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EleutherAI is an open-source AI research platform that focuses on discussing and disseminating cutting-edge research in the field of artificial intelligence. The platform provides updates on various research projects, including Mechanistic Anomaly Detection, Automated Interpretability for Sparse Autoencoder Features, Experiments in Generalization, Concept Erasure, Knowledge Elicitation, and more. EleutherAI aims to foster collaboration and innovation in the AI community by sharing insights and advancements in the field.

John Schulman's Homepage is an AI tool developed by a researcher at Anthropic. The website focuses on aligning large language models, scalable oversight, and developing better written specifications of model behavior. It showcases the researcher's work in the field of AI, including projects like ChatGPT and the OpenAI API. The homepage also highlights the researcher's academic background, including a PhD in Computer Science from UC Berkeley with a focus on robotics and reinforcement learning.

MindpoolAI is a tool that allows users to access multiple leading AI models with a single query. This means that users can get the answers they are looking for, spark ideas, and fuel their work, creativity, and curiosity. MindpoolAI is easy to use and does not require any technical expertise. Users simply need to enter their prompt and select the AI models they want to compare. MindpoolAI will then send the query to the selected models and present the results in an easy-to-understand format.

Runway is a platform that provides tools and resources for artists and researchers to create and explore artificial intelligence-powered creative applications. The platform includes a library of pre-trained models, a set of tools for building and training custom models, and a community of users who share their work and collaborate on projects. Runway's mission is to make AI more accessible and understandable, and to empower artists and researchers to create new and innovative forms of creative expression.

Victor Dibia's website showcases his expertise in Applied Machine Learning and Human-Computer Interaction (HCI). He is a Principal Research Software Engineer at Microsoft Research, focusing on Generative AI. The site features his publications, projects, CV, and blog posts, covering topics such as multi-agent systems, recommender systems, and more. Victor's work has been recognized in conferences and media outlets, highlighting his contributions to the field of AI and HCI.

Applied AI Institute is an educational platform that provides AI education to business and IT professionals. They offer a variety of instructor-led webinars, tailored courses, guided hackathons, and solution development services. The institute focuses on enhancing learners' competencies and attitudes for success by offering customized courses with real-world client projects. Additionally, they provide consultation services to create solution assets for specific use cases, ensuring optimal results.

Google Quantum AI is a leading platform dedicated to advancing the field of quantum computing. The platform offers educational resources, research publications, open-source tools like Cirq, and career opportunities. Google Quantum AI's mission is to build best-in-class quantum computing systems with error correction capabilities, enabling the solution of complex problems that are currently intractable for classical computers. The platform also hosts the XPRIZE Quantum Applications competition, collaborates with industry and academic partners to explore future applications of quantum computing, and shares research and blog posts on quantum algorithms and advancements.

The AI Conference 2024 is a groundbreaking vendor-neutral event that brings together researchers, engineers, and entrepreneurs to learn, collaborate, and network with some of the brightest minds in AI. The conference explores cutting-edge technologies, practical applications, and strategic insights in the field of artificial intelligence. Attendees can expect thought-provoking sessions, captivating talks, and valuable networking opportunities, all aimed at shaping the future of AI.

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.

ChatTTS is a natural and expressive text-to-speech tool designed for dialogue applications. It supports mixed language input and offers multi-speaker capabilities with precise control over prosodic elements like laughter, pauses, and intonation. Users can explore the unique capabilities of ChatTTS, enjoy conversational TTS optimized for dialogue-based tasks, and benefit from fine-grained control over prosodic features. The tool is multilingual, supporting both English and Chinese languages, and is open-source and customizable with pretrained models available for further research and development.

Salesforce AI Blog is an AI tool that focuses on various AI research topics such as accountability, accuracy, AI agents, AI coding, AI ethics, AI object detection, deep learning, forecasting, generative AI, and more. The blog showcases cutting-edge research, advancements, and projects in the field of artificial intelligence. It also highlights the work of Salesforce Research team members and their contributions to the AI community.

Midjourney is an independent research lab that aims to explore new mediums of thought and expand the imaginative powers of the human species. The website serves as a platform where users can collaborate to create new worlds, fantastic characters, and unique imagery from short text descriptions. Midjourney focuses on design, human infrastructure, and AI, with a small self-funded team dedicated to pushing the boundaries of creativity and innovation.

Wildlife Insights is an AI application that brings cutting-edge technology to wildlife conservation. It streamlines decision-making by providing machine learning models and tools to manage, analyze, and share camera trap data. Users can easily upload, identify, analyze, and discover wildlife through the platform, enabling better decisions to help wildlife thrive globally.

AIAI.Tools is a comprehensive directory of AI-powered tools and applications designed to enhance various aspects of work and productivity. The platform features a wide range of AI tools spanning different categories such as SEO, productivity, research, automation, and development. Users can explore and discover innovative AI solutions that leverage cutting-edge technologies like reinforcement learning algorithms, ChatGPT AI technology, and video search capabilities. AIAI.Tools aims to simplify tasks, improve efficiency, and streamline workflows by providing access to advanced AI tools that cater to diverse needs across industries.

AILaunching is a comprehensive directory platform that showcases a variety of AI tools across different categories. It simplifies the process of finding and exploring AI-powered solutions for various needs, such as sales proposals, social media assistance, travel planning, hosting, web apps, research, marketing, and more. The platform aims to connect users with cutting-edge AI technologies to enhance their productivity and efficiency in different domains.

Magenta is an open-source research project that explores the role of machine learning as a tool in the creative process. It provides a collection of music creativity tools built on Magenta's open-source models, using cutting-edge machine learning techniques for music generation.

Google DeepMind is an AI research lab that aims to build AI responsibly to benefit humanity. They work on complex challenges in AI, focusing on breakthroughs and innovations. The lab develops various AI models and agents, such as Gemini, Project Astra, Imagen, Veo, AlphaFold, and SynthID. Google DeepMind emphasizes responsibility, safety, education, and career development in the AI field. They also share their research through publications, events, and podcasts, showcasing how AI is transforming the world.

JH360 AI is an AI-driven consulting service that helps companies tap into their true essence and explore new growth opportunities. They offer services such as market research, strategy development, and measurement and analysis. JH360 AI excels in process improvement by leveraging cutting-edge AI technology and expert methodologies. They provide tailor-made generative AI strategies, rapid prototyping, and smart AI integration to drive innovation and success across various industries.

Papertalk.io is an AI-powered platform that revolutionizes research by providing users with access to over 215 million papers, AI-generated explanations, and actionable insights. The platform offers precision search tools, AI-powered understanding of research papers, and personalized guidance on applying insights practically. Papertalk.io aims to make research more accessible and approachable for users from diverse backgrounds, transforming complex data into easy-to-digest formats to foster innovation and expertise.

Connected Papers is a search engine for academic papers that uses artificial intelligence to help users find and explore relevant research. It allows users to search for papers by keyword, author, or title, and then explore the connections between them. Connected Papers also provides a variety of tools to help users organize and manage their research, including the ability to create custom collections of papers, add notes and annotations, and share their research with others.

CEO-Agentic-AI-Framework is an ultra-lightweight Agentic AI framework based on the ReAct paradigm. It supports mainstream LLMs and is stronger than Swarm. The framework allows users to build their own agents, assign tasks, and interact with them through a set of predefined abilities. Users can customize agent personalities, grant and deprive abilities, and assign queries for specific tasks. CEO also supports multi-agent collaboration scenarios, where different agents with distinct capabilities can work together to achieve complex tasks. The framework provides a quick start guide, examples, and detailed documentation for seamless integration into research projects.

HuggingFaceGuidedTourForMac is a guided tour on how to install optimized pytorch and optionally Apple's new MLX, JAX, and TensorFlow on Apple Silicon Macs. The repository provides steps to install homebrew, pytorch with MPS support, MLX, JAX, TensorFlow, and Jupyter lab. It also includes instructions on running large language models using HuggingFace transformers. The repository aims to help users set up their Macs for deep learning experiments with optimized performance.

Awesome Mobile LLMs is a curated list of Large Language Models (LLMs) and related studies focused on mobile and embedded hardware. The repository includes information on various LLM models, deployment frameworks, benchmarking efforts, applications, multimodal LLMs, surveys on efficient LLMs, training LLMs on device, mobile-related use-cases, industry announcements, and related repositories. It aims to be a valuable resource for researchers, engineers, and practitioners interested in mobile LLMs.

Collective Mind (CM) is a collection of portable, extensible, technology-agnostic and ready-to-use automation recipes with a human-friendly interface (aka CM scripts) to unify and automate all the manual steps required to compose, run, benchmark and optimize complex ML/AI applications on any platform with any software and hardware: see online catalog and source code. CM scripts require Python 3.7+ with minimal dependencies and are continuously extended by the community and MLCommons members to run natively on Ubuntu, MacOS, Windows, RHEL, Debian, Amazon Linux and any other operating system, in a cloud or inside automatically generated containers while keeping backward compatibility - please don't hesitate to report encountered issues here and contact us via public Discord Server to help this collaborative engineering effort! CM scripts were originally developed based on the following requirements from the MLCommons members to help them automatically compose and optimize complex MLPerf benchmarks, applications and systems across diverse and continuously changing models, data sets, software and hardware from Nvidia, Intel, AMD, Google, Qualcomm, Amazon and other vendors: * must work out of the box with the default options and without the need to edit some paths, environment variables and configuration files; * must be non-intrusive, easy to debug and must reuse existing user scripts and automation tools (such as cmake, make, ML workflows, python poetry and containers) rather than substituting them; * must have a very simple and human-friendly command line with a Python API and minimal dependencies; * must require minimal or zero learning curve by using plain Python, native scripts, environment variables and simple JSON/YAML descriptions instead of inventing new workflow languages; * must have the same interface to run all automations natively, in a cloud or inside containers. CM scripts were successfully validated by MLCommons to modularize MLPerf inference benchmarks and help the community automate more than 95% of all performance and power submissions in the v3.1 round across more than 120 system configurations (models, frameworks, hardware) while reducing development and maintenance costs.

This repository is a curated list of awesome public object detection and recognition datasets. It includes a wide range of datasets related to object detection and recognition tasks, such as general detection and recognition datasets, autonomous driving datasets, adverse weather datasets, person detection datasets, anti-UAV datasets, optical aerial imagery datasets, low-light image datasets, infrared image datasets, SAR image datasets, multispectral image datasets, 3D object detection datasets, vehicle-to-everything field datasets, super-resolution field datasets, and face detection and recognition datasets. The repository also provides information on tools for data annotation, data augmentation, and data management related to object detection tasks.

Efficient Foundation Model Survey is a comprehensive analysis of resource-efficient large language models (LLMs) and multimodal foundation models. The survey covers algorithmic and systemic innovations to support the growth of large models in a scalable and environmentally sustainable way. It explores cutting-edge model architectures, training/serving algorithms, and practical system designs. The goal is to provide insights on tackling resource challenges posed by large foundation models and inspire future breakthroughs in the field.

A Collection of Papers and Codes for CVPR 2024 AIGC. This repository compiles and organizes research papers and code related to CVPR 2024 and ECCV 2024 AIGC (Artificial Intelligence and Graphics Computing). It serves as a valuable resource for individuals interested in the latest advancements in the field of computer vision and artificial intelligence. Users can find a curated list of papers and accompanying code repositories for further exploration and research. The repository encourages collaboration and contributions from the community through stars, forks, and pull requests.

The Together Cookbook is a collection of code and guides designed to help developers build with open source models using Together AI. The recipes provide examples on how to chain multiple LLM calls, create agents that route tasks to specialized models, run multiple LLMs in parallel, break down tasks into parallel subtasks, build agents that iteratively improve responses, perform LoRA fine-tuning and inference, fine-tune LLMs for repetition, improve summarization capabilities, fine-tune LLMs on multi-step conversations, implement retrieval-augmented generation, conduct multimodal search and conditional image generation, visualize vector embeddings, improve search results with rerankers, implement vector search with embedding models, extract structured text from images, summarize and evaluate outputs with LLMs, generate podcasts from PDF content, and get LLMs to generate knowledge graphs.

Awesome AI Tools is a curated list of popular tools and resources for artificial intelligence enthusiasts. It includes a wide range of tools such as machine learning libraries, deep learning frameworks, data visualization tools, and natural language processing resources. Whether you are a beginner or an experienced AI practitioner, this repository aims to provide you with a comprehensive collection of tools to enhance your AI projects and research. Explore the list to discover new tools, stay updated with the latest advancements in AI technology, and find the right resources to support your AI endeavors.

This repository is a curated list of papers related to 3D tasks empowered by Large Language Models (LLMs). It covers tasks such as 3D understanding, reasoning, generation, and embodied agents. The repository also includes other Foundation Models like CLIP and SAM to provide a comprehensive view of the area. It is actively maintained and updated to showcase the latest advances in the field. Users can find a variety of research papers and projects related to 3D tasks and LLMs in this repository.

MindSearch is an open-source AI Search Engine Framework that mimics human minds to provide deep AI search capabilities. It allows users to deploy their own search engine using either close-source or open-source language models. MindSearch offers features such as answering any question using web knowledge, in-depth knowledge discovery, detailed solution paths, optimized UI experience, and dynamic graph construction process.

Open AI is a powerful tool for artificial intelligence research and development. It provides a wide range of machine learning models and algorithms, making it easier for developers to create innovative AI applications. With Open AI, users can explore cutting-edge technologies such as natural language processing, computer vision, and reinforcement learning. The platform offers a user-friendly interface and comprehensive documentation to support users in building and deploying AI solutions. Whether you are a beginner or an experienced AI practitioner, Open AI offers the tools and resources you need to accelerate your AI projects and stay ahead in the rapidly evolving field of artificial intelligence.

This repository, named Awesome-Embodied-Agent-with-LLMs, is a curated list of research related to Embodied AI or agents with Large Language Models. It includes various papers, surveys, and projects focusing on topics such as self-evolving agents, advanced agent applications, LLMs with RL or world models, planning and manipulation, multi-agent learning and coordination, vision and language navigation, detection, 3D grounding, interactive embodied learning, rearrangement, benchmarks, simulators, and more. The repository provides a comprehensive collection of resources for individuals interested in exploring the intersection of embodied agents and large language models.

Qlib is an open-source, AI-oriented quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It covers the entire chain of quantitative investment, from alpha seeking to order execution. The platform empowers researchers to explore ideas and implement productions using AI technologies in quantitative investment. Qlib collaboratively solves key challenges in quantitative investment by releasing state-of-the-art research works in various paradigms. It provides a full ML pipeline for data processing, model training, and back-testing, enabling users to perform tasks such as forecasting market patterns, adapting to market dynamics, and modeling continuous investment decisions.

hugegraph-ai aims to explore the integration of HugeGraph with artificial intelligence (AI) and provide comprehensive support for developers to leverage HugeGraph's AI capabilities in their projects. It includes modules for large language models, graph machine learning, and a Python client for HugeGraph. The project aims to address challenges like timeliness, hallucination, and cost-related issues by integrating graph systems with AI technologies.

Kalavai is an open-source platform that transforms everyday devices into an AI supercomputer by aggregating resources from multiple machines. It facilitates matchmaking of resources for large AI projects, making AI hardware accessible and affordable. Users can create local and public pools, connect with the community's resources, and share computing power. The platform aims to be a management layer for research groups and organizations, enabling users to unlock the power of existing hardware without needing a devops team. Kalavai CLI tool helps manage both versions of the platform.

Awesome Agents is a curated list of open source AI agents designed for various tasks such as private interactions with documents, chat implementations, autonomous research, human-behavior simulation, code generation, HR queries, domain-specific research, and more. The agents leverage Large Language Models (LLMs) and other generative AI technologies to provide solutions for complex tasks and projects. The repository includes a diverse range of agents for different use cases, from conversational chatbots to AI coding engines, and from autonomous HR assistants to vision task solvers.

**RVC_CLI: Retrieval-based Voice Conversion Command Line Interface** This command-line interface (CLI) provides a comprehensive set of tools for voice conversion, enabling you to modify the pitch, timbre, and other characteristics of audio recordings. It leverages advanced machine learning models to achieve realistic and high-quality voice conversions. **Key Features:** * **Inference:** Convert the pitch and timbre of audio in real-time or process audio files in batch mode. * **TTS Inference:** Synthesize speech from text using a variety of voices and apply voice conversion techniques. * **Training:** Train custom voice conversion models to meet specific requirements. * **Model Management:** Extract, blend, and analyze models to fine-tune and optimize performance. * **Audio Analysis:** Inspect audio files to gain insights into their characteristics. * **API:** Integrate the CLI's functionality into your own applications or workflows. **Applications:** The RVC_CLI finds applications in various domains, including: * **Music Production:** Create unique vocal effects, harmonies, and backing vocals. * **Voiceovers:** Generate voiceovers with different accents, emotions, and styles. * **Audio Editing:** Enhance or modify audio recordings for podcasts, audiobooks, and other content. * **Research and Development:** Explore and advance the field of voice conversion technology. **For Jobs:** * Audio Engineer * Music Producer * Voiceover Artist * Audio Editor * Machine Learning Engineer **AI Keywords:** * Voice Conversion * Pitch Shifting * Timbre Modification * Machine Learning * Audio Processing **For Tasks:** * Convert Pitch * Change Timbre * Synthesize Speech * Train Model * Analyze Audio

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