Best AI tools for< Improve Safety Programs >

Samsara is a leading provider of Connected Operations™ technology that connects people, systems, and data to give businesses visibility into every area of their operations. Samsara's platform includes a suite of products that help businesses improve safety, efficiency, and sustainability. Samsara's AI-powered video safety solutions provide real-time visibility into fleet operations, helping businesses to prevent accidents and protect their workforce. Samsara's fleet management solutions provide performance insights, asset protection, and live tracking for improved fleet productivity. Samsara's apps and workflows solutions provide customized driver experiences, real-time dispatch data, and streamlined ELD compliance. Samsara's site visibility solutions provide remote visibility, proactive alerting, and on-the-go access to data from remote sites.

Cambridge Mobile Telematics (CMT) is the world's largest telematics service provider, dedicated to making roads and drivers safer. Their AI-driven platform, DriveWell Fusion®, utilizes sensor data from various IoT devices to analyze and improve vehicle and driver behavior. CMT collaborates with auto insurers, automakers, gig companies, and the public sector to enhance risk assessment, safety, claims, and driver improvement programs. With headquarters in Cambridge, MA, and global offices, CMT protects millions of drivers worldwide daily.

Insitro is a drug discovery and development company that uses machine learning and data to identify and develop new medicines. The company's platform integrates in vitro cellular data produced in its labs with human clinical data to help redefine disease. Insitro's pipeline includes wholly-owned and partnered therapeutic programs in metabolism, oncology, and neuroscience.

Covera Health is a clinical intelligence platform that supports the end-to-end delivery of clinical-grade, AI-powered quality insights for providers and insurers. The platform is seamlessly integrated across the healthcare ecosystem to elevate everything from diagnosis and care coordination to prior authorization and claims administration. Covera Health is certified by AHRQ as a Patient Safety Organization to safeguard access to provider and patient data.

Byterat is a cloud-based platform that provides battery data management, visualization, and analytics. It offers an end-to-end data pipeline that automatically synchronizes, processes, and visualizes materials, manufacturing, and test data from all labs. Byterat also provides 24/7 access to experiments from anywhere in the world and integrates seamlessly with current workflows. It is customizable to specific cell chemistries and allows users to build custom visualizations, dashboards, and analyses. Byterat's AI-powered battery research has been published in leading journals, and its team has pioneered a new class of models that extract tell-tale signals of battery health from electrical signals to forecast future performance.

Snaplet is a data management tool for developers that provides AI-generated dummy data for local development, end-to-end testing, and debugging. It uses a real programming language (TypeScript) to define and edit data, ensuring type safety and auto-completion. Snaplet understands database structures and relationships, automatically transforming personally identifiable information and seeding data accordingly. It integrates seamlessly into development workflows, providing data where it's needed most: on local machines, for CI/CD testing, and preview environments.

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.'

Motive is an all-in-one fleet management platform that provides businesses with a variety of tools to help them improve safety, efficiency, and profitability. Motive's platform includes features such as AI-powered dashcams, ELD compliance, GPS fleet tracking, equipment monitoring, and fleet card management. Motive's platform is used by over 120,000 companies, including small businesses and Fortune 500 enterprises.

Nauto is an AI-powered fleet management software that helps businesses improve driver safety and reduce collisions. It uses a dual-facing camera and external sensors to detect distracted and drowsy driving, as well as in-cabin and external risks. Nauto's predictive AI algorithms can assess, predict, and alert drivers of imminent risks to avoid collisions. It also provides real-time alerts to end distracted and drowsy driving, and self-guided coaching videos to help drivers improve their behavior. Nauto's claims management feature can quickly and reliably process and resolve claims, resulting in millions of dollars saved. Overall, Nauto is a comprehensive driver and vehicle safety platform that can help businesses reduce risk, improve safety, and save money.

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.

Wobot AI is a transformative camera system that leverages artificial intelligence to provide actionable business insights for enhanced operations and revenue growth across industries. The platform offers intelligent automation, robust reporting, and a scalable platform designed to adapt to businesses of all sizes. With a user-friendly interface, Wobot AI simplifies camera and task management, making it accessible for all employees. Trusted by businesses worldwide, Wobot AI enhances productivity, safety, and operational efficiency.

FYLD is an award-winning digital platform that utilizes machine learning to automatically transform video and audio footage into real-time workflows, video risk assessments, and analytics dashboards. It aims to eliminate paperwork, save time, and create safer work sites for various sectors such as Highways, Energy, Water, and Wastewater. FYLD helps managers prioritize high-risk sites, reduces paperwork, and enhances efficiency by providing remote visibility of site conditions. The platform empowers fieldworkers, contractors, and civil engineers by streamlining job processes, improving safety measures, and minimizing environmental impact.

Harvy is an AI-driven automation tool designed to streamline work diary data entry and compliance reporting for heavy vehicle operators. By automating tasks such as scanning logbook sheets, detecting breaches, and generating compliance reports, Harvy simplifies complex processes, reduces human error, and enhances operational efficiency. The platform offers significant time and cost savings, promotes regulatory compliance, and provides valuable insights to improve safety and fatigue management. With user-friendly features and a proactive approach to compliance, Harvy is a valuable tool for transport operations seeking to optimize their processes and ensure regulatory adherence.

Prompt Engineering is a discipline focused on developing and optimizing prompts to efficiently utilize language models (LMs) for various applications and research topics. It involves skills to understand the capabilities and limitations of large language models, improving their performance on tasks like question answering and arithmetic reasoning. Prompt engineering is essential for designing robust prompting techniques that interact with LLMs and other tools, enhancing safety and building new capabilities by augmenting LLMs with domain knowledge and external tools.

Leela AI is a visual intelligence platform and analytics software designed to help manufacturing companies increase production capacity, reduce wasted time, improve workplace safety, and streamline operations. By leveraging AI technology, Leela AI turns standard cameras into powerful data feeds, enabling real-time monitoring, analysis, and optimization of manufacturing processes. The platform provides actionable insights to enhance performance, quality, and safety, ultimately leading to significant cost savings and operational improvements for manufacturing businesses.

Rekor is an AI-powered platform that delivers revolutionary roadway intelligence by collecting, connecting, and organizing mobility data. It offers a range of software platforms, hardware systems, and applications for urban mobility, transportation management, and public safety. Rekor's technology utilizes computer vision, edge processing, and predictive algorithms to transform data into actionable intelligence, benefiting communities and businesses on a daily basis. With a focus on security standards and data governance, Rekor provides comprehensive traffic and vehicle analytics, license plate recognition, and compliance automation solutions.

Airship AI is a cutting-edge, artificial intelligence-driven video, sensor, and data management surveillance platform. Customers rely on their services to provide actionable intelligence in real-time, collected from a wide range of deployed sensors, utilizing the latest in edge and cloud-based analytics. These capabilities improve public safety and operational efficiency for both public sector and commercial clients. Founded in 2006, Airship AI is U.S. owned and operated, headquartered in Redmond, Washington. Airship's product suite is comprised of three core offerings: Acropolis, the enterprise software stack, Command, the family of viewing clients, and Outpost, edge hardware and software AI offerings.

Athina AI is a platform that provides research and guides for building safe and reliable AI products. It helps thousands of AI engineers in building safer products by offering tutorials, research papers, and evaluation techniques related to large language models. The platform focuses on safety, prompt engineering, hallucinations, and evaluation of AI models.

Figure is a pioneering AI robotics company that is revolutionizing the industry by introducing a general purpose humanoid robot to the global workforce. By combining cutting-edge AI technology with the dexterity of the human form, Figure aims to enhance human capabilities, address labor shortages, and improve workplace safety. The company's innovative approach is reshaping the future of manufacturing, logistics, warehousing, and retail sectors.

VirtuSense Technologies is a leading provider of fall prevention and remote patient monitoring (RPM) solutions powered by artificial intelligence (AI). Their AI-driven solutions, VSTAlert and VSTBalance, are designed to help healthcare providers reduce falls, improve patient safety, and enhance care delivery. VSTAlert is a fall prevention system that uses AI to detect falls before they happen, reducing the risk of injury and improving patient outcomes. VSTBalance is a balance assessment tool that helps clinicians identify patients at risk of falling and provides personalized exercises to improve their balance and mobility. VirtuSense's solutions integrate with various healthcare systems and are used by hospitals, post-acute care facilities, and ambulatory care centers to improve patient care and reduce costs.

MATLAB-Simulink-Challenge-Project-Hub is a repository aimed at contributing to the progress of engineering and science by providing challenge projects with real industry relevance and societal impact. The repository offers a wide range of projects covering various technology trends such as Artificial Intelligence, Autonomous Vehicles, Big Data, Computer Vision, and Sustainability. Participants can gain practical skills with MATLAB and Simulink while making a significant contribution to science and engineering. The projects are designed to enhance expertise in areas like Sustainability and Renewable Energy, Control, Modeling and Simulation, Machine Learning, and Robotics. By participating in these projects, individuals can receive official recognition for their problem-solving skills from technology leaders at MathWorks and earn rewards upon project completion.

draive is an open-source Python library designed to simplify and accelerate the development of LLM-based applications. It offers abstract building blocks for connecting functionalities with large language models, flexible integration with various AI solutions, and a user-friendly framework for building scalable data processing pipelines. The library follows a function-oriented design, allowing users to represent complex programs as simple functions. It also provides tools for measuring and debugging functionalities, ensuring type safety and efficient asynchronous operations for modern Python apps.

This repository is a collection of resources focused on interpretability in large language models (LLMs). It aims to help beginners get started in the area and keep researchers updated on the latest progress. It includes libraries, blogs, tutorials, forums, tools, programs, papers, and more related to interpretability in LLMs.

The LLM Prompt Tuning Playbook is a comprehensive guide for improving the performance of post-trained Language Models (LLMs) through effective prompting strategies. It covers topics such as pre-training vs. post-training, considerations for prompting, a rudimentary style guide for prompts, and a procedure for iterating on new system instructions. The playbook emphasizes the importance of clear, concise, and explicit instructions to guide LLMs in generating desired outputs. It also highlights the iterative nature of prompt development and the need for systematic evaluation of model responses.

The Awesome-Neuro-Symbolic-Learning-with-LLM repository is a curated collection of papers and resources focusing on improving reasoning and planning capabilities of Large Language Models (LLMs) and Multi-Modal Large Language Models (MLLMs) through neuro-symbolic learning. It covers a wide range of topics such as neuro-symbolic visual reasoning, program synthesis, logical reasoning, mathematical reasoning, code generation, visual reasoning, geometric reasoning, classical planning, game AI planning, robotic planning, AI agent planning, and more. The repository provides a comprehensive overview of tutorials, workshops, talks, surveys, papers, datasets, and benchmarks related to neuro-symbolic learning with LLMs and MLLMs.

The repository 'Awesome-LLM4Cybersecurity' provides a comprehensive overview of the applications of Large Language Models (LLMs) in cybersecurity. It includes a systematic literature review covering topics such as constructing cybersecurity-oriented domain LLMs, potential applications of LLMs in cybersecurity, and research directions in the field. The repository analyzes various benchmarks, datasets, and applications of LLMs in cybersecurity tasks like threat intelligence, fuzzing, vulnerabilities detection, insecure code generation, program repair, anomaly detection, and LLM-assisted attacks.

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.

LLM-as-a-Judge is a repository that includes papers discussed in a survey paper titled 'A Survey on LLM-as-a-Judge'. The repository covers various aspects of using Large Language Models (LLMs) as judges for tasks such as evaluation, reasoning, and decision-making. It provides insights into evaluation pipelines, improvement strategies, and specific tasks related to LLMs. The papers included in the repository explore different methodologies, applications, and future research directions for leveraging LLMs as evaluators in various domains.

Awesome-LLMs-for-Video-Understanding is a repository dedicated to exploring Video Understanding with Large Language Models. It provides a comprehensive survey of the field, covering models, pretraining, instruction tuning, and hybrid methods. The repository also includes information on tasks, datasets, and benchmarks related to video understanding. Contributors are encouraged to add new papers, projects, and materials to enhance the repository.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

LLMWare is a framework for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows. This project provides a comprehensive set of tools that anyone can use - from a beginner to the most sophisticated AI developer - to rapidly build industrial-grade, knowledge-based enterprise LLM applications. Our specific focus is on making it easy to integrate open source small specialized models and connecting enterprise knowledge safely and securely.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

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

構造体の写真を入力すると、弱点部位を分析してくれます

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