Best AI tools for< Collision Repair Technician >

CaseYak is an AI-powered platform that uses artificial intelligence to estimate the value of personal injury claims. By comparing user inputs to ten years of historical jury verdict data, CaseYak's AI Case Calculator provides a projected claim value for motor vehicle accident cases. The platform aims to offer an objective and data-driven prediction to help individuals seeking legal recourse after being injured in a car accident. Additionally, CaseYak provides information on how a personal injury attorney can assist in handling the claim process.

Neuraspace is an AI/ML solution designed for smarter space traffic management. It automates risk assessment and provides maneuver suggestions up to 5 days before potential collisions, streamlining operations for satellite operators. The platform aims to help satellite companies protect their fleets from space debris, reduce service disruptions, and optimize space operations. Neuraspace offers tools to empower teams to make informed decisions and keep their growing fleets safe with minimal impact on operations.

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.

Luminar is a leading developer of automotive lidar technology. The company's mission is to make roads safer by eliminating vehicle accidents. Luminar's lidar sensors provide cars with a detailed view of their surroundings, enabling them to make better decisions and avoid collisions. Luminar's technology is being used by a number of automakers, including Volvo, SAIC Motor, and Polestar.

The Coalition for Health AI (CHAI) is an AI application that provides guidelines for the responsible use of AI in health. It focuses on developing best practices and frameworks for safe and equitable AI in healthcare. CHAI aims to address algorithmic bias and collaborates with diverse stakeholders to drive the development, evaluation, and appropriate use of AI in healthcare.

The Coalition for Secure AI (CoSAI) is an open ecosystem of AI and security experts dedicated to sharing best practices for secure AI deployment and collaborating on AI security research and product development. It aims to foster a collaborative ecosystem of diverse stakeholders to invest in AI security research collectively, share security expertise and best practices, and build technical open-source solutions for secure AI development and deployment.

Coach is an AI-powered career development platform designed for learners, job seekers, and career shifters. It offers personalized career guidance, resources for skills development, and assistance in exploring various career paths. Built by CareerVillage and a coalition of educators and nonprofits, Coach aims to provide trustworthy AI career advising and support for individuals at every stage of their career journey.

DotRecast is a C# port of Recast & Detour, a navigation library used in many AAA and indie games and engines. It provides automatic navmesh generation, fast turnaround times, detailed customization options, and is dependency-free. Recast Navigation is divided into multiple modules, each contained in its own folder: - DotRecast.Core: Core utils - DotRecast.Recast: Navmesh generation - DotRecast.Detour: Runtime loading of navmesh data, pathfinding, navmesh queries - DotRecast.Detour.TileCache: Navmesh streaming. Useful for large levels and open-world games - DotRecast.Detour.Crowd: Agent movement, collision avoidance, and crowd simulation - DotRecast.Detour.Dynamic: Robust support for dynamic nav meshes combining pre-built voxels with dynamic objects which can be freely added and removed - DotRecast.Detour.Extras: Simple tool to import navmeshes created with A* Pathfinding Project - DotRecast.Recast.Toolset: All modules - DotRecast.Recast.Demo: Standalone, comprehensive demo app showcasing all aspects of Recast & Detour's functionality - Tests: Unit tests Recast constructs a navmesh through a multi-step mesh rasterization process: 1. First Recast rasterizes the input triangle meshes into voxels. 2. Voxels in areas where agents would not be able to move are filtered and removed. 3. The walkable areas described by the voxel grid are then divided into sets of polygonal regions. 4. The navigation polygons are generated by re-triangulating the generated polygonal regions into a navmesh. You can use Recast to build a single navmesh, or a tiled navmesh. Single meshes are suitable for many simple, static cases and are easy to work with. Tiled navmeshes are more complex to work with but better support larger, more dynamic environments. Tiled meshes enable advanced Detour features like re-baking, hierarchical path-planning, and navmesh data-streaming.

This repository contains a collection of research papers related to watermarking techniques for text and images, specifically focusing on large language models (LLMs). The papers cover various aspects of watermarking LLM-generated content, including robustness, statistical understanding, topic-based watermarks, quality-detection trade-offs, dual watermarks, watermark collision, and more. Researchers have explored different methods and frameworks for watermarking LLMs to protect intellectual property, detect machine-generated text, improve generation quality, and evaluate watermarking techniques. The repository serves as a valuable resource for those interested in the field of watermarking for LLMs.

The TensorRT-LLM Backend is a Triton backend designed to serve TensorRT-LLM models with Triton Inference Server. It supports features like inflight batching, paged attention, and more. Users can access the backend through pre-built Docker containers or build it using scripts provided in the repository. The backend can be used to create models for tasks like tokenizing, inferencing, de-tokenizing, ensemble modeling, and more. Users can interact with the backend using provided client scripts and query the server for metrics related to request handling, memory usage, KV cache blocks, and more. Testing for the backend can be done following the instructions in the 'ci/README.md' file.

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.

CLadder is a repository containing the CLadder dataset for evaluating causal reasoning in language models. The dataset consists of yes/no questions in natural language that require statistical and causal inference to answer. It includes fields such as question_id, given_info, question, answer, reasoning, and metadata like query_type and rung. The dataset also provides prompts for evaluating language models and example questions with associated reasoning steps. Additionally, it offers dataset statistics, data variants, and code setup instructions for using the repository.

Nuitrack™ is an ultimate 3D body tracking solution developed by 3DiVi Inc. It enables body motion analytics applications for virtually any widespread depth sensors and hardware platforms, supporting a wide range of applications from real-time gesture recognition on embedded platforms to large-scale multisensor analytical systems. Nuitrack provides highly-sophisticated 3D skeletal tracking, basic facial analysis, hand tracking, and gesture recognition APIs for UI control. It offers two skeletal tracking engines: classical for embedded hardware and AI for complex poses, providing a human-centric spatial understanding tool for natural and intelligent user engagement.

Cool-GenAI-Fashion-Papers is a curated list of resources related to GenAI-Fashion, including papers, workshops, companies, and products. It covers a wide range of topics such as fashion design synthesis, outfit recommendation, fashion knowledge extraction, trend analysis, and more. The repository provides valuable insights and resources for researchers, industry professionals, and enthusiasts interested in the intersection of AI and fashion.

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.

The 'ollama-ebook-summary' repository is a Python project that creates bulleted notes summaries of books and long texts, particularly in epub and pdf formats with ToC metadata. It automates the extraction of chapters, splits them into ~2000 token chunks, and allows for asking arbitrary questions to parts of the text for improved granularity of response. The tool aims to provide summaries for each page of a book rather than a one-page summary of the entire document, enhancing content curation and knowledge sharing capabilities.

**Curated collection of papers and resources on how to unlock the reasoning ability of LLMs and MLLMs.** **Description in less than 400 words, no line breaks and quotation marks.** Large Language Models (LLMs) have revolutionized the NLP landscape, showing improved performance and sample efficiency over smaller models. However, increasing model size alone has not proved sufficient for high performance on challenging reasoning tasks, such as solving arithmetic or commonsense problems. This curated collection of papers and resources presents the latest advancements in unlocking the reasoning abilities of LLMs and Multimodal LLMs (MLLMs). It covers various techniques, benchmarks, and applications, providing a comprehensive overview of the field. **5 jobs suitable for this tool, in lowercase letters.** - content writer - researcher - data analyst - software engineer - product manager **Keywords of the tool, in lowercase letters.** - llm - reasoning - multimodal - chain-of-thought - prompt engineering **5 specific tasks user can use this tool to do, in less than 3 words, Verb + noun form, in daily spoken language.** - write a story - answer a question - translate a language - generate code - summarize a document

DataDreamer is a powerful open-source Python library designed for prompting, synthetic data generation, and training workflows. It is simple, efficient, and research-grade, allowing users to create prompting workflows, generate synthetic datasets, and train models with ease. The library is built for researchers, by researchers, focusing on correctness, best practices, and reproducibility. It offers features like aggressive caching, resumability, support for bleeding-edge techniques, and easy sharing of datasets and models. DataDreamer enables users to run multi-step prompting workflows, generate synthetic datasets for various tasks, and train models by aligning, fine-tuning, instruction-tuning, and distilling them using existing or synthetic data.

Aimeos Laravel is a professional, full-featured, and ultra-fast Laravel ecommerce package that can be easily integrated into existing Laravel applications. It offers a wide range of features including multi-vendor, multi-channel, and multi-warehouse support, fast performance, support for various product types, subscriptions with recurring payments, multiple payment gateways, full RTL support, flexible pricing options, admin backend, REST and GraphQL APIs, modular structure, SEO optimization, multi-language support, AI-based text translation, mobile optimization, and high-quality source code. The package is highly configurable and extensible, making it suitable for e-commerce SaaS solutions, marketplaces, and online shops with millions of vendors.

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.

Expert en recherche de peinture auto et moto

Asistente especializado en accidentes de tráfico que te facilitará información y recomendaciones para iniciar una reclamación

Emulates Patrick Collison; advises on startups, tech progress, and book recommendations.

Helps resolve Game Maker & GML related questions.

I help you understand coalition agreements in three languages.

Specializes in Luxembourg's 2023-2028 coalition agreement

Your guide to Content Credentials, Content Authenticity Initiative (CAI) and Coalition for Content Provenance and Authenticity (C2PA) at File.Baby.

Your go-to guide for community activism. Member of the Hipster Energy Team. https://hipster.energy/team