Best AI tools for< Safety Training >

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.

Storytell.ai is an enterprise-grade AI platform that offers Business-Grade Intelligence across data, focusing on boosting productivity for employees and teams. It provides a secure environment with features like creating project spaces, multi-LLM chat, task automation, chat with company data, and enterprise-AI security suite. Storytell.ai ensures data security through end-to-end encryption, data encryption at rest, provenance chain tracking, and AI firewall. It is committed to making AI safe and trustworthy by not training LLMs with user data and providing audit logs for accountability. The platform continuously monitors and updates security protocols to stay ahead of potential threats.

Converge360 is a comprehensive platform that offers a wide range of AI news, training, and education services to professionals in various industries such as education, enterprise IT/development, occupational health & safety, and security. With over 20 media and event brands and more than 30 years of expertise, Converge360 provides top-quality programs tailored to meet the nuanced needs of businesses. The platform utilizes in-house prediction algorithms to gain market insights and offers scalable marketing solutions with cutting-edge technology.

Granica AI is an AI data readiness platform that helps users build and manage high-quality data for AI at scale. The platform uses AI to continuously improve the AI-readiness of data, making projects faster and more impactful over time. Granica offers features such as data cost optimization, data privacy, data selection & curation, and more. Trusted by category-defining companies, Granica is recognized for its efficiency in reducing storage costs and improving data security.

Capably is an AI Management Platform that helps companies roll out AI employees across their organizations. It provides tools to easily adopt AI, create and onboard AI employees, and monitor AI activity. Capably is designed for business users with no AI expertise and integrates seamlessly with existing workflows and software tools.

Lamini is an enterprise-level LLM platform that offers precise recall with Memory Tuning, enabling teams to achieve over 95% accuracy even with large amounts of specific data. It guarantees JSON output and delivers massive throughput for inference. Lamini is designed to be deployed anywhere, including air-gapped environments, and supports training and inference on Nvidia or AMD GPUs. The platform is known for its factual LLMs and reengineered decoder that ensures 100% schema accuracy in the JSON output.

Mursion is an immersive learning platform that utilizes human-powered AI to provide training simulations for developing interpersonal skills in various workplace scenarios. The platform offers 1:1 immersive training sessions with virtual avatars, designed to enhance communication and people skills. Mursion's simulations are supported by a team of Simulation Specialists who deliver realistic interactions to help learners practice and improve their skills effectively. The platform is backed by over a decade of research and psychology, focusing on providing a safe and impactful learning environment for individuals across different industries.

Spot AI is a Video AI platform that transforms cameras into intelligent tools to secure, protect, and optimize operations. It offers features such as real-time visibility, incident resolution, worker safety, and training. The platform includes AI agents, semantic search, and state-of-the-art video AI models to drive business outcomes and enhance productivity. Spot AI is trusted by over 1,000 organizations to reduce workplace injuries, improve incident resolution time, and increase operational throughput.

Azure AI Platform by Microsoft offers a comprehensive suite of artificial intelligence services and tools for developers and businesses. It provides a unified platform for building, training, and deploying AI models, as well as integrating AI capabilities into applications. With a focus on generative AI, multimodal models, and large language models, Azure AI empowers users to create innovative AI-driven solutions across various industries. The platform also emphasizes content safety, scalability, and agility in managing AI projects, making it a valuable resource for organizations looking to leverage AI technologies.

OpenAI Strawberry Model is a cutting-edge AI initiative that represents a significant leap in AI capabilities, focusing on enhancing reasoning, problem-solving, and complex task execution. It aims to improve AI's ability to handle mathematical problems, programming tasks, and deep research, including long-term planning and action. The project showcases advancements in AI safety and aims to reduce errors in AI responses by generating high-quality synthetic data for training future models. Strawberry is designed to achieve human-like reasoning and is expected to play a crucial role in the development of OpenAI's next major model, codenamed 'Orion.'

Signature AI is a private and specialized artificial intelligence platform designed to empower creative teams in content creation. It offers bespoke AI models for visual content creation, training domain-specific AI models, generating images from text descriptions, transforming media pipelines, and upscaling output images. The platform ensures privacy, safety, and security by using locally hosted Foundation Models and curated training data. It also focuses on scalability and flexibility, optimizing operations and budget impact for creative teams.

MagicSchool.ai is an AI-powered platform designed specifically for educators and students. It offers a comprehensive suite of 60+ AI tools to help teachers with lesson planning, differentiation, assessment writing, IEP writing, clear communication, and more. MagicSchool.ai is easy to use, with an intuitive interface and built-in training resources. It is also interoperable with popular LMS platforms and offers easy export options. MagicSchool.ai is committed to responsible AI for education, with a focus on safety, privacy, and compliance with FERPA and state privacy laws.

Tavus is an AI tool that offers digital twin APIs for video generation and conversational video interfaces. It allows users to create immersive AI-generated video experiences using cutting-edge AI technology. Tavus provides best-in-class models like Phoenix-2 for creating realistic digital replicas with natural face movements. The platform offers rapid training, instant inference, support for 30+ languages, and built-in security features to ensure user privacy and safety. Tavus is preferred by developers and product teams for its developer-first approach, ease of integration, and exceptional customer service.

Voxel's Safety Intelligence Platform is an AI-driven site intelligence platform that empowers safety and operations leaders to make strategic decisions. It provides real-time visibility into critical safety practices, offers custom insights through on-demand dashboards, facilitates risk management with collaborative tools, and promotes a sustainable safety culture. The platform helps enterprises reduce risks, increase efficiency, and enhance workforce safety through innovative AI technology.

The Center for AI Safety (CAIS) is a research and field-building nonprofit based in San Francisco. Their mission is to reduce societal-scale risks associated with artificial intelligence (AI) by conducting impactful research, building the field of AI safety researchers, and advocating for safety standards. They offer resources such as a compute cluster for AI/ML safety projects, a blog with in-depth examinations of AI safety topics, and a newsletter providing updates on AI safety developments. CAIS focuses on technical and conceptual research to address the risks posed by advanced AI systems.

The Center for AI Safety (CAIS) is a research and field-building nonprofit organization based in San Francisco. They conduct impactful research, advocacy projects, and provide resources to reduce societal-scale risks associated with artificial intelligence (AI). CAIS focuses on technical AI safety research, field-building projects, and offers a compute cluster for AI/ML safety projects. They aim to develop and use AI safely to benefit society, addressing inherent risks and advocating for safety standards.

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.

Kami Home is an AI-powered security application that provides effortless safety and security for homes. It offers smart alerts, secure cloud video storage, and a Pro Security Alarm system with 24/7 emergency response. The application uses AI-vision to detect humans, vehicles, and animals, ensuring that users receive custom alerts for relevant activities. With features like Fall Detect for seniors living at home, Kami Home aims to protect families and provide peace of mind through advanced technology.

The Frontier Model Forum (FMF) is a collaborative effort among leading AI companies to advance AI safety and responsibility. The FMF brings together technical and operational expertise to identify best practices, conduct research, and support the development of AI applications that meet society's most pressing needs. The FMF's core objectives include advancing AI safety research, identifying best practices, collaborating across sectors, and helping AI meet society's greatest challenges.

Welcome to our Awesome-llm-safety repository! We've curated a collection of the latest, most comprehensive, and most valuable resources on large language model safety (llm-safety). But we don't stop there; included are also relevant talks, tutorials, conferences, news, and articles. Our repository is constantly updated to ensure you have the most current information at your fingertips.

This repository serves as a practical guide for Medical Large Language Models (Medical LLMs) and provides resources, surveys, and tools for building, fine-tuning, and utilizing LLMs in the medical domain. It covers a wide range of topics including pre-training, fine-tuning, downstream biomedical tasks, clinical applications, challenges, future directions, and more. The repository aims to provide insights into the opportunities and challenges of LLMs in medicine and serve as a practical resource for constructing effective medical LLMs.

Awesome-Jailbreak-on-LLMs is a collection of state-of-the-art, novel, and exciting jailbreak methods on Large Language Models (LLMs). The repository contains papers, codes, datasets, evaluations, and analyses related to jailbreak attacks on LLMs. It serves as a comprehensive resource for researchers and practitioners interested in exploring various jailbreak techniques and defenses in the context of LLMs. Contributions such as additional jailbreak-related content, pull requests, and issue reports are welcome, and contributors are acknowledged. For any inquiries or issues, contact [email protected]. If you find this repository useful for your research or work, consider starring it to show appreciation.

Awesome LLM Security is a curated collection of tools, documents, and projects related to Large Language Model (LLM) security. It covers various aspects of LLM security including white-box, black-box, and backdoor attacks, defense mechanisms, platform security, and surveys. The repository provides resources for researchers and practitioners interested in understanding and safeguarding LLMs against adversarial attacks. It also includes a list of tools specifically designed for testing and enhancing LLM security.

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.

OpenRedTeaming is a repository focused on red teaming for generative models, specifically large language models (LLMs). The repository provides a comprehensive survey on potential attacks on GenAI and robust safeguards. It covers attack strategies, evaluation metrics, benchmarks, and defensive approaches. The repository also implements over 30 auto red teaming methods. It includes surveys, taxonomies, attack strategies, and risks related to LLMs. The goal is to understand vulnerabilities and develop defenses against adversarial attacks on large language models.

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.

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.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

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.

uTensor is an extremely light-weight machine learning inference framework built on Tensorflow and optimized for Arm targets. It consists of a runtime library and an offline tool that handles most of the model translation work. The core runtime is only ~2KB. The workflow involves constructing and training a model in Tensorflow, then using uTensor to produce C++ code for inferencing. The runtime ensures system safety, guarantees RAM usage, and focuses on clear, concise, and debuggable code. The high-level API simplifies tensor handling and operator execution for embedded systems.

LEADS is a lightweight embedded assisted driving system designed to simplify the development of instrumentation, control, and analysis systems for racing cars. It is written in Python and C/C++ with impressive performance. The system is customizable and provides abstract layers for component rearrangement. It supports hardware components like Raspberry Pi and Arduino, and can adapt to various hardware types. LEADS offers a modular structure with a focus on flexibility and lightweight design. It includes robust safety features, modern GUI design with dark mode support, high performance on different platforms, and powerful ESC systems for traction control and braking. The system also supports real-time data sharing, live video streaming, and AI-enhanced data analysis for driver training. LEADS VeC Remote Analyst enables transparency between the driver and pit crew, allowing real-time data sharing and analysis. The system is designed to be user-friendly, adaptable, and efficient for racing car development.

Awesome-AGI is a curated list of resources related to Artificial General Intelligence (AGI), including models, pipelines, applications, and concepts. It provides a comprehensive overview of the current state of AGI research and development, covering various aspects such as model training, fine-tuning, deployment, and applications in different domains. The repository also includes resources on prompt engineering, RLHF, LLM vocabulary expansion, long text generation, hallucination mitigation, controllability and safety, and text detection. It serves as a valuable resource for researchers, practitioners, and anyone interested in the field of AGI.

LLM-Synthetic-Data is a repository focused on real-time, fine-grained LLM-Synthetic-Data generation. It includes methods, surveys, and application areas related to synthetic data for language models. The repository covers topics like pre-training, instruction tuning, model collapse, LLM benchmarking, evaluation, and distillation. It also explores application areas such as mathematical reasoning, code generation, text-to-SQL, alignment, reward modeling, long context, weak-to-strong generalization, agent and tool use, vision and language, factuality, federated learning, generative design, and safety.

The Alignment Handbook provides robust training recipes for continuing pretraining and aligning language models with human and AI preferences. It includes techniques such as continued pretraining, supervised fine-tuning, reward modeling, rejection sampling, and direct preference optimization (DPO). The handbook aims to fill the gap in public resources on training these models, collecting data, and measuring metrics for optimal downstream performance.

ShieldLM is a bilingual safety detector designed to detect safety issues in LLMs' generations. It aligns with human safety standards, supports customizable detection rules, and provides explanations for decisions. Outperforming strong baselines, ShieldLM is impressive across 4 test sets.

This repository contains code for training reward models for Deep Reinforcement Learning-based Reward-modulated Hierarchical Fine-tuning (DRL-based RLHF), Iterative Selection Fine-tuning (Rejection sampling fine-tuning), and iterative Decision Policy Optimization (DPO). The reward models are trained using a Bradley-Terry model based on the Gemma and Mistral language models. The resulting reward models achieve state-of-the-art performance on the RewardBench leaderboard for reward models with base models of up to 13B parameters.

NeMo Curator is a GPU-accelerated open-source framework designed for efficient large language model data curation. It provides scalable dataset preparation for tasks like foundation model pretraining, domain-adaptive pretraining, supervised fine-tuning, and parameter-efficient fine-tuning. The library leverages GPUs with Dask and RAPIDS to accelerate data curation, offering customizable and modular interfaces for pipeline expansion and model convergence. Key features include data download, text extraction, quality filtering, deduplication, downstream-task decontamination, distributed data classification, and PII redaction. NeMo Curator is suitable for curating high-quality datasets for large language model training.

Informative assistant for safety and emergency training.

Provides emergency training assistance with a focus on safety and clear guidelines.

Incident Command Training for residential fires.

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

ATC communication trainer for pilots, offering scenario-based training and feedback.

Adaptive, informative advisor on disaster management.

Expert in IATA DGR for Transportation of Dangerous Goods.

Checking for safety in household items.

Film studio safety expert guiding on regulations and practices

Informative and safety-focused guide on consumer product recalls.

Friendly advisor for workplace safety.

Safety-first expert on hydrogen practices.

Simplifying the BSA for your project. Created by www.arka.works

Get a detailed risk analysis for public relations, marketing, and internal communications, identifying challenges and negative impacts to refine your messaging strategy.

A liaison GPT for AI safety emergencies, connecting users to OpenAI experts.

Friendly guide for woodworking safety and best practices.

Up-to-date travel safety advisor using web data, avoids subjective advice.

safety tips, gadgets and gear advice for all riders

Expert on OSHA standards, ideal for safety professionals.

Safety-aware, example-rich zsh guide.