Open Source Artificial Intelligence: Leading Projects – Datamation

  • Lauren
  • May 19, 2020
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Open source artificial intelligence projects don’t always get a lot of publicity, but they play a vital role in the development of artificial intelligence. Because these open source projects are often pursued as passion projects by developers (sometimes in colleges and universities), the advances are creative and particularly forward looking.
Typically freed from the constraints of a corporate setting (though some are supported by companies), these open source AI projects can dream big – and often deliver ground breaking machine learning and AI advances.
Also important: the advances from these leading open source AI projects fuel the larger AI sector. That is, a new idea from this month’s AI project ends up next year (or even next month) in a high end AI solution sold by a company.
Remember, if you know of additional top open source AI tools that should be on this list, please include them in the comments section below.
Open Source AI Projects

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Is there a developer who doesn’t know TensorFlow? It’s practically a household name. Developed by the Google Brain team for internal use at Google, TensorFlow is now one of the most well-known open source machine learning platforms. Google is also making a cloud-based version of TensorFlow available for free to researchers. Operating System: Windows, Linux, macOS, Android.


Originally created by the bright minds are UC Berkeley, Caffe has become a very popular deep learning framework. Its claims to fame include expressive architecture, extensible code, and speed. Operating System: Windows, Linux, macOS.


With more huge user base, H2O claims to be “the world’s leading open source deep learning platform.” In addition to the Open Source version, the company also offers a Premium edition with paid support. Operating System: Windows, Linux, macOS.

Microsoft Cognitive Toolkit

Clearly, Microsoft has moved into the world of open source. Formerly known as CNTK, the Microsoft Cognitive Toolkit promises to train deep-learning algorithms to think like the human brain. It boasts speed, scalability, commercial-grade quality and compatibility with C++ and Python. Microsoft uses it to power the AI features in Skype, Cortana and Bing. Operating System: Windows, Linux.

DeepMind Labs

Another very big name in AI and ML. Intended for use in AI research, DeepMind Lab is a 3D game environment. It was created by the DeepMind group at Google and is said to be especially good for deep reinforcement learning research. Operating System: Linux.


Developed at Carnegie Mellon University, ACT-R is the name of both a theory of human cognition and software based on that theory. The software is based on Lisp, and extensive documentation is available. Operating System: Windows, Linux, macOS.

StarCraft II API Library

You didn’t think AI was all work, did you? Google’s DeepMind and Blizzard Entertainment are collaborating on a project that makes it possible to use the StarCraft II video game as an AI research platform. It’s a cross-platform C++ library for building scripted bots. Operating System: Windows, Linux, macOS, Android, iOS.


The Numenta organization offers numerous open source projects related to hierarchical temporal memory. Essentially, these projects attempt to create machine intelligence based on current biological understandings of the human neocortex. Operating System: Windows, Linux, macOS.

Open Cog

A big ambition, to be sure: instead of focusing on a narrow aspect of AI such as deep learning or neural networks, Open Cog aims to create beneficial artificial general intelligence (AGI). The project is working toward creating systems and robots with the capacity for human-like intelligence. Operating System: Linux.
Stanford CoreNLP

This Java-based natural language processing software can identify the base forms of words, their parts of speech and whether they are names of companies or people, as well as normalizing dates and times. It marks up the structure of sentences in terms of phrases and syntactic dependencies, indicating which noun phrases refer to the same entities, identifying sentiment, extracting particular or open-class relations between entity mentions and getting quotes. It was designed for English but also supports a wide array of languages. Operating System: Windows, Linux, macOS.


Developed and used by Facebook – yes, they have deep resources – Prophet forecasts time series data. It’s implemented in R or Python and is fully automatic, accurate, fast and tunable. Operating System: Windows, Linux.


Originally an IBM Research project, SystemML is now a top-level Apache project. It describes itself as “an optimal workplace for machine learning using big data,” and it integrates with Spark. Operating System: Windows, Linux, macOS.


Deep learning can be thought of as the furthest edge of AI. Theano, geared for deep learning, describes itself as “a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.” Key features include GPU support, integration with NumPy, efficient symbolic differentiation, dynamic C code generation and more. Operating System: Windows, Linux, macOS.


Short for “Machine Learning LanguagE Toolkit,” MALLET includes Java-based tools for statistical natural language processing, document classification, clustering, topic modeling, information extraction and more. It was first created in 2002 by faculty and graduate students at the University of Massachusetts Amherst and the University of Pennsylvania. Operating System: Windows, Linux.


An example of cross-collaboration in the open source AI sector, DeepDetect has been used by organizations like Airbus and Microsoft. DeepDetect is an open source deep learning server based on Caffe, TensorFlow and XGBoost. It offers an easy-to-use API for image classification, object detection, and text and numerical data analysis. Operating System: Windows, Linux, macOS.


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