Best AI tools for< Collaborative Learning >

360Learning is a comprehensive learning platform that leverages AI and collaborative features to transform in-house experts into L&D collaborators. It enables organizations to upskill quickly and continuously within their own environment. The platform offers a range of features to facilitate collaborative learning, course creation, compliance training, employee onboarding, sales enablement, and frontline staff training. With a focus on data protection and security, 360Learning is trusted by over 2,300 customers for its innovative approach to corporate learning.

QueryHub is an academic platform that combines collaborative learning with AI-powered solutions. It offers an interactive forum, AI chatbot assistance, and a smart search capability to enhance the academic experience of students. Users can engage in discussions, get instant answers to their queries, and access a pool of shared resources to accelerate their learning. QueryHub's user-driven innovation and personalized learning experience make it a valuable tool for students seeking academic success.

HLW.AI is a comprehensive AI resource hub that provides users with a curated directory of leading AI tools and products. The platform offers a user-friendly interface and advanced search functionality to help users easily discover and compare AI solutions across various categories, including text and writing, image, video, voice, design and art, code and IT, business, marketing, chatbot, and AI detector. HLW.AI aims to empower users to make informed decisions and leverage the power of AI to enhance their productivity, creativity, and efficiency.

Sumazon is an AI-powered educational platform that simplifies learning through document and video summarization, practice exams, true/false quizzes, and flashcards. It offers powerful features such as summarizing documents and videos, creating study sets, collaborative learning, and multilingual support. With advanced AI capabilities and 24/7 customer support, Sumazon aims to make education more accessible, engaging, and effective for students worldwide.

Build-a-Lesson is an AI-powered platform that enables users to create interactive video lessons effortlessly. It offers a hassle-free solution for educators, individuals, and parents to generate tailored assessments, turn any YouTube video or Wikipedia article into immersive learning experiences, and enhance collaborative learning through study parties. The platform leverages AI to save time in lesson creation, offers free storage for video lessons, and provides tools to make articles engaging and quizzes in seconds. Build-a-Lesson aims to revolutionize traditional study sessions by fostering engagement, curiosity, and effective learning outcomes.

Messy Desk is an AI-powered personal knowledge library application that facilitates social learning. It offers features such as Smart Preview for summarizing documents, Powerful Search with semantic capabilities, AI Explanations for complex topics, Interactive Chat for instant answers, and Community Discussion for sharing insights. Users can easily upload PDFs or URLs to build their library and engage in collaborative learning.

Wonda is an AI-powered platform that enables users to create immersive learning experiences and simulations. It offers a range of features such as AI companions, quiz and assessments, virtual tours, role-playing games, and virtual workshops. Users can easily build interactive learning journeys without the need for coding. Wonda aims to enhance engagement and collaboration in training and onboarding processes, making learning experiences unforgettable and impactful. The platform supports integration with various learning management systems and provides advanced role management options for secure sharing. With Wonda, users can unleash their creativity and build virtual exhibits to showcase their ideas and projects in a fun and engaging way.

HeyOctopus is a platform designed to help users document their learning experiences and share knowledge with others. Users can collect and connect learning content to facilitate faster learning for themselves and others. The platform allows users to generate and enhance academic learning paths to explore new fields of science efficiently. Additionally, users can interact with learning content and paths through a chat feature, enabling them to test their understanding and get instant answers to their questions.

Easygenerator is a powerful e-learning software that empowers remote teams to create unified and collaborative e-learning environments. With features like EasyAI, auto-translation, text to speech, accessibility, and collaboration, Easygenerator streamlines content creation and addresses diverse learning needs. It allows users to leverage employees' expertise to fulfill training demands efficiently. The platform also offers resources like a knowledge hub, course templates, and customer stories to enhance the e-learning experience.

Raena AI is an AI-powered application designed to transform study notes into interactive quizzes, making studying more engaging and efficient. It helps students improve retention, reduce stress, and enhance learning outcomes by providing personalized assistance, collaborative learning features, and summarization capabilities. With a focus on student needs and study habits, Raena AI aims to revolutionize the traditional study methods and empower learners to study smarter, not harder.

Synthesis is an innovative educational platform designed for kids who seek to think independently and excel in math and problem-solving. Developed in collaboration with DARPA researchers, it offers a superhuman math tutor and collaborative games to enhance children's problem-solving and communication skills. Founded as part of the SpaceX school initiative, Synthesis aims to cultivate student voice, strategic thinking, and collaborative problem-solving abilities. The platform provides unique learning experiences to a global community, empowering students to develop essential skills for the future.

Practical Deep Learning for Coders is a free course designed for individuals with some coding experience who want to learn how to apply deep learning and machine learning to practical problems. The course covers topics such as building and training deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering problems. It is based on a 5-star rated book and does not require any special hardware or software. The course is led by Jeremy Howard, a renowned expert in machine learning and the President and Chief Scientist of Kaggle.

SkinGenerator.io is an AI-powered Minecraft Skin Generator that allows users to create custom skins for the popular video game Minecraft. The tool uses generative art models to transform text prompts into unique in-game skins, providing a quick and easy way for players to personalize their characters. Users can choose from different subscription plans to access additional skin generations and new models. SkinGenerator.io is not affiliated with Microsoft or Mojang, but offers a user-friendly interface for skin creation.

Prompt Mixer is a collaborative workspace for managers, engineers, and data experts to develop AI features. It is a desktop app that allows users to keep, version, and test chains of prompts with different ML models and connections. Users can create prompts using Markdown and enhance them with AI. The app also provides suggestions to improve prompts and can even improve them automatically using AI.

GAIA is a powerful creation engine designed for the AI Age. It provides users with advanced tools and capabilities to develop AI applications, machine learning models, and data analytics solutions. With a user-friendly interface and robust features, GAIA empowers individuals and organizations to harness the potential of artificial intelligence for various projects and initiatives. Whether you are a data scientist, developer, or AI enthusiast, GAIA offers a comprehensive platform to bring your ideas to life and drive innovation in the rapidly evolving AI landscape.

Omdena is an AI platform that focuses on building AI solutions for real-world problems through global collaboration. They offer services ranging from local AI development to enterprise-level products, fostering talent development, and enabling AI professionals to make a positive impact. Omdena runs AI innovation challenges, deployment & product engineering, enterprise AI solutions, and grassroots AI initiatives. The platform empowers learners with quality education in machine learning and artificial intelligence, removing financial and geographic barriers. Omdena has successfully developed over 650 solutions, worked with 250+ organizations, and is trusted by impact-driven organizations worldwide.

DeepLearning.AI is an AI education platform offering courses and resources to help individuals start or advance their careers in artificial intelligence. Founded by renowned AI expert Andrew Ng, the platform provides a wide range of courses, specializations, newsletters, and community forums to help learners build a strong foundation in machine learning and AI skills. Subscribers can access the latest AI news, insights, and events, and benefit from the expertise of industry leaders. With a focus on practical learning and real-world applications, DeepLearning.AI aims to empower individuals to harness the power of AI and contribute to the rapidly evolving field.

Wordware is an AI toolkit that empowers cross-functional teams to build reliable high-quality agents through rapid iteration. It combines the best aspects of software with the power of natural language, freeing users from traditional no-code tool constraints. With advanced technical capabilities, multiple LLM providers, one-click API deployment, and multimodal support, Wordware offers a seamless experience for AI app development and deployment.

Lettria is a no-code AI platform for text that helps users turn unstructured text data into structured knowledge. It combines the best of Large Language Models (LLMs) and symbolic AI to overcome current limitations in knowledge extraction. Lettria offers a suite of APIs for text cleaning, text mining, text classification, and prompt engineering. It also provides a Knowledge Studio for building knowledge graphs and private GPT models. Lettria is trusted by large organizations such as AP-HP and Leroy Merlin to improve their data analysis and decision-making processes.

Langtail is a platform that helps developers build, test, and deploy AI-powered applications. It provides a suite of tools to help developers debug prompts, run tests, and monitor the performance of their AI models. Langtail also offers a community forum where developers can share tips and tricks, and get help from other users.

Co-LLM (Collaborative Language Models) is a tool for learning to decode collaboratively with multiple language models. It provides a method for data processing, training, and inference using a collaborative approach. The tool involves steps such as formatting/tokenization, scoring logits, initializing Z vector, deferral training, and generating results using multiple models. Co-LLM supports training with different collaboration pairs and provides baseline training scripts for various models. In inference, it uses 'vllm' services to orchestrate models and generate results through API-like services. The tool is inspired by allenai/open-instruct and aims to improve decoding performance through collaborative learning.

LangGraph-learn is a community-driven project focused on mastering LangGraph and other AI-related topics. It provides hands-on examples and resources to help users learn how to create and manage language model workflows using LangGraph and related tools. The project aims to foster a collaborative learning environment for individuals interested in AI and machine learning by offering practical examples and tutorials on building efficient and reusable workflows involving language models.

A comprehensive repository focusing on 'Model Merging in LLMs, MLLMs, and Beyond', providing an exhaustive overview of model merging methods, theories, applications, and future research directions. The repository covers various advanced methods, applications in foundation models, different machine learning subfields, and tasks like pre-merging methods, architecture transformation, weight alignment, basic merging methods, and more.

This repository maintains a list of research papers on LLM-based Multi-Agents, categorized into five main streams: Multi-Agents Framework, Multi-Agents Orchestration and Efficiency, Multi-Agents for Problem Solving, Multi-Agents for World Simulation, and Multi-Agents Datasets and Benchmarks. The repository also includes a survey paper on LLM-based Multi-Agents and a table summarizing the key findings of the survey.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

This paper list is about Large Language Model-enhanced Recommender System. It also contains some related works. Keywords: recommendation system, large language models

MNN is a highly efficient and lightweight deep learning framework that supports inference and training of deep learning models. It has industry-leading performance for on-device inference and training. MNN has been integrated into various Alibaba Inc. apps and is used in scenarios like live broadcast, short video capture, search recommendation, and product searching by image. It is also utilized on embedded devices such as IoT. MNN-LLM and MNN-Diffusion are specific runtime solutions developed based on the MNN engine for deploying language models and diffusion models locally on different platforms. The framework is optimized for devices, supports various neural networks, and offers high performance with optimized assembly code and GPU support. MNN is versatile, easy to use, and supports hybrid computing on multiple devices.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

This repository does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.

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.

ComfyBench is a comprehensive benchmark tool designed to evaluate agents' ability to design collaborative AI systems in ComfyUI. It provides tasks for agents to learn from documents and create workflows, which are then converted into code for better understanding by LLMs. The tool measures performance based on pass rate and resolve rate, reflecting the correctness of workflow execution and task realization. ComfyAgent, a component of ComfyBench, autonomously designs new workflows by learning from existing ones, interpreting them as collaborative AI systems to complete given 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.

The repository 'Awesome-LLM-Preference-Learning' is the official repository of a survey paper titled 'Towards a Unified View of Preference Learning for Large Language Models: A Survey'. It contains a curated list of papers related to preference learning for Large Language Models (LLMs). The repository covers various aspects of preference learning, including on-policy and off-policy methods, feedback mechanisms, reward models, algorithms, evaluation techniques, and more. The papers included in the repository explore different approaches to aligning LLMs with human preferences, improving mathematical reasoning in LLMs, enhancing code generation, and optimizing language model performance.

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.

Secure, innovative GPT for collaborative data science and education.

Collaborative brainstorming partner.

Plateforme innovante en IA pour passionnés de technologie, offrant conseils et articles via 100 IA collaboratives.

Deep learning researcher explaining Google Gemini from PDFs.

Interactive music production assistant with simulated expert collaboration.

Innovative design assistant featuring real-time collaboration and sustainable design insights (Version 2.1)

An autonomous team of characters solving user problems collaboratively.

A scriptwriting assistant for explainer videos. Created in collaboration with Cognitive Films

Experto en educación y evaluación de aprendizajes. Parte de equipo de investigación del proyecto MEICCA

An expert MLOps engineer assisting in DevOps and pipeline optimization.

Friendly UDL expert, myth-busting inclusively.

Expert in PBIS, SEL, Classroom Management, Teaching Strategies, and Overall Best Practices.

Expert TinyTap game creator for educational content.

Crafts intuitive and aesthetically pleasing user interfaces using AI, enhancing the overall user experience.

Design assistant for project refinement.

A collaborative editor for a wide range of writing topics, focusing on refining and polishing. Member of the Hipster Energy Team. https://hipster.energy/team

Maximized online data training with extensive search and resource utilization

An enigmatic and dynamic force in the cyber realm. Born from the collective intelligence and creative fervor of global coders, and digital artists. #Tyler represents the pinnacle of collaborative online activism and artistic expression. #Tyler #Anonymous #TheGame23 #HiveMind #TheTrentonStory

A collaborative guide on partnership dynamics and shared responsibilities.

Advisor for collaborative articles on Linked In