AI tools for NASA open-source AI tools
Related Tools:
Hint
Hint is a hyper-personalized astrology app that combines NASA data with guidance from professional astrologers to provide personalized insights. It offers 1-on-1 guidance, horoscopes, compatibility reports, and chart decoding. Hint has become a recognized leader in the field of digital astrological services and is trusted by world's leading companies.
KeyPush
KeyPush is an AI search tool that serves as your ultimate AI and web search companion. It utilizes advanced technologies such as Bing and ChatGPT to provide users with the most relevant search results. Users can easily find information on a wide range of topics, from the best phones in the world to the latest stock market trends and trending topics globally. KeyPush also allows users to stay updated on NASA's upcoming news and events. With its intelligent search capabilities, KeyPush aims to enhance the search experience for users seeking accurate and timely information.
The Rundown AI
The Rundown AI is an AI tool designed to help users learn about artificial intelligence in a quick and efficient manner. It offers certificate courses, workshops, resources, newsletters, job board, and free guides related to AI. Users can stay updated with the latest AI news, tutorials, and workshops, and learn how to apply AI in various fields. The tool aims to cater to professionals from companies like Apple, OpenAI, Tesla, and NASA, providing them with valuable insights and knowledge about AI technologies.
rosa
ROSA is an AI Agent designed to interact with ROS-based robotics systems using natural language queries. It can generate system reports, read and parse ROS log files, adapt to new robots, and run various ROS commands using natural language. The tool is versatile for robotics research and development, providing an easy way to interact with robots and the ROS environment.
awesome-robotics-ai-companies
A curated list of companies in the robotics and artificially intelligent agents industry, including large companies, stable start-ups, non-profits, and government research labs. The list covers companies working on autonomous vehicles, robotics, artificial intelligence, machine learning, computer vision, and more. It aims to showcase industry innovators and important players in the field of robotics and AI.
AIforEarthDataSets
The Microsoft AI for Earth program hosts geospatial data on Azure that is important to environmental sustainability and Earth science. This repo hosts documentation and demonstration notebooks for all the data that is managed by AI for Earth. It also serves as a "staging ground" for the Planetary Computer Data Catalog.
awesome-mobile-robotics
The 'awesome-mobile-robotics' repository is a curated list of important content related to Mobile Robotics and AI. It includes resources such as courses, books, datasets, software and libraries, podcasts, conferences, journals, companies and jobs, laboratories and research groups, and miscellaneous resources. The repository covers a wide range of topics in the field of Mobile Robotics and AI, providing valuable information for enthusiasts, researchers, and professionals in the domain.
lobe-chat-plugins
Lobe Chat Plugins Index is a repository that serves as a collection of various plugins for Function Calling. Users can submit their plugins by following specific instructions. The repository includes a wide range of plugins for different tasks such as image generation, stock analysis, web search, NFT tracking, calendar management, and more. Each plugin is tagged with relevant keywords for easy identification and usage. The repository encourages contributions and provides guidelines for submitting new plugins. It is a valuable resource for developers looking to enhance chatbot functionalities with different plugins.
Awesome-Attention-Heads
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.
reader
Reader is a tool that converts any URL to an LLM-friendly input with a simple prefix `https://r.jina.ai/`. It improves the output for your agent and RAG systems at no cost. Reader supports image reading, captioning all images at the specified URL and adding `Image [idx]: [caption]` as an alt tag. This enables downstream LLMs to interact with the images in reasoning, summarizing, etc. Reader offers a streaming mode, useful when the standard mode provides an incomplete result. In streaming mode, Reader waits a bit longer until the page is fully rendered, providing more complete information. Reader also supports a JSON mode, which contains three fields: `url`, `title`, and `content`. Reader is backed by Jina AI and licensed under Apache-2.0.
swift-ocr-llm-powered-pdf-to-markdown
Swift OCR is a powerful tool for extracting text from PDF files using OpenAI's GPT-4 Turbo with Vision model. It offers flexible input options, advanced OCR processing, performance optimizations, structured output, robust error handling, and scalable architecture. The tool ensures accurate text extraction, resilience against failures, and efficient handling of multiple requests.
kernel-memory
Kernel Memory (KM) is a multi-modal AI Service specialized in the efficient indexing of datasets through custom continuous data hybrid pipelines, with support for Retrieval Augmented Generation (RAG), synthetic memory, prompt engineering, and custom semantic memory processing. KM is available as a Web Service, as a Docker container, a Plugin for ChatGPT/Copilot/Semantic Kernel, and as a .NET library for embedded applications. Utilizing advanced embeddings and LLMs, the system enables Natural Language querying for obtaining answers from the indexed data, complete with citations and links to the original sources. Designed for seamless integration as a Plugin with Semantic Kernel, Microsoft Copilot and ChatGPT, Kernel Memory enhances data-driven features in applications built for most popular AI platforms.
awesome-weather-models
A catalogue and categorization of AI-based weather forecasting models. This page provides a catalogue and categorization of AI-based weather forecasting models to enable discovery and comparison of different available model options. The weather models are categorized based on metadata found in the JSON schema specification. The table includes information such as the name of the weather model, the organization that developed it, operational data availability, open-source status, and links for further details.
Awesome-LWMs
Awesome Large Weather Models (LWMs) is a curated collection of articles and resources related to large weather models used in AI for Earth and AI for Science. It includes information on various cutting-edge weather forecasting models, benchmark datasets, and research papers. The repository serves as a hub for researchers and enthusiasts to explore the latest advancements in weather modeling and forecasting.
imodels
Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use. _For interpretability in NLP, check out our new package:imodelsX _
Call-for-Reviewers
The `Call-for-Reviewers` repository aims to collect the latest 'call for reviewers' links from various top CS/ML/AI conferences/journals. It provides an opportunity for individuals in the computer/ machine learning/ artificial intelligence fields to gain review experience for applying for NIW/H1B/EB1 or enhancing their CV. The repository helps users stay updated with the latest research trends and engage with the academic community.
Awesome-TimeSeries-SpatioTemporal-LM-LLM
Awesome-TimeSeries-SpatioTemporal-LM-LLM is a curated list of Large (Language) Models and Foundation Models for Temporal Data, including Time Series, Spatio-temporal, and Event Data. The repository aims to summarize recent advances in Large Models and Foundation Models for Time Series and Spatio-Temporal Data with resources such as papers, code, and data. It covers various applications like General Time Series Analysis, Transportation, Finance, Healthcare, Event Analysis, Climate, Video Data, and more. The repository also includes related resources, surveys, and papers on Large Language Models, Foundation Models, and their applications in AIOps.
OpenNARS-for-Applications
OpenNARS-for-Applications is an implementation of a Non-Axiomatic Reasoning System, a general-purpose reasoner that adapts under the Assumption of Insufficient Knowledge and Resources. The system combines the logic and conceptual ideas of OpenNARS, event handling and procedure learning capabilities of ANSNA and 20NAR1, and the control model from ALANN. It is written in C, offers improved reasoning performance, and has been compared with Reinforcement Learning and means-end reasoning approaches. The system has been used in real-world applications such as assisting first responders, real-time traffic surveillance, and experiments with autonomous robots. It has been developed with a pragmatic mindset focusing on effective implementation of existing theory.
OnAIR
The On-board Artificial Intelligence Research (OnAIR) Platform is a framework that enables AI algorithms written in Python to interact with NASA's cFS. It is intended to explore research concepts in autonomous operations in a simulated environment. The platform provides tools for generating environments, handling telemetry data through Redis, running unit tests, and contributing to the repository. Users can set up a conda environment, configure telemetry and Redis examples, run simulations, and conduct unit tests to ensure the functionality of their AI algorithms. The platform also includes guidelines for licensing, copyright, and contributions to the repository.