Best AI tools for< Improve Safety Alignment >

Glass Health is an AI-powered clinical decision support platform that empowers clinicians by providing tools to develop differential diagnoses and draft clinical plans. The platform combines cutting-edge AI technology with evidence-based medical knowledge to streamline diagnostic decisions and optimize clinician workflows. Glass Health's mission is to leverage technology to enhance the practice of medicine, increase diagnostic accuracy, implement evidence-based medicine, promote health equity, and improve patient outcomes globally. The platform is built by clinicians, for clinicians, with a deep commitment to safety, ethics, and alignment.

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.

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.

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.

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.

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 set to transform various sectors such as manufacturing, logistics, warehousing, and retail. With a team of experts boasting over 100 years of combined experience in AI and humanoid robotics, Figure is at the forefront of shaping the future of work.

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.

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.

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.

Motional is a company that is developing driverless technology and autonomous vehicles. They are working to make driverless vehicles a safe, reliable, and accessible reality. Motional's all-electric IONIQ 5 robotaxis are now available to public riders in Las Vegas. The company has a strong commitment to safety and is constantly developing new technologies to improve the safety of its vehicles. Motional is also working to make driverless vehicles more accessible by partnering with ride-hail and delivery services.

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.

This repository is a collection of papers on Generative AI Machine Unlearning, categorized based on modality and applications. It includes datasets, benchmarks, and surveys related to unlearning scenarios in generative AI. The repository aims to provide a comprehensive overview of research in the field of machine unlearning for generative models.

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.

A comprehensive repository focusing on 'Model Merging in LLMs, MLLMs, and Beyond', providing an exhaustive overview of model merging methods, theories, applications, and future research directions. The repository covers various advanced methods, applications in foundation models, different machine learning subfields, and tasks like pre-merging methods, architecture transformation, weight alignment, basic merging methods, and more.

This repository tracks the latest research on machine unlearning in large language models (LLMs). It offers a comprehensive list of papers, datasets, and resources relevant to the topic.

The Awesome-LLM-Post-training repository is a curated collection of influential papers, code implementations, benchmarks, and resources related to Large Language Models (LLMs) Post-Training Methodologies. It covers various aspects of LLMs, including reasoning, decision-making, reinforcement learning, reward learning, policy optimization, explainability, multimodal agents, benchmarks, tutorials, libraries, and implementations. The repository aims to provide a comprehensive overview and resources for researchers and practitioners interested in advancing LLM technologies.

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.

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.

Awesome-LLM-in-Social-Science is a repository that compiles papers evaluating Large Language Models (LLMs) from a social science perspective. It includes papers on evaluating, aligning, and simulating LLMs, as well as enhancing tools in social science research. The repository categorizes papers based on their focus on attitudes, opinions, values, personality, morality, and more. It aims to contribute to discussions on the potential and challenges of using LLMs in social science research.

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.

MM-RLHF is a comprehensive project for aligning Multimodal Large Language Models (MLLMs) with human preferences. It includes a high-quality MLLM alignment dataset, a Critique-Based MLLM reward model, a novel alignment algorithm MM-DPO, and benchmarks for reward models and multimodal safety. The dataset covers image understanding, video understanding, and safety-related tasks with model-generated responses and human-annotated scores. The reward model generates critiques of candidate texts before assigning scores for enhanced interpretability. MM-DPO is an alignment algorithm that achieves performance gains with simple adjustments to the DPO framework. The project enables consistent performance improvements across 10 dimensions and 27 benchmarks for open-source MLLMs.

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.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

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.

The Awesome-System2-Reasoning-LLM repository is dedicated to a survey paper titled 'From System 1 to System 2: A Survey of Reasoning Large Language Models'. It explores the development of reasoning Large Language Models (LLMs), their foundational technologies, benchmarks, and future directions. The repository provides resources and updates related to the research, tracking the latest developments in the field of reasoning LLMs.

A toolkit for agent autonomy, evolution, and governance enabling agents to learn from experience, collaborate, communicate, and build new tools within governance guardrails. It focuses on autonomous evolution, agent self-discovery, governance firmware, self-building systems, and agent-centric architecture. The toolkit leverages existing frameworks to enable agent autonomy and self-governance, moving towards truly autonomous AI systems.

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