, covering surveys, methods, and resources related to this integration.

Awesome-Colorful-LLM is a meticulously assembled anthology of vibrant multimodal research focusing on advancements propelled by large language models (LLMs) in domains such as Vision, Audio, Agent, Robotics, and Fundamental Sciences like Mathematics. The repository contains curated collections of works, datasets, benchmarks, projects, and tools related to LLMs and multimodal learning. It serves as a comprehensive resource for researchers and practitioners interested in exploring the intersection of language models and various modalities for tasks like image understanding, video pretraining, 3D modeling, document understanding, audio analysis, agent learning, robotic applications, and mathematical research.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

This repository is a Chinese translation of the GitHub project 'LLMs-from-scratch', including detailed markdown notes and related Jupyter code. The translation process aims to maintain the accuracy of the original content while optimizing the language and expression to better suit Chinese learners' reading habits. The repository features detailed Chinese annotations for all Jupyter code, aiding users in practical implementation. It also provides various supplementary materials to expand knowledge. The project focuses on building Large Language Models (LLMs) from scratch, covering fundamental constructions like Transformer architecture, sequence modeling, and delving into deep learning models such as GPT and BERT. Each part of the project includes detailed code implementations and learning resources to help users construct LLMs from scratch and master their core technologies.

Awesome LLM is a curated list of resources related to Large Language Models (LLMs), including models, projects, datasets, benchmarks, materials, papers, posts, GitHub repositories, HuggingFace repositories, and reading materials. It provides detailed information on various LLMs, their parameter sizes, announcement dates, and contributors. The repository covers a wide range of LLM-related topics and serves as a valuable resource for researchers, developers, and enthusiasts interested in the field of natural language processing and artificial intelligence.

The W3C Cognitive AI Community Group focuses on advancing Cognitive AI through collaboration on defining use cases, open source implementations, and application areas. The group aims to demonstrate the potential of Cognitive AI in various domains such as customer services, healthcare, cybersecurity, online learning, autonomous vehicles, manufacturing, and web search. They work on formal specifications for chunk data and rules, plausible knowledge notation, and neural networks for human-like AI. The group positions Cognitive AI as a combination of symbolic and statistical approaches inspired by human thought processes. They address research challenges including mimicry, emotional intelligence, natural language processing, and common sense reasoning. The long-term goal is to develop cognitive agents that are knowledgeable, creative, collaborative, empathic, and multilingual, capable of continual learning and self-awareness.

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