Best AI tools for< Conduct Reasoning >

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.

Workhub.ai is a website that offers a platform for conducting robot challenge screen tests. Users can assess the security of their site connection through this tool. The site prompts users to enable cookies in their browser settings to access the page.

SanptoBook is a personal accounting software designed to help individuals manage their finances efficiently. It offers features such as invoice and receipt management, reimbursement facilitation, tax filing assistance, bill splitting, and project tracking. The application aims to simplify financial tasks and improve overall financial organization for users. With AI-powered efficiency, SnaptoBook provides state-of-the-art receipt recognition technology and secure cloud storage for all receipts.

Poll the People is an AI-powered consumer research platform that offers 10X more effective surveys powered by ChatGPT. With a human panel of 500,000+ participants, the platform provides insights on brand testing, concept testing, logo testing, content testing, and more. By leveraging AI technology, users can make data-driven decisions, achieve faster insights, and access deep consumer understanding for better decision-making. The platform automates survey analysis, saving time and resources, and offers unbiased and accurate market insights. Poll the People is trusted by top brands worldwide for its efficiency and reliability in consumer research.

Rimo is a human-centered AI writer that helps you create high-quality content, fast. With Rimo, you can write blog posts, articles, website copy, social media posts, and more, in just a few minutes. Rimo's AI is trained on a massive dataset of human-written text, so it can generate content that is both informative and engaging.

The Website Audit AI is an AI-powered tool that offers free website audits and analysis for conversion rate optimization (CRO) and user experience (UX). It provides detailed insights and actionable recommendations to improve user experience and enhance conversion rates. The tool generates real reports based on data from actual users on genuine websites. With a focus on providing accurate and reliable information, Website Audit AI aims to help businesses optimize their online presence and drive better results.

Parroview is a revolutionary AI-powered user research platform that automates the process of conducting user interviews. It uses natural language processing (NLP) to engage with users in real-time conversations, asking follow-up questions and uncovering insights that would be difficult to obtain through traditional methods. Parroview is designed to be fully autonomous, allowing researchers to set up interviews and gather insights without the need for manual intervention. It supports multiple languages, making it accessible to a global audience. Parroview offers a range of features, including the ability to conduct interviews via text or voice, analyze insights in real-time, and generate detailed transcripts. It is suitable for a wide range of research needs, including product validation, consumer behavior analysis, post-purchase evaluations, brand perception studies, and customer persona development.

Wix.com is a website building platform that allows users to create stunning websites with ease. It offers a user-friendly interface with drag-and-drop functionality, making it simple for individuals and businesses to design their online presence. With a wide range of templates and customization options, Wix.com caters to users of all skill levels. Whether you're a beginner looking to build a personal blog or a professional wanting to showcase your portfolio, Wix.com provides the tools and features to bring your vision to life.

Recroo is a fully automated AI interview application that allows users to conduct interviews using artificial intelligence technology. The app is designed to streamline the screening process for recruiters by providing a real-interview like environment, complete feedback with ratings, AI assistant for answering questions, interview transcript review, and interview audio playback. Recroo simplifies the interview process by allowing users to provide job details and custom questions, while the AI engine takes care of conducting the interview. It is a powerful tool for recruiters looking to efficiently screen candidates and focus on other tasks.

EmpathixAI is an innovative AI tool designed to analyze and interpret human emotions through text and voice inputs. The tool uses advanced natural language processing and sentiment analysis algorithms to provide accurate insights into the emotional state of individuals. EmpathixAI helps businesses understand customer feedback, improve communication strategies, and enhance user experiences. With its user-friendly interface and powerful analytics capabilities, EmpathixAI is a valuable tool for companies looking to gain a deeper understanding of customer sentiment and emotions.

SciSpace is an AI-powered tool that helps researchers understand research papers better. It can explain and elaborate most academic texts in simple words. It is a great tool for students, researchers, and anyone who wants to learn more about a particular topic. SciSpace has a user-friendly interface and is easy to use. Simply upload a research paper or enter a URL, and SciSpace will do the rest. It will highlight key concepts, provide definitions, and generate a summary of the paper. SciSpace can also be used to generate citations and find related papers.

Search&AI is a comprehensive platform designed for patent due diligence, offering efficient and accurate results in minutes. It provides services such as prior art search, claim chart generation, novelty diligence analysis, portfolio analysis, document search, and AI-powered chatbot assistance. The platform is built by a team of experienced engineers and is tailored to streamline the patent discovery and analysis process, saving time and money compared to traditional outsourced search firms.

Ziggy is an AI-powered platform that automates 1-on-1 conversations, enabling users to conduct feedback interviews seamlessly with anyone, anytime. It helps businesses gather insightful and more natural responses from users than static forms ever could. Ziggy's commitment to collaboration and creativity helps teams work together to get the best results. Its powerful analytical tools help ensure users understand their users' wants and needs. With Ziggy, users can see the original responses from their users and use them to make better decisions.

Profundo is an AI-powered research assistant that automates data collection, analysis, and reporting. It enables users to conduct in-depth research on various topics efficiently and accurately. With cutting-edge AI algorithms, Profundo minimizes errors, maximizes productivity, and provides user-friendly interface for seamless integration with existing tools. Trusted by professionals, Profundo is used for self-study, content creation, academic research, industry analysis, and more.

Datarails is a financial planning and analysis platform for Excel users. It automates data consolidation, reporting, and planning while enabling finance teams to continue using their spreadsheets and financial models. With Datarails, finance teams can save time on repetitive tasks and focus on strategic insights that drive business growth.

InterviewAI is an AI-powered platform designed to streamline the interview process for both interviewers and candidates. It generates tailored interview questions in real-time, offers advanced practice tools, and provides automated scoring for candidate responses. The platform aims to enhance interview skills, boost confidence, and improve preparation for job seekers, companies, and educational institutions.

LawDroid is an AI legal assistant application designed to help legal professionals work smarter and more efficiently. It serves as a force multiplier, enabling users to accomplish more tasks without additional overhead. LawDroid offers features such as case law research, drafting emails and letters, and document analysis. It also provides benefits like intake new clients, data-driven decisions, automate documents, human in the loop automation, and building scalable tools. The application is used by students, faculty, and legal professionals to streamline legal processes and improve client experiences.

hirex.ai is an AI-powered platform that revolutionizes the job interview process by making it open, fair, and accessible to all. The platform utilizes GenAI assistant to help candidates get interviewed instantly, jump the line to secure their dream job, and undergo a faster screening process. With no credit card required, users can register for free early access, upload their resumes, and post job offers. The platform aims to transform HR and talent acquisition by providing innovative solutions for both job seekers and employers.

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.

UserCue is an AI-powered market research tool that revolutionizes the way businesses conduct interviews and analyze data. It offers intelligent agents for dynamic AI moderated interviews and analysis at scale, enabling users to interact with up to 1,000 key opinion leaders, industry experts, or target populations in just one hour. With a focus on speed and quality, UserCue provides versatile applications, quick turnaround times, and comprehensive reports, all aimed at delivering valuable insights to clients in a fraction of the time traditionally required for market research.

Search-R1 is a tool that trains large language models (LLMs) to reason and call a search engine using reinforcement learning. It is a reproduction of DeepSeek-R1 methods for training reasoning and searching interleaved LLMs, built upon veRL. Through rule-based outcome reward, the base LLM develops reasoning and search engine calling abilities independently. Users can train LLMs on their own datasets and search engines, with preliminary results showing improved performance in search engine calling and reasoning tasks.

LLM Reasoners is a library that enables LLMs to conduct complex reasoning, with advanced reasoning algorithms. It approaches multi-step reasoning as planning and searches for the optimal reasoning chain, which achieves the best balance of exploration vs exploitation with the idea of "World Model" and "Reward". Given any reasoning problem, simply define the reward function and an optional world model (explained below), and let LLM reasoners take care of the rest, including Reasoning Algorithms, Visualization, LLM calling, and more!

Buffer of Thoughts (BoT) is a thought-augmented reasoning framework designed to enhance the accuracy, efficiency, and robustness of large language models (LLMs). It introduces a meta-buffer to store high-level thought-templates distilled from problem-solving processes, enabling adaptive reasoning for efficient problem-solving. The framework includes a buffer-manager to dynamically update the meta-buffer, ensuring scalability and stability. BoT achieves significant performance improvements on reasoning-intensive tasks and demonstrates superior generalization ability and robustness while being cost-effective compared to other methods.

This repository contains the source code and datasets for Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs accepted to ACL 2024. It proposes a framework called Graph Chain-of-thought (Graph-CoT) to enable Language Models to traverse graphs step-by-step for reasoning, interaction, and execution. The motivation is to alleviate hallucination issues in Language Models by augmenting them with structured knowledge sources represented as graphs.

Open Deep Research is an open-source project that serves as a clone of Open AI's Deep Research experiment. It utilizes Firecrawl's extract and search method along with a reasoning model to conduct in-depth research on the web. The project features Firecrawl Search + Extract, real-time data feeding to AI via search, structured data extraction from multiple websites, Next.js App Router for advanced routing, React Server Components and Server Actions for server-side rendering, AI SDK for generating text and structured objects, support for various model providers, styling with Tailwind CSS, data persistence with Vercel Postgres and Blob, and simple and secure authentication with NextAuth.js.

This repository, RAM, focuses on developing advanced algorithms and methods for Reasoning, Alignment, Memory. It contains projects related to these areas and is maintained by a team of individuals. The repository is licensed under the MIT License.

AReaL (Ant Reasoning RL) is an open-source reinforcement learning system developed at the RL Lab, Ant Research. It is designed for training Large Reasoning Models (LRMs) in a fully open and inclusive manner. AReaL provides reproducible experiments for 1.5B and 7B LRMs, showcasing its scalability and performance across diverse computational budgets. The system follows an iterative training process to enhance model performance, with a focus on mathematical reasoning tasks. AReaL is equipped to adapt to different computational resource settings, enabling users to easily configure and launch training trials. Future plans include support for advanced models, optimizations for distributed training, and exploring research topics to enhance LRMs' reasoning capabilities.

This repository contains the implementation of Self-Play of Adversarial Language Game (SPAG) as described in the paper 'Self-playing Adversarial Language Game Enhances LLM Reasoning'. The SPAG involves training Language Models (LLMs) in an adversarial language game called Adversarial Taboo. The repository provides tools for imitation learning, self-play episode collection, and reinforcement learning on game episodes to enhance LLM reasoning abilities. The process involves training models using GPUs, launching imitation learning, conducting self-play episodes, assigning rewards based on outcomes, and learning the SPAG model through reinforcement learning. Continuous improvements on reasoning benchmarks can be observed by repeating the episode-collection and SPAG-learning processes.

MMOS (Mix of Minimal Optimal Sets) is a dataset designed for math reasoning tasks, offering higher performance and lower construction costs. It includes various models and data subsets for tasks like arithmetic reasoning and math word problem solving. The dataset is used to identify minimal optimal sets through reasoning paths and statistical analysis, with a focus on QA-pairs generated from open-source datasets. MMOS also provides an auto problem generator for testing model robustness and scripts for training and inference.

AIW is a code base for experiments and raw data related to Alice in Wonderland, showcasing complete reasoning breakdown in state-of-the-art large language models. Users can collect experiments data using LiteLLM and TogetherAI, and plot the data using provided scripts. The tool allows for executing experiments over LiteLLM and lmsys, with options for different prompt types and AIW variations. The project also includes acknowledgments and a citation for reference.

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

LLM-Geo is an AI-powered geographic information system (GIS) that leverages Large Language Models (LLMs) for automatic spatial data collection, analysis, and visualization. By adopting LLM as the reasoning core, it addresses spatial problems with self-generating, self-organizing, self-verifying, self-executing, and self-growing capabilities. The tool aims to make spatial analysis easier, faster, and more accessible by reducing manual operation time and delivering accurate results through case studies. It uses GPT-4 API in a Python environment and advocates for further research and development in autonomous GIS.

This repository is a rich resource offering examples of synthetic image generation, manipulation, and reasoning using Azure Machine Learning, Computer Vision, OpenAI, and open-source frameworks like Stable Diffusion. It provides practical insights into image processing applications, including content generation, video analysis, avatar creation, and image manipulation with various tools and APIs.

ViGenAiR is a tool that harnesses the power of Generative AI models on Google Cloud Platform to automatically transform long-form Video Ads into shorter variants, targeting different audiences. It generates video, image, and text assets for Demand Gen and YouTube video campaigns. Users can steer the model towards generating desired videos, conduct A/B testing, and benefit from various creative features. The tool offers benefits like diverse inventory, compelling video ads, creative excellence, user control, and performance insights. ViGenAiR works by analyzing video content, splitting it into coherent segments, and generating variants following Google's best practices for effective ads.

The Eureka Framework is an open-source toolkit that leverages advanced Artificial Intelligence and Decentralized Science principles to revolutionize scientific discovery. It enables researchers, developers, and decentralized organizations to explore scientific papers, conduct AI-driven experiments, monetize research contributions, provide token-gated access to AI agents, and customize AI agents for specific research domains. The framework also offers features like a RESTful API, robust scheduler for task automation, and webhooks for real-time notifications, empowering users to automate research tasks, enhance productivity, and foster a committed research community.

InternLM is a powerful language model series with features such as 200K context window for long-context tasks, outstanding comprehensive performance in reasoning, math, code, chat experience, instruction following, and creative writing, code interpreter & data analysis capabilities, and stronger tool utilization capabilities. It offers models in sizes of 7B and 20B, suitable for research and complex scenarios. The models are recommended for various applications and exhibit better performance than previous generations. InternLM models may match or surpass other open-source models like ChatGPT. The tool has been evaluated on various datasets and has shown superior performance in multiple tasks. It requires Python >= 3.8, PyTorch >= 1.12.0, and Transformers >= 4.34 for usage. InternLM can be used for tasks like chat, agent applications, fine-tuning, deployment, and long-context inference.

Astra.ai is a multimodal agent powered by TEN, showcasing its capabilities in speech, vision, and reasoning through RAG from local documentation. It provides a platform for developing AI agents with features like RTC transportation, extension store, workflow builder, and local deployment. Users can build and test agents locally using Docker and Node.js, with prerequisites including Agora App ID, Azure's speech-to-text and text-to-speech API keys, and OpenAI API key. The platform offers advanced customization options through config files and API keys setup, enabling users to create and deploy their AI agents for various tasks.

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.

AIlice is a fully autonomous, general-purpose AI agent that aims to create a standalone artificial intelligence assistant, similar to JARVIS, based on the open-source LLM. AIlice achieves this goal by building a "text computer" that uses a Large Language Model (LLM) as its core processor. Currently, AIlice demonstrates proficiency in a range of tasks, including thematic research, coding, system management, literature reviews, and complex hybrid tasks that go beyond these basic capabilities. AIlice has reached near-perfect performance in everyday tasks using GPT-4 and is making strides towards practical application with the latest open-source models. We will ultimately achieve self-evolution of AI agents. That is, AI agents will autonomously build their own feature expansions and new types of agents, unleashing LLM's knowledge and reasoning capabilities into the real world seamlessly.

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

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