Best AI tools for< Research Industry Data >

AnalyzeMe is an application that allows users to easily conduct PEST analysis. By entering industry and keywords, it provides detailed market environmental analysis. Users can use AnalyzeMe to reassess their business strategies.

Survaii is an AI-powered platform revolutionizing the market research industry by delivering accurate, bias-free data for strategic decision-making. The platform empowers startups and enterprises with cutting-edge tools for survey generation, audience targeting, response bias combat, data analysis, and AI-driven reports with actionable insights. Survaii offers a user-friendly interface, scalable solutions, top-notch security, and innovative features like live video call surveys to provide a seamless and insightful market research experience.

AI News is a website dedicated to providing news, analysis, and insights related to artificial intelligence (AI) technologies. The site covers a wide range of topics within the AI domain, including applications, chatbots, face recognition, virtual assistants, voice recognition, companies like Amazon, Apple, Google, and Microsoft, as well as deep learning, ethics, industries, machine learning, robotics, security, and more. AI News aims to keep readers informed about the latest developments, trends, and innovations in the field of artificial intelligence.

StockGPT is an AI-powered financial research assistant that provides knowledge of earnings releases, financial reports, and fundamental information for S&P 500 and Nasdaq companies. It offers features like AI search, customizable filters, up-to-date data, industry research, and more to help users analyze companies and markets efficiently.

Ledge.ai is an AI application that focuses on the latest trends in artificial intelligence. The platform provides articles, videos, and solutions related to various fields such as business, learning, engineering, academics & study, public, entertainment & art. Users can stay updated on AI developments, including new models like GPT-4o and multi-modal AI. Ledge.ai covers a wide range of topics from OpenAI announcements to academic research and industry applications of AI technology.

Caffe is a deep learning framework developed by Berkeley AI Research (BAIR) and community contributors. It is designed for speed, modularity, and expressiveness, allowing users to define models and optimization through configuration without hard-coding. Caffe supports both CPU and GPU training, making it suitable for research experiments and industry deployment. The framework is extensible, actively developed, and tracks the state-of-the-art in code and models. Caffe is widely used in academic research, startup prototypes, and large-scale industrial applications in vision, speech, and multimedia.

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.

Elicit is an AI research assistant that helps researchers analyze research papers at superhuman speed. It automates time-consuming research tasks such as summarizing papers, extracting data, and synthesizing findings. Trusted by researchers, Elicit offers a plethora of features to speed up the research process and is particularly beneficial for empirical domains like biomedicine and machine learning.

Thales Labs is a premier AI research lab and incubator empowering entrepreneurs and domain experts to revolutionize industries with large language models and web3. They focus on fostering innovation in sectors like Insurance, Finance, Healthcare, Pharma, Law, and Journalism. The user-friendly app allows experts to build AI applications using their natural language skills, with support from skilled engineers for complex challenges. Join Thales Labs to transform industries, unlock new opportunities, and create value with AI-driven innovation.

Maya is an AI-powered data robot that provides personalized answers and insights for enterprise data research. It combines multiple data sources and tools into one, automates tasks, offers smart suggestions, and saves time. Maya understands the specific insights required for each workflow and provides justification for implementation. It can access data from various sources, including internal integrations and external sources, and can translate queries in up to 14 languages. Maya is constantly learning and improving through advanced machine learning and regular updates with new data. It prioritizes data privacy and security, following industry-standard protocols to keep customer data safe.

Derwen is an open-source integration platform for production machine learning in enterprise, specializing in natural language processing, graph technologies, and decision support. It offers expertise in developing knowledge graph applications and domain-specific authoring. Derwen collaborates closely with Hugging Face and provides strong data privacy guarantees, low carbon footprint, and no cloud vendor involvement. The platform aims to empower AI engineers and domain experts with quality, time-to-value, and ownership since 2017.

AiCure provides a patient-centric eClinical trial management platform that enhances drug development through improved medication adherence rates, more powerful analysis and prediction of treatment response using digital biomarkers, and reduced clinical tech burden. AiCure's solutions support traditional, decentralized, or hybrid trials and offer flexibility to meet the needs of various research designs.

CCDS (Center for Computational & Data Sciences) is a research center at Independent University Bangladesh dedicated to artificial intelligence, data sciences, and computational science. The center has various wings focusing on AI, computational biology, physics, data science, human-computer interaction, and industry partnerships. CCDS explores the use of computation to understand nature and society, uncover hidden stories in data, and tackle complex challenges. The center collaborates with institutions like CERN and the Dunlap Institute for Astronomy and Astrophysics.

The Deepfake Detection Challenge Dataset is a project initiated by Facebook AI to accelerate the development of new ways to detect deepfake videos. The dataset consists of over 100,000 videos and was created in collaboration with industry leaders and academic experts. It includes two versions: a preview dataset with 5k videos and a full dataset with 124k videos, each featuring facial modification algorithms. The dataset was used in a Kaggle competition to create better models for detecting manipulated media. The top-performing models achieved high accuracy on the public dataset but faced challenges when tested against the black box dataset, highlighting the importance of generalization in deepfake detection. The project aims to encourage the research community to continue advancing in detecting harmful manipulated media.

AcademicID is an AI-powered platform that helps students and researchers discover and access academic resources. It provides a comprehensive database of academic papers, journals, and other resources, as well as tools to help users organize and manage their research. AcademicID also offers a variety of features to help users collaborate with others and share their research findings.

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.

Aiiot Talk is an AI tool that focuses on Artificial Intelligence, Robotics, Technology, Internet of Things, Machine Learning, Business Technology, Data Security, and Marketing. The platform provides insights, articles, and discussions on the latest trends and applications of AI in various industries. Users can explore how AI is reshaping businesses, enhancing security measures, and revolutionizing technology. Aiiot Talk aims to educate and inform readers about the potential of AI and its impact on society and the future.

The Munich Center for Machine Learning (MCML) is a top spot for AI and ML research in Europe. It is one of six national AI Competence Centers funded by the German and Bavarian government's AI strategy. MCML brings together leading ML researchers from LMU, TUM, and associated institutions to transfer innovations and AI potential to industry and society. The center's vision is to unite leading researchers in Germany to strengthen competence in ML and AI at international, national, and regional levels, fostering talent and making potential accessible to users from various sectors.

Aurora is a market research tool powered by cutting-edge AI technology that provides instant, expert-level business insights for entrepreneurs and startups. It helps users discover their market size, gain valuable customer insights, make data-driven decisions, and stay ahead of the competition by accessing precise, up-to-date data on competitors and customers. Aurora offers a suite of AI market research tools with verified, traceable data to empower users to launch and grow their startups with confidence.

The website is a platform that covers a wide range of topics related to artificial intelligence, big data, machine learning, blockchain, cybersecurity, fintech, gaming, internet of things, startups, and various industries. It provides news, articles, and insights on the latest trends and developments in the field of AI and technology. The platform also offers whitepapers, industry-specific information, and event updates. Users can stay informed about the advancements in AI and related technologies through the content provided on the website.

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.

Phi-3CookBook is a manual on how to use the Microsoft Phi-3 family, which consists of open AI models developed by Microsoft. The Phi-3 models are highly capable and cost-effective small language models, outperforming models of similar and larger sizes across various language, reasoning, coding, and math benchmarks. The repository provides detailed information on different Phi-3 models, their performance, availability, and usage scenarios across different platforms like Azure AI Studio, Hugging Face, and Ollama. It also covers topics such as fine-tuning, evaluation, and end-to-end samples for Phi-3-mini and Phi-3-vision models, along with labs, workshops, and contributing guidelines.

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 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.

Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.

This repository aims to promote global equality for individuals pursuing a PhD in NLP by providing resources and information on various aspects of the academic journey. It covers topics such as applying for a PhD, getting research opportunities, preparing for the job market, and succeeding in academia. The repository is actively updated and includes contributions from experts in the field.

Lance is a modern columnar data format optimized for ML workflows and datasets. It offers high-performance random access, vector search, zero-copy automatic versioning, and ecosystem integrations with Apache Arrow, Pandas, Polars, and DuckDB. Lance is designed to address the challenges of the ML development cycle, providing a unified data format for collection, exploration, analytics, feature engineering, training, evaluation, deployment, and monitoring. It aims to reduce data silos and streamline the ML development process.

MediaAI is a repository containing lectures and materials for Aalto University's AI for Media, Art & Design course. The course is a hands-on, project-based crash course focusing on deep learning and AI techniques for artists and designers. It covers common AI algorithms & tools, their applications in art, media, and design, and provides hands-on practice in designing, implementing, and using these tools. The course includes lectures, exercises, and a final project based on students' interests. Students can complete the course without programming by creatively utilizing existing tools like ChatGPT and DALL-E. The course emphasizes collaboration, peer-to-peer tutoring, and project-based learning. It covers topics such as text generation, image generation, optimization, and game AI.

A curated list of data science & AI guided projects to start building your portfolio. The repository contains guided projects covering various topics such as large language models, time series analysis, computer vision, natural language processing (NLP), and data science. Each project provides detailed instructions on how to implement specific tasks using different tools and technologies.

Zshot is a highly customizable framework for performing Zero and Few shot named entity and relationships recognition. It can be used for mentions extraction, wikification, zero and few shot named entity recognition, zero and few shot named relationship recognition, and visualization of zero-shot NER and RE extraction. The framework consists of two main components: the mentions extractor and the linker. There are multiple mentions extractors and linkers available, each serving a specific purpose. Zshot also includes a relations extractor and a knowledge extractor for extracting relations among entities and performing entity classification. The tool requires Python 3.6+ and dependencies like spacy, torch, transformers, evaluate, and datasets for evaluation over datasets like OntoNotes. Optional dependencies include flair and blink for additional functionalities. Zshot provides examples, tutorials, and evaluation methods to assess the performance of the components.

Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to increase the quality of trained models. The purpose of image augmentation is to create new training samples from the existing data.

Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

Swarms provides simple, reliable, and agile tools to create your own Swarm tailored to your specific needs. Currently, Swarms is being used in production by RBC, John Deere, and many AI startups.

The AI Enablement Stack is a curated collection of venture-backed companies, tools, and technologies that enable developers to build, deploy, and manage AI applications. It provides a structured view of the AI development ecosystem across five key layers: Agent Consumer Layer, Observability and Governance Layer, Engineering Layer, Intelligence Layer, and Infrastructure Layer. Each layer focuses on specific aspects of AI development, from end-user interaction to model training and deployment. The stack aims to help developers find the right tools for building AI applications faster and more efficiently, assist engineering leaders in making informed decisions about AI infrastructure and tooling, and help organizations understand the AI development landscape to plan technology adoption.

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.

YuLan-Mini is a lightweight language model with 2.4 billion parameters that achieves performance comparable to industry-leading models despite being pre-trained on only 1.08T tokens. It excels in mathematics and code domains. The repository provides pre-training resources, including data pipeline, optimization methods, and annealing approaches. Users can pre-train their own language models, perform learning rate annealing, fine-tune the model, research training dynamics, and synthesize data. The team behind YuLan-Mini is AI Box at Renmin University of China. The code is released under the MIT License with future updates on model weights usage policies. Users are advised on potential safety concerns and ethical use of the model.

MindNLP is an open-source NLP library based on MindSpore. It provides a platform for solving natural language processing tasks, containing many common approaches in NLP. It can help researchers and developers to construct and train models more conveniently and rapidly. Key features of MindNLP include: * Comprehensive data processing: Several classical NLP datasets are packaged into a friendly module for easy use, such as Multi30k, SQuAD, CoNLL, etc. * Friendly NLP model toolset: MindNLP provides various configurable components. It is friendly to customize models using MindNLP. * Easy-to-use engine: MindNLP simplified complicated training process in MindSpore. It supports Trainer and Evaluator interfaces to train and evaluate models easily. MindNLP supports a wide range of NLP tasks, including: * Language modeling * Machine translation * Question answering * Sentiment analysis * Sequence labeling * Summarization MindNLP also supports industry-leading Large Language Models (LLMs), including Llama, GLM, RWKV, etc. For support related to large language models, including pre-training, fine-tuning, and inference demo examples, you can find them in the "llm" directory. To install MindNLP, you can either install it from Pypi, download the daily build wheel, or install it from source. The installation instructions are provided in the documentation. MindNLP is released under the Apache 2.0 license. If you find this project useful in your research, please consider citing the following paper: @misc{mindnlp2022, title={{MindNLP}: a MindSpore NLP library}, author={MindNLP Contributors}, howpublished = {\url{https://github.com/mindlab-ai/mindnlp}}, year={2022} }

GOLEM is an open-source AI framework focused on optimization and learning of structured graph-based models using meta-heuristic methods. It emphasizes the potential of meta-heuristics in complex problem spaces where gradient-based methods are not suitable, and the importance of structured models in various problem domains. The framework offers features like structured model optimization, metaheuristic methods, multi-objective optimization, constrained optimization, extensibility, interpretability, and reproducibility. It can be applied to optimization problems represented as directed graphs with defined fitness functions. GOLEM has applications in areas like AutoML, Bayesian network structure search, differential equation discovery, geometric design, and neural architecture search. The project structure includes packages for core functionalities, adapters, graph representation, optimizers, genetic algorithms, utilities, serialization, visualization, examples, and testing. Contributions are welcome, and the project is supported by ITMO University's Research Center Strong Artificial Intelligence in Industry.

Writes as a crypto journalist with insights, analysis, and visuals, in UK English.

Expert in industry analysis and data interpretation.

Designed to Provide Comprehensive Insights from the AI industry from Reputable Sources.

专注于“投资策略”类型的研报分析总结，提炼对行业配置的核心观点 Focusing on the analysis and summary of research reports on the type of "investment strategy", refining core perspectives on industry allocation

Company Profile Builder and Analyzer

EXPERIMENT - Friendly expert on NYC dog license data from 2015-2016, with info on names, breeds and boroughs

Analyzes annual reports, focusing on specific user-provided data

Retrieves hunting-related data for each state. Providing insightful data analysis on trends in hunting statistics. (beta)

Insightful Analyst of China's NEV Industry

Enter a software vendor's domain or company name to receive comprehensive company analysis and insight. Let VendorInsights bootstrap your research.

Focuses on pioneers of the cable TV industry.

Expert in retail industry discussions and consumer goods insights.

提供深入且全面的行业分析

GICS is a classification standard developed by MSCI and S&P Dow Jones Indices. This GPT is not a MSCI and S&P product. Official website : https://www.msci.com/our-solutions/indexes/gics

Provides latest AI news and global events updates.

Channels Kunal Shah's tweet essence

Detailed information about the Architect Advirsory market in Denmark

Cannabis investment AI expert, delivering clear, adaptable, and comprehensive guidance.

Adversarial AI expert aiding in AI red teaming, informed by cutting-edge industry research (early dev)

Your personal Librarian. Knows a lot about books and their publishing history!