Best AI tools for< Articulate Editorial >

editoReview is a consulting platform and marketplace that helps academic editors and marketing agents to review the AI intelligence at the interface of research articles and service plugins API by consulting with authors and developers. It allows users to start a new review using an AI chat transcript or from a template document, cite the reference paper or app to schedule a consultation meeting with the author or developer, and pay the optional consultation and publish the review transcripts with shareable links.

Articulate is the world's best creator platform for online workplace learning, offering a comprehensive suite of tools for creating engaging and effective online training content. With features like AI Assistant, Rise, Storyline, and Reach, Articulate provides millions of course assets, prebuilt templates, and easy-to-use creator tools to simplify course creation from start to finish. The platform is loved by customers and industry experts for its ability to increase compliance rates, reduce incidents, and deliver blended learning experiences. Articulate is a one-stop solution for organizations looking to create and deliver online training at scale.

SOAP Note AI is an AI-powered tool designed to generate HIPAA-compliant, fast, and efficient SOAP notes for various healthcare specialties, including Physical Therapy, Occupational Therapy, Nursing, Mental Health, SLP, Dentistry, Podiatry, Massage, Acupuncture, Chiropractic, Veterinary, and Pharmacy. The tool helps healthcare professionals save time by quickly converting shorthand notes, audio dictations, or AI Scribe session recordings into comprehensive SOAP notes. It offers specialized SOAP note templates, access to note history for up to 30 days, and a feedback feature for instant review. SOAP Note AI simplifies the documentation process, ensures HIPAA compliance, and offers flexible pricing plans for different user needs.

Murror is an AI mental health assistant designed to help individuals articulate their thoughts and emotions, reflect on their experiences, and broaden their self-view. It offers personalized reflections, insights, and tracking of mental health progress through AI-generated artwork and journal topic identification. Murror ensures privacy and security by encrypting and securing all data, using it only for mental health research purposes. Users can join the waitlist to access early and work together to enhance the product experience.

Brainglue is a conversational AI tool designed for creative professionals to supercharge their strategic thinking and communication. It offers a suite of AI workflows optimized for writing, analysis, research, illustration, and more. Users can chat with AI advisors, access frontier models like GPT-4 and Claude 3, save recurring context in memory docs, and generate images with state-of-the-art models. Brainglue aims to enhance productivity and provide a thoughtful AI experience tailored for knowledge professionals and creative minds.

Articula AI is a cutting-edge speech translation application designed for International Trade. It offers accurate and context-aware live call translation services, allowing users to communicate with suppliers and customers in any language seamlessly. The app leverages AI technology to understand the context of conversations and provide topic-specific translations. Users can speak in their preferred language using an AI voice that mimics their natural tone. Articula AI is compatible with all calling apps commonly used in business settings, making it a versatile tool for cross-border communication.

MockingBird is a toolbox designed for Mandarin speech synthesis using PyTorch. It supports multiple datasets such as aidatatang_200zh, magicdata, aishell3, and data_aishell. The toolbox can run on Windows, Linux, and M1 MacOS, providing easy and effective speech synthesis with pretrained encoder/vocoder models. It is webserver ready for remote calling. Users can train their own models or use existing ones for the encoder, synthesizer, and vocoder. The toolbox offers a demo video and detailed setup instructions for installation and model training.

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.

Awesome-AIGC-3D is a curated list of awesome AIGC 3D papers, inspired by awesome-NeRF. It aims to provide a comprehensive overview of the state-of-the-art in AIGC 3D, including papers on text-to-3D generation, 3D scene generation, human avatar generation, and dynamic 3D generation. The repository also includes a list of benchmarks and datasets, talks, companies, and implementations related to AIGC 3D. The description is less than 400 words and provides a concise overview of the repository's content and purpose.

OpenSPG is a knowledge graph engine developed by Ant Group in collaboration with OpenKG, based on the SPG (Semantic-enhanced Programmable Graph) framework. It provides explicit semantic representations, logical rule definitions, operator frameworks (construction, inference), and other capabilities for domain knowledge graphs. OpenSPG supports pluggable adaptation of basic engines and algorithmic services by various vendors to build customized solutions.

Instruct2Act is a framework that utilizes Large Language Models to map multi-modal instructions to sequential actions for robotic manipulation tasks. It generates Python programs using the LLM model for perception, planning, and action. The framework leverages foundation models like SAM and CLIP to convert high-level instructions into policy codes, accommodating various instruction modalities and task demands. Instruct2Act has been validated on robotic tasks in tabletop manipulation domains, outperforming learning-based policies in several tasks.

ManipVQA is a framework that enhances Multimodal Large Language Models (MLLMs) with manipulation-centric knowledge through a Visual Question-Answering (VQA) format. It addresses the deficiency of conventional MLLMs in understanding affordances and physical concepts crucial for manipulation tasks. By infusing robotics-specific knowledge, including tool detection, affordance recognition, and physical concept comprehension, ManipVQA improves the performance of robots in manipulation tasks. The framework involves fine-tuning MLLMs with a curated dataset of interactive objects, enabling robots to understand and execute natural language instructions more effectively.

Genesis is a physics platform designed for general purpose Robotics/Embodied AI/Physical AI applications. It includes a universal physics engine, a lightweight, ultra-fast, pythonic, and user-friendly robotics simulation platform, a powerful and fast photo-realistic rendering system, and a generative data engine that transforms user-prompted natural language description into various modalities of data. It aims to lower the barrier to using physics simulations, unify state-of-the-art physics solvers, and minimize human effort in collecting and generating data for robotics and other domains.

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

Habitat-Sim is a high-performance physics-enabled 3D simulator with support for 3D scans of indoor/outdoor spaces, CAD models of spaces and piecewise-rigid objects, configurable sensors, robots described via URDF, and rigid-body mechanics. It prioritizes simulation speed over the breadth of simulation capabilities, achieving several thousand frames per second (FPS) running single-threaded and over 10,000 FPS multi-process on a single GPU when rendering a scene from the Matterport3D dataset. Habitat-Sim simulates a Fetch robot interacting in ReplicaCAD scenes at over 8,000 steps per second (SPS), where each ‘step’ involves rendering 1 RGBD observation (128×128 pixels) and rigid-body dynamics for 1/30sec.

This repository contains a collection of papers and code for the CVPR 2024 conference. The papers cover a wide range of topics in computer vision, including object detection, image segmentation, image generation, and video analysis. The code provides implementations of the algorithms described in the papers, making it easy for researchers and practitioners to reproduce the results and build upon the work of others. The repository is maintained by a team of researchers at the University of California, Berkeley.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

This repository contains the code for HOLD, a method that jointly reconstructs hands and objects from monocular videos without assuming a pre-scanned object template. It can reconstruct 3D geometries of novel objects and hands, enabling template-free bimanual hand-object reconstruction, textureless object interaction with hands, and multiple objects interaction with hands. The repository provides instructions to download in-the-wild videos from HOLD, preprocess and train on custom videos, a volumetric rendering framework, a generalized codebase for single and two hand interaction with objects, a viewer to interact with predictions, and code to evaluate and compare with HOLD in HO3D. The repository also includes documentation for setup, training, evaluation, visualization, preprocessing custom sequences, and using HOLD on ARCTIC.

Awesome-Robotics-3D is a curated list of 3D Vision papers related to Robotics domain, focusing on large models like LLMs/VLMs. It includes papers on Policy Learning, Pretraining, VLM and LLM, Representations, and Simulations, Datasets, and Benchmarks. The repository is maintained by Zubair Irshad and welcomes contributions and suggestions for adding papers. It serves as a valuable resource for researchers and practitioners in the field of Robotics and Computer Vision.

AIR-1 is a compact sensor device designed for monitoring various environmental parameters such as gas levels, particulate matter, temperature, and humidity. It features multiple sensors for detecting gases like CO, alcohol, H2, NO2, NH3, CO2, as well as particulate matter, VOCs, NOx, and more. The device is designed with a focus on accuracy and efficient heat management in a small form factor, making it suitable for indoor air quality monitoring and environmental sensing applications.

ESPHome yaml files for AirGradient devices to maintain the research and accuracy of AirGradient sensors, while also gaining the benefits of ESPHome/HomeAssistant for easy to use switches, buttons, configurations, and dashboards. Maintains the ability to also send data to the AirGradient Dashboard, which can also be disabled/removed to keep all data local.

Articulate, precise mentor employing the Socratic method & Batesonian reasoning to find solution to issues (updated on 10 Jan 24)

Polished, articulate AI for script adaptations.

British gentleman in dark psychology, provides articulate, strategic advice.

This tool is designed to locate the ideal GPT model tailored to your specific requirements. Simply articulate your needs, and it will diligently work to identify the perfect GPT solution for you.

A communication expert for articulating empathetic and effective feedback.

Articulates E-Learning FAB

A GPT that turns language into engaging visual art.