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Google Research is a leading research organization focusing on advancing science and artificial intelligence. They conduct research in various domains such as AI/ML foundations, responsible human-centric technology, science & societal impact, computing paradigms, and algorithms & optimization. Google Research aims to create an environment for diverse research across different time scales and levels of risk, driving advancements in computer science through fundamental and applied research. They publish hundreds of research papers annually, collaborate with the academic community, and work on projects that impact technology used by billions of people worldwide.

Google Research is a team of scientists and engineers working on a wide range of topics in computer science, including artificial intelligence, machine learning, and quantum computing. Our mission is to advance the state of the art in these fields and to develop new technologies that can benefit society. We publish hundreds of research papers each year and collaborate with researchers from around the world. Our work has led to the development of many new products and services, including Google Search, Google Translate, and Google Maps.

The Google Research Blog is a platform for researchers at Google to share their latest work in artificial intelligence, machine learning, and other related fields. The blog covers a wide range of topics, from theoretical research to practical applications. The goal of the blog is to provide a forum for researchers to share their ideas and findings, and to foster collaboration between researchers at Google and around the world.

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.

RapidAI Research Institute is an academic institution under the RapidAI open-source organization, a non-enterprise academic institution. It serves as a platform for academic research and collaboration, providing opportunities for aspiring researchers to publish papers and engage in scholarly activities. The institute offers mentorship programs and benefits for members, including access to resources such as internet connectivity, GPU configurations, and storage space. The management team consists of esteemed professionals in the field, ensuring a conducive environment for academic growth and development.

Research Studio is a next-level UX research tool that helps you streamline your user research with AI-enhanced analysis. Whether you're a freelance UX designer, user researcher, or agency, Research Studio can help you get the insights you need to make better decisions about your products and services.

HelpMoji Research is an AI-powered product research assistant designed to help users conduct internet research without being tracked by digital advertising giants. The tool allows users to search for product specifications, compare products, and conduct research in a distraction-free environment. It works on all devices and browsers, ensuring accessibility for all users.

MIRI (Machine Intelligence Research Institute) is a non-profit research organization dedicated to ensuring that artificial intelligence has a positive impact on humanity. MIRI conducts foundational mathematical research on topics such as decision theory, game theory, and reinforcement learning, with the goal of developing new insights into how to build safe and beneficial AI systems.

Branded Research, acquired by Dynata, provides access to AI-verified audience insights. It offers a range of research methods, including surveys, webcam studies, and emotional AI. With its advanced algorithms and extensive profiling, Branded helps businesses connect with their target audience and gain valuable insights to drive innovation. The company serves various industries, including tech, consumer goods, healthcare, and research agencies.

The Berkeley Artificial Intelligence Research (BAIR) Lab is a renowned research lab at UC Berkeley focusing on computer vision, machine learning, natural language processing, planning, control, and robotics. With over 50 faculty members and 300 graduate students, BAIR conducts research on fundamental advances in AI and interdisciplinary themes like multi-modal deep learning and human-compatible AI.

AIM Research is a leading platform providing insights and analysis on the Artificial Intelligence industry. The website offers a comprehensive range of resources, including research reports, event coverage, news articles, and expert opinions. AIM Research focuses on highlighting the latest trends, innovations, and key players in the AI sector, catering to professionals, researchers, and enthusiasts seeking in-depth knowledge and understanding of AI technologies and applications.

Opus Research is a leading provider of market research, consulting, and advisory services to the global digital communications and collaboration sectors. The company's research focuses on the convergence of emerging technologies, including artificial intelligence (AI), machine learning (ML), and natural language processing (NLP), with the communications and collaboration industries.

Competitor Research is an AI-powered tool that helps businesses analyze and understand their competitors. It provides a comprehensive research report on direct, indirect, substitute, and potential competitors, including insights on search traffic, keywords, backlinks, target audience, pricing strategy, website performance, and customer engagement. The tool uses AI to save time and deliver actionable insights to help businesses grow and stay ahead of the competition.

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 website is an AI tool that offers a wide range of AI, software, and tools for enterprise growth and automation. It provides solutions in areas such as AI hardware, automation, application security, CRM, cloud services, data management, generative AI, network monitoring, process intelligence, proxies, remote monitoring, surveys, sustainability, workload automation, and more. The platform aims to help businesses leverage AI technologies to enhance efficiency, security, and productivity across various industries.

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.

Imagen is an AI application that leverages text-to-image diffusion models to create photorealistic images based on input text. The application utilizes large transformer language models for text understanding and diffusion models for high-fidelity image generation. Imagen has achieved state-of-the-art results in terms of image fidelity and alignment with text. The application is part of Google Research's text-to-image work and focuses on encoding text for image synthesis effectively.

Google Colab is a free Jupyter notebook environment that runs in the cloud. It allows you to write and execute Python code without having to install any software or set up a local environment. Colab notebooks are shareable, so you can easily collaborate with others on projects.

arXiv.org is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.

Prolific is a platform that allows users to quickly find research participants they can trust. It offers a diverse participant pool, including domain experts and API integration. Prolific ensures high-quality human-powered datasets in less than 2 hours, trusted by over 3000 organizations. The platform is designed for ease of use, with self-serve options and scalability. It provides rich, accurate, and comprehensive responses from engaged participants, verified through manual and algorithmic quality checks.

TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. It provides pytorch and **python-first** , low and high level abstractions for RL that are intended to be **efficient** , **modular** , **documented** and properly **tested**. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort.

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.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

veRL is a flexible and efficient reinforcement learning training framework designed for large language models (LLMs). It allows easy extension of diverse RL algorithms, seamless integration with existing LLM infrastructures, and flexible device mapping. The framework achieves state-of-the-art throughput and efficient actor model resharding with 3D-HybridEngine. It supports popular HuggingFace models and is suitable for users working with PyTorch FSDP, Megatron-LM, and vLLM backends.

ScrapeGraphAI is a Python library that uses Large Language Models (LLMs) and direct graph logic to create web scraping pipelines for websites, documents, and XML files. It allows users to extract specific information from web pages by providing a prompt describing the desired data. ScrapeGraphAI supports various LLMs, including Ollama, OpenAI, Gemini, and Docker, enabling users to choose the most suitable model for their needs. The library provides a user-friendly interface through its `SmartScraper` class, which simplifies the process of building and executing scraping pipelines. ScrapeGraphAI is open-source and available on GitHub, with extensive documentation and examples to guide users. It is particularly useful for researchers and data scientists who need to extract structured data from web pages for analysis and exploration.

The 100 Days in AI repository provides a comprehensive roadmap for individuals to learn Artificial Intelligence over a period of 100 days. It covers topics ranging from basic programming in Python to advanced concepts in AI, including machine learning, deep learning, and specialized AI topics. The repository includes daily tasks, resources, and exercises to ensure a structured learning experience. By following this roadmap, users can gain a solid understanding of AI and be prepared to work on real-world AI projects.

AI-resources is a repository containing links to various resources for learning Artificial Intelligence. It includes video lectures, courses, tutorials, and open-source libraries related to deep learning, reinforcement learning, machine learning, and more. The repository categorizes resources for beginners, average users, and advanced users/researchers, providing a comprehensive collection of materials to enhance knowledge and skills in AI.

Godot RL Agents is an open-source package that facilitates the integration of Machine Learning algorithms with games created in the Godot Engine. It provides interfaces for popular RL frameworks, support for memory-based agents, 2D and 3D games, AI sensors, and is licensed under MIT. Users can train agents in the Godot editor, create custom environments, export trained agents in ONNX format, and utilize advanced features like different RL training frameworks.

Awesome-Papers-Autonomous-Agent is a curated collection of recent papers focusing on autonomous agents, specifically interested in RL-based agents and LLM-based agents. The repository aims to provide a comprehensive resource for researchers and practitioners interested in intelligent agents that can achieve goals, acquire knowledge, and continually improve. The collection includes papers on various topics such as instruction following, building agents based on world models, using language as knowledge, leveraging LLMs as a tool, generalization across tasks, continual learning, combining RL and LLM, transformer-based policies, trajectory to language, trajectory prediction, multimodal agents, training LLMs for generalization and adaptation, task-specific designing, multi-agent systems, experimental analysis, benchmarking, applications, algorithm design, and combining with RL.

OpenRL is an open-source general reinforcement learning research framework that supports training for various tasks such as single-agent, multi-agent, offline RL, self-play, and natural language. Developed based on PyTorch, the goal of OpenRL is to provide a simple-to-use, flexible, efficient and sustainable platform for the reinforcement learning research community. It supports a universal interface for all tasks/environments, single-agent and multi-agent tasks, offline RL training with expert dataset, self-play training, reinforcement learning training for natural language tasks, DeepSpeed, Arena for evaluation, importing models and datasets from Hugging Face, user-defined environments, models, and datasets, gymnasium environments, callbacks, visualization tools, unit testing, and code coverage testing. It also supports various algorithms like PPO, DQN, SAC, and environments like Gymnasium, MuJoCo, Atari, and more.

A curated list of open-ended learning AI resources focusing on algorithms that invent new and complex tasks endlessly, inspired by human advancements. The repository includes papers, safety considerations, surveys, perspectives, and blog posts related to open-ended AI research.

LLM4Opt is a collection of references and papers focusing on applying Large Language Models (LLMs) for diverse optimization tasks. The repository includes research papers, tutorials, workshops, competitions, and related collections related to LLMs in optimization. It covers a wide range of topics such as algorithm search, code generation, machine learning, science, industry, and more. The goal is to provide a comprehensive resource for researchers and practitioners interested in leveraging LLMs for optimization tasks.

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.

Pgx is a collection of GPU/TPU-accelerated parallel game simulators for reinforcement learning (RL). It provides JAX-native game simulators for various games like Backgammon, Chess, Shogi, and Go, offering super fast parallel execution on accelerators and beautiful visualization in SVG format. Pgx focuses on faster implementations while also being sufficiently general, allowing environments to be converted to the AEC API of PettingZoo for running Pgx environments through the PettingZoo API.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

The 'awesome-ai4db-paper' repository is a curated paper list focusing on AI for database (AI4DB) theory, frameworks, resources, and tools for data engineers. It includes a collection of research papers related to learning-based query optimization, training data set preparation, cardinality estimation, query-driven approaches, data-driven techniques, hybrid methods, pretraining models, plan hints, cost models, SQL embedding, join order optimization, query rewriting, end-to-end systems, text-to-SQL conversion, traditional database technologies, storage solutions, learning-based index design, and a learning-based configuration advisor. The repository aims to provide a comprehensive resource for individuals interested in AI applications in the field of database management.

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