Best AI tools for< Hazard Identification >

The European Agency for Safety and Health at Work (EU-OSHA) is an EU agency that provides information, statistics, legislation, and risk assessment tools on occupational safety and health (OSH). The agency's mission is to make Europe's workplaces safer, healthier, and more productive.

Visionify.ai is an advanced Vision AI application designed to enhance workplace safety and compliance through AI-driven surveillance. The platform offers over 60 Vision AI scenarios for hazard warnings, worker health, compliance policies, environment monitoring, vehicle monitoring, and suspicious activity detection. Visionify.ai empowers EHS professionals with continuous monitoring, real-time alerts, proactive hazard identification, and privacy-focused data security measures. The application transforms ordinary cameras into vigilant protectors, providing instant alerts and video analytics tailored to safety needs.

SWMS AI is an AI-powered safety risk assessment tool that helps businesses streamline compliance and improve safety. It leverages a vast knowledge base of occupational safety resources, codes of practice, risk assessments, and safety documents to generate risk assessments tailored specifically to a project, trade, and industry. SWMS AI can be customized to a company's policies to align its AI's document generation capabilities with proprietary safety standards and requirements.

Drive AI is an AI-powered application designed to enhance driving experiences by providing real-time alerts, predictive route suggestions, and police presence notifications. The app aims to help users maintain safe driving speeds, avoid unnecessary stops, and stay informed about traffic conditions. By leveraging AI technology, Drive AI offers voice and text updates powered by ChatGPT, ensuring that users can receive alerts without distractions. With limited early adopter offers available, Drive AI is a valuable tool for drivers looking to navigate roads efficiently and safely.

SEA.AI is an AI tool that provides Machine Vision for Safety at Sea. It utilizes the latest camera technology combined with artificial intelligence to detect and classify objects on the surface of the water, including unsignalled craft, floating obstacles, buoys, kayaks, and persons overboard. The application offers various solutions for sailing, commercial, motor, maritime surveillance, search & rescue, and government sectors. SEA.AI aims to enhance safety and convenience for sailors by leveraging AI technology for early detection of potential hazards at sea.

BlazeGard is an AI-powered fire safety application that utilizes cutting-edge object detection technology to analyze video feeds in real-time, identifying potential fire hazards and smoke before flames erupt. It offers comprehensive protection for homes, businesses, and industrial facilities, going beyond traditional smoke detectors. BlazeGard provides early detection, real-time alerts, and peace of mind through its proactive approach to fire safety.

Copilot is a smart tech application designed for cyclists to enhance safety and provide peace of mind while riding. It leverages artificial intelligence to constantly monitor the road behind the cyclist, alerting them to approaching vehicles and potential dangers. Through a combination of audible and visual alerts, Copilot aims to prevent crashes and ensure a safer cycling experience. The application also features an Ultimate Protection System that records rides with video evidence, customizable audio alerts, and reactive light patterns to communicate with drivers. Copilot's advanced AI technology anticipates hazards before they occur, offering a comprehensive safety solution for cyclists.

Construction-Hazard-Detection is an AI-driven tool focused on improving safety at construction sites by utilizing the YOLOv8 model for object detection. The system identifies potential hazards like overhead heavy loads and steel pipes, providing real-time analysis and warnings. Users can configure the system via a YAML file and run it using Docker. The primary dataset used for training is the Construction Site Safety Image Dataset enriched with additional annotations. The system logs are accessible within the Docker container for debugging, and notifications are sent through the LINE messaging API when hazards are detected.

The 'bookmark-summary' repository reads bookmarks from 'bookmark-collection', extracts text content using Jina Reader, and then summarizes the text using LLM. The detailed implementation can be found in 'process_changes.py'. It needs to be used together with the Github Action in 'bookmark-collection'.

Recent LLM (Large Language Models)-based CV and multi-modal works.

This repository contains custom recipes for H2O Driverless AI, which is an Automatic Machine Learning platform for the Enterprise. Custom recipes are Python code snippets that can be uploaded into Driverless AI at runtime to automate feature engineering, model building, visualization, and interpretability. Users can gain control over the optimization choices made by Driverless AI by providing their own custom recipes. The repository includes recipes for various tasks such as data manipulation, data preprocessing, feature selection, data augmentation, model building, scoring, and more. Best practices for creating and using recipes are also provided, including security considerations, performance tips, and safety measures.

LARS is an application that enables users to run Large Language Models (LLMs) locally on their devices, upload their own documents, and engage in conversations where the LLM grounds its responses with the uploaded content. The application focuses on Retrieval Augmented Generation (RAG) to increase accuracy and reduce AI-generated inaccuracies. LARS provides advanced citations, supports various file formats, allows follow-up questions, provides full chat history, and offers customization options for LLM settings. Users can force enable or disable RAG, change system prompts, and tweak advanced LLM settings. The application also supports GPU-accelerated inferencing, multiple embedding models, and text extraction methods. LARS is open-source and aims to be the ultimate RAG-centric LLM application.

ModelBench is a tool for running safety benchmarks against AI models and generating detailed reports. It is part of the MLCommons project and is designed as a proof of concept to aggregate measures, relate them to specific harms, create benchmarks, and produce reports. The tool requires LlamaGuard for evaluating responses and a TogetherAI account for running benchmarks. Users can install ModelBench from GitHub or PyPI, run tests using Poetry, and create benchmarks by providing necessary API keys. The tool generates static HTML pages displaying benchmark scores and allows users to dump raw scores and manage cache for faster runs. ModelBench is aimed at enabling users to test their own models and create tests and benchmarks.

OneKE is a flexible dockerized system for schema-guided knowledge extraction, capable of extracting information from the web and raw PDF books across multiple domains like science and news. It employs a collaborative multi-agent approach and includes a user-customizable knowledge base to enable tailored extraction. OneKE offers various IE tasks support, data sources support, LLMs support, extraction method support, and knowledge base configuration. Users can start with examples using YAML, Python, or Web UI, and perform tasks like Named Entity Recognition, Relation Extraction, Event Extraction, Triple Extraction, and Open Domain IE. The tool supports different source formats like Plain Text, HTML, PDF, Word, TXT, and JSON files. Users can choose from various extraction models like OpenAI, DeepSeek, LLaMA, Qwen, ChatGLM, MiniCPM, and OneKE for information extraction tasks. Extraction methods include Schema Agent, Extraction Agent, and Reflection Agent. The tool also provides support for schema repository and case repository management, along with solutions for network issues. Contributors to the project include Ningyu Zhang, Haofen Wang, Yujie Luo, Xiangyuan Ru, Kangwei Liu, Lin Yuan, Mengshu Sun, Lei Liang, Zhiqiang Zhang, Jun Zhou, Lanning Wei, Da Zheng, and Huajun Chen.

Purple Llama is an umbrella project that aims to provide tools and evaluations to support responsible development and usage of generative AI models. It encompasses components for cybersecurity and input/output safeguards, with plans to expand in the future. The project emphasizes a collaborative approach, borrowing the concept of purple teaming from cybersecurity, to address potential risks and challenges posed by generative AI. Components within Purple Llama are licensed permissively to foster community collaboration and standardize the development of trust and safety tools for generative AI.

The `Call-for-Reviewers` repository aims to collect the latest 'call for reviewers' links from various top CS/ML/AI conferences/journals. It provides an opportunity for individuals in the computer/ machine learning/ artificial intelligence fields to gain review experience for applying for NIW/H1B/EB1 or enhancing their CV. The repository helps users stay updated with the latest research trends and engage with the academic community.

This repository provides a comprehensive list of academic papers, articles, tutorials, slides, and projects related to Large Language Model (LLM) systems. It covers various aspects of LLM research, including pre-training, serving, system efficiency optimization, multi-model systems, image generation systems, LLM applications in systems, ML systems, survey papers, LLM benchmarks and leaderboards, and other relevant resources. The repository is regularly updated to include the latest developments in this rapidly evolving field, making it a valuable resource for researchers, practitioners, and anyone interested in staying abreast of the advancements in LLM technology.

Checking for safety in household items.

A construction safety expert providing OSHA-cited advice

Generates risk maps, emergency response plans and safety protocols for disaster management professionals.

Upload a site photo to assess the potential hazard and seek advises from experience AI Safety Officer

Informative guide on asbestos hazards and removal

Expert on OSHA standards, ideal for safety professionals.

Informative assistant for safety and emergency training.

Friendly advisor for workplace safety.

Coastal professor at your service, explaining coastal and oceanic processes.

Food quality inspection expert for analyzing food images.

Specialist in HACCP food safety management systems

Leading-edge Traffic and Road Safety Optimizer, powered by OpenAI

Your expert caddy for hole-specific golf strategies.

Informs about product recalls in various industries, focusing on consumer safety.

Physical Geology expert

Expert in geology, providing detailed and accurate information from a vast resource base.