The OpenCog Framework is a platform to build and share artificial intelligence programs. It includes components for procedural and declarative knowledge representation (AtomSpace), task scheduling (CogServer), AI algorithm containers (MindAgents), connectors to instant messaging and virtual world systems, and other components. MindAgents and other add-ons explore a wide variety of AI techniques including evolutionary program learning (MOSES), natural language processing, and others.

OpenCog - Framework to build Artificial Intelligence Programs

DeepDetect is an Instant Machine Learning for your Applications. It can classify images, text and numerical data from your application or the command line by series of simple calls to the deep learning server.  A simple yet powerful and generic API for use of Machine Learning.


DeepDetect - Deep Learning Server

ConvNetJS is a Javascript implementation of Neural networks, It currently supports Common Neural Network modules, Classification (SVM/Softmax) and Regression (L2) cost functions, A MagicNet class for fully automatic neural network learning (automatic hyperparameter search and cross-validatations), Ability to specify and train Convolutional Networks that process images, An experimental Reinforcement Learning module, based on Deep Q Learning.


ConvNetJS - Javascript implementation of Neural networks

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. 

Caffe - Deep Learning Framework from Berkley Vision

AirSim is a simulator for drones (and soon other vehicles) built on Unreal Engine. It is open-source, cross platform and supports hardware-in-loop with popular flight controllers such as PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped in to any Unreal environment you want.
 
Our goal is to develop AirSim as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles. For this purpose, AirSim also exposes APIs to retrieve data and control vehicles in a platform independent way.

AirSim is a simulator for drones (and soon other vehicles) built on Unreal Engine. It is open-source, cross platform and supports hardware-in-loop with popular flight controllers such as PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped in to any Unreal environment you want.

AirSim - Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research

Sonnet is a library built on top of TensorFlow for building complex neural networks. The library uses an object-oriented approach, similar to Torch/NN, allowing modules to be created which define the forward pass of some computation. Modules are called with some input Tensors, which adds ops to the Graph and returns output Tensors. 
 
Sonnet is designed specifically to work with TensorFlow, and as such does not prevent you from accessing the underlying details such as Tensors and variable_scopes. Models written in Sonnet can be freely mixed with raw TF code, and that in other high level libraries.

Sonnet is a library built on top of TensorFlow for building complex neural networks. The library uses an object-oriented approach, similar to Torch/NN, allowing modules to be created which define the forward pass of some computation. Modules are called with some input Tensors, which adds ops to the Graph and returns output Tensors. 

Sonnet - Library built on top of TensorFlow for building complex neural networks

DSSTNE (pronounced "Destiny") is an open source software library for training and deploying recommendation models with sparse inputs, fully connected hidden layers, and sparse outputs. Models with weight matrices that are too large for a single GPU can still be trained on a single host. DSSTNE has been used at Amazon to generate personalized product recommendations for our customers at Amazon's scale. 
 
 It is designed for production deployment of real-world applications which need to emphasize speed and scale over experimental flexibility. 
Its features include: 
<UL>
<LI> Multi-GPU Scale: Training and prediction both scale out to use multiple GPUs, spreading out computation and storage in a model-parallel fashion for each layer.</LI><LI>Large Layers: Model-parallel scaling enables larger networks than are possible with a single GPU.</LI><LI>Sparse Data: DSSTNE is optimized for fast performance on sparse datasets, common in recommendation problems. Custom GPU kernels perform sparse computation on the GPU, without filling in lots of zeroes.
</LI></UL>

DSSTNE (pronounced "Destiny") is an open source software library for training and deploying recommendation models with sparse inputs, fully connected hidden layers, and sparse outputs. Models with weight matrices that are too large for a single GPU can still be trained on a single host. DSSTNE has been used at Amazon to generate personalized product recommendations for our customers at Amazon's scale. 

Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine

Obsei is a low code AI powered automation tool. It can be used in various business flows like social listening, AI based alerting, brand image analysis, comparative study and more. 
It consist of Observer, Analyzer and Informer. Observer observes the platform like Twitter, Facebook, App Stores, Google reviews, Amazon reviews, News, Website etc and feed that information. Analyzer performs text analysis like classification, sentiment, translation, PII on the analyzed data. Informer sends it to ticketing system, data store, dataframe etc for further action and analysis.

Obsei - Low code AI powered automation tool

Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility.
As the Internet grew and evolved, unstructured data became more and more common, including emails, papers, IoT sensor data, Facebook photos, protein structures, and much more. In order for computers to understand and process unstructured data, these are converted into vectors using embedding techniques. Milvus stores and indexes these vectors. Milvus is able to analyze the correlation between two vectors by calculating their similarity distance. If the two embedding vectors are very similar, it means that the original data sources are similar as well. &nbsp; Millvus is capable of indexing vectors on a trillion scale. Unlike existing relational databases which mainly deal with structured data following a pre-defined pattern, Milvus is designed from the bottom-up to handle embedding vectors converted from unstructured data.
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Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility.



Milvus - An open-source vector database for embedding similarity search and AI applications

Arguflow is a truly all-in-one service for hosting AI powered semantic search and LLM retrieval-augmented generation (RAG) on your data.
Semantic search is the core feature. With traditional lexical search (google, duckduckgo, etc.) you search for a word or phrase and the search engine returns results that contain that word or phrase. With semantic search you search for a concept and the search engine returns results that contain that concept.
Arguflow creates embeddings for you, store them in a vector db, and coordinate metadata joins with a transactional db. It includes best in class API for data servicing and white-label UI's for semantic / full-text search and source-cited llm-chat. User can pick any SQL database compatible with diesel for &nbsp;OLTP storage and choose any vector database of their choice.
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Build AI powered semantic search and LLM retrieval-augmented generation (RAG) fast


Arguflow - Build AI powered semantic search and LLM retrieval-augmented generation (RAG) fast

Ghost Perl Webserver Strong AI thinks in English or Russian by spreading activation among concepts.

Ghost Perl Webserver Strong AI

The platform provides tools for AI, SOA, ETL, ESB, database, web application, data quality, predictive analytics, chatbot ..., in a revolutionary data language (MQL). The server is based on a new generation of AI algorithm, and on an innovative SOA layer to reach the WWD. 
 
Mentalese is the language of thought structuring the human brain. This language is able to accommodate different common languages and allows autonomy in a machine. WWD literally means 'World Wide Data'. It is a global strategy. A concept of widespread standardization of the exchange of data or intelligences between companies and softwares in the world.

The platform provides tools for AI, SOA, ETL, ESB, database, web application, data quality, predictive analytics, chatbot ..., in a revolutionary data language (MQL). The server is based on a new generation of AI algorithm, and on an innovative SOA layer to reach the WWD.

MentDB - Mentalese Database Engine

NFStream is a Python package providing fast, flexible, and expressive data structures designed to make working with online or offline network data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world network data analysis in Python. Additionally, it has the broader goal of becoming a common network data processing framework for researchers providing data reproducibility across experiments. NFStream extracts +90 flow features and can convert it directly to a pandas Dataframe or a CSV file.


NFStream - A Flexible Network Data Analysis Framework

Rubrix is a production-ready Python framework for exploring, annotating, and managing data in NLP projects.
Most annotation tools treat data collection as a one-off activity at the beginning of each project. In real-world projects, data collection is a key activity of the iterative process of ML model development. Once a model goes into production, you want to monitor and analyze its predictions, and collect more data to improve your model over time. Rubrix is designed to close this gap, enabling you to iterate as much as you need.

RUBRIX -  Python framework to explore, label, and monitor data for NLP

OSTINATO is a chess library that runs on the server (Scala), on the browser (ScalaJS) and on the REPL. It supports the following Chess notations (with variants): PGN, Algebraic, Figurine, Coordinate, Descriptive, ICCF, Smith and FEN. 

OSTINATO - A chess library that runs on the server (Scala), on the browser (ScalaJS) and on the REPL

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