Open source AI projects don’t always get a lot of publicity, but they play a crucial role in the development of AI. Because these open source projects are often picked up by developers as inspiration projects, these advances are creative and particularly forward-looking.
These open source AI projects are often free from the constraints of an enterprise development environment and can be a dream come true — and often lead to breakthrough machine learning and AI advances. Just as important: advances in these leading open source AI projects drive advances in the larger ai field.
If you know of other top open source AI tools that should be on this list, please share them with us in the comments below.
1, PyTorch
PyTorch has all the elements of a leading open source AI project. It focuses on machine learning, arguably the most popular application of ARTIFICIAL intelligence in the development phase of emerging technologies. More importantly, developers and AI engineers can build PyTorch on PyTorchAWS and PyTorch on Azure on top Cloud computing platforms, as well as Google Cloud and Alibaba. PyTorch provides neural networks, an essential element in the development of ARTIFICIAL intelligence.
Open Neural Network Exchange
The Open Neural Network Exchange, developed by Microsoft and Facebook, provides some very powerful tools, especially the ability to recycle fully developed Neural Network models (which have spent a lot of time training in the system) into various other systems. In essence, open neural network switching greatly extends the usefulness of existing models by enabling this migration. ONNX is expected to become more and more popular in the coming years.
3. IBM AI Fairness 360
AI Fairness 360 is an open source solution to the growing concern about bias in AI algorithms. The tool provides algorithms that enable developers to scan maximum likelihood models to find any potential biases, which is an important part of fighting bias and certainly a complex task. Importantly, AI fairness allows AI engineers to explore algorithms throughout the development lifecycle. The tool can be set to work automatically. The foundation of the tool is a framework for checking dependencies; Does this correlation create a prediction that implies harmful stereotypes?
4, Keras
Keras is a rarity in the world of open source AI projects: it advertises itself as “an application programming interface designed for humans, not machines.” As a Python deep learning API, Keras can interoperate with high profile AI projects like Antao and Microsoft Cognitive Toolkit. Developers and AI engineers use it as an ML library to build prototypes in a relatively easy way. Also helping with ease of deployment, Keras can run on hybrid processor hardware.
5, Accord.NET
As the name implies, Accord.NET is used. The.net framework. This is a.net ML learning framework that provides image and audio libraries encoded in C#. It is forward-looking because it provides a platform for the development of business-grade applications, including signal-processing-oriented applications, audio-visual toolsets, and statistical applications. If you’re just getting started, Accord.NET also includes template applications so you can start building faster.
6, GPT – 2
Of course, open source AI technology is making a splash, and the Creative Pretraining Transformer 2 (GPT-2) was released by OpenAI in 2019. GPT utilizes deep neural networks, which use layers of software to process any number of inputs. GPT 2 is known to process text, from translation to creating text that, at best, can be very similar to human-written text. In addition, it is a very powerful learning tool that can synthesize and adapt data with great accuracy.
7, Cheatsheets AI
If you are a language/AI developer who can lend a hand in an open source language/AI project, this project is very useful. Not so much a project as a learning tool to help you keep up with ai/AI projects, from Keras to Scripy to PySpark to Dask. The guidance it provides is in-depth and necessarily complex. While Cheatsheets AI is designed for “AI novices,” you actually need some prior training to use this resource.
8 TensorFlow.
Are there developers out there who don’t know TensorFlow? It’s almost a household name. Developed by the Google Brain team for internal use at Google, it is now one of the best-known open source machine learning platforms. Google has also made a cloud-based version of TensorFlow available for free to researchers.
9, Caffe
Caffe, originally created by the elite at the University of California, Berkeley, has become a hugely popular deep learning framework. Its reputation includes presentation architecture, extensible code, and speed.
10 and H2O
With its huge user base, H2O calls itself “the world’s leading open source deep learning platform.” In addition to the open source version, the company also offers premium versions with paid support.
11. Microsoft Cognitive Toolkit
Clearly, Microsoft has entered the open source world. Microsoft Cognitive Toolkit, formerly known as CNTK, promises to train deep learning algorithms to think like the human brain. It has speed, extensibility, business-grade quality, and compatibility with C++ and Python. Microsoft uses it to support artificial intelligence features in Skype, Cortana and Bing.
12, DeepMind Labs
Another big name in AI and ML. The DeepMind lab is designed for artificial intelligence research and is a 3D gaming environment. It was created by Google’s DeepMind group and is said to be particularly suited for deep reinforcement learning research.
13, the ACT – R
Act-r, developed at Carnegie Mellon University, is the general term for the theory of human cognition and the software based on it. The software is based on Lisp and has a lot of documentation. Operating system: Windows, Linux, macOS.
StarCraft II API Library
You don’t think AI is all about office work, do you? Google’s DeepMind and Blizzard Entertainment are collaborating on a project that could make the StarCraft II video game a research platform for artificial intelligence. This is a cross-platform C++ library for building scripting robots.
15, Numenta
The Numenta organization provides a number of open source projects related to hierarchical temporal memory. In essence, these projects seek to create machine intelligence based on current biological understanding of the human neocortex.
16, the Open Cog
Admittedly, this is a big ambition: Open Cog aims not to focus on a narrow aspect of AI, such as deep learning or neural networks, but to create beneficial artificial General intelligence (AGI). The project aims to create systems and robots with human-like intelligence.
17, Stanford CoreNLP
The Java-based natural language processing software can identify the basic form of words, their part of speech and whether they are company or person names, as well as normalize dates and times. It marks the structure of sentences according to phrases and syntactic dependencies, indicates which noun phrases refer to the same entities, identifies emotions, extracts specific or open class relationships between entity references, and obtains citations. It is designed for English, but also supports multiple languages.
In the 18th and Prophet
Developed and used by Facebook — yes, they have deep resources — prophets predict time series data. It is implemented in R or Python, and is fully automatic, accurate, fast, and tunable.
19, SystemML
SystemML was originally an IBM research project and is now a top-level Apache project. It describes itself as “the best place to work for machine learning using big data” and integrates with Spark.
20, Theano
Deep learning can be considered the furthest edge of AI. Deep learning-oriented Anano describes itself as “a Python library that allows efficient definition, optimization, and evaluation of mathematical expressions involving multidimensional arrays.” Key features include GPU support, integration with NumPy, efficient symbol differentiation, dynamic C code generation, and more.
21, MALLET
MALLET stands for “Machine Learning Language Toolkit” and includes Java-based tools for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and more. It was first founded in 2002 by faculty and graduate students at Amherst University in Massachusetts and the University of Pennsylvania.
22, DeepDetect
As an example of cross-collaboration in the field of open source ARTIFICIAL intelligence, DeepDetect is already being used by organisations such as Airbus and Microsoft. DeepDetect is an open source deep learning server based on Caffe, TensorFlow, and XGBoost. It provides an easy-to-use application programming interface for image classification, object detection, and text and digital data analysis.
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