There are many open-source projects in Artificial Intelligence that are never heard of. But many of these projects also grow to be part of the fundamentals in Artificial Intelligence. Take TensorFlow for instance. Everybody has heard about TensorFlow in the AI world! But it was initially just a project by the Google Brain team for internal Google use. Similarly, most of these open-source projects start as passion projects of developers in universities or tech companies like Google, Microsoft, etc. That is why they are so forward-thinking and they push the envelope in Artificial Intelligence. So we discuss the Top Open Source Projects using Artificial Intelligence in this article that is now a foundation in the AI world.
These open-source projects using AI were created by the developers in top companies such as Google, Facebook, Microsoft, IBM, etc. They are mostly groundbreaking projects that have created new innovations in the fields of Artificial Intelligence and Machine Learning. And even more important is the fact that the advances from these open-source projects have benefited the AI sector as a whole with even more funding and innovation provided for newer projects. So let’s check out these trailblazing projects now!
1. Google Open Source Projects
Google believes that open source is good for everyone as it leads to collaboration and the further development of technology. There are more than 2000 projects in the Google opensource, some of which have given birth to popular technologies. Let’s see the most famous ones:
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TensorFlow: It is a free end-to-end open-source platform that has a comprehensive, flexible variety of tools, libraries, and resources for Machine Learning. It was developed by the Google Brain team and is also available on the Google Open Source Platform. It is very easy to build and train Machine Learning models with high-level API’s such as Keras using TensorFlow. You can also deploy the machine learning models anywhere including the cloud, the browser, on-premises, or on the device regardless of the language you use. There are many versions of TensorFlow for various uses such as TensorFlow Lite for mobile devices, TensorFlow Extended for the full experience, TensorFlow.js for JavaScript environments, TensorFlow Rust for Rust bindings, etc. Google also extensively uses TensorFlow in many of its internal products including Google Search, Google Maps, Gmail, Google Translate, Android, Google Photos, YouTube, Google Play, etc.
- DeepMind Lab: It is an artificial intelligence company that was acquired by Google in 2014. It is focused on solving various problems and making breakthroughs in the field of artificial intelligence. The DeepMind Lab is an open-source 3D game platform that was created for research and development in the fields of artificial intelligence and machine learning. It has many tasks relating to navigation and puzzle-solving that provide a foundation in deep reinforcement learning. The primary language used for the DeepMind Lab is C and it is used internally at DeepMind for training the learning algorithms for research purposes.
2. Facebook Open Source Projects
Facebook has a large repository of open source projects and it mainly believes in empowering the community using open-source technology. So let’s see some of the most famous open-source projects on Facebook:
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PyTorch: It is an open-source Python package that is primarily focused on Machine Learning. PyTorch provides tensor computation as well as deep neural networks. PyTorch can also be extended if required using various Python packages such as NumPy, SciPy, Cython, etc. PyTorch also has libraries for different functionalities like Captum for model interpretability, skorch for scikit-learn compatibility, PyTorch Geometric for Deep Learning on graphs, etc. PyTorch provides TorchScript, which facilitates a seamless transition between the eager mode and graph mode. Moreover, the torch.distributed backend provides scalable distributed training for Machine Learning and optimized performance. Facebook Open Source provides details about PyTorch and links to both its website and the Git repository.
- Prophet: It is an open-source forecasting procedure in Python and R. This is mainly for data scientists and data analysts so that they can obtain fast and accurate forecasts. The forecasts are automated but they can be tuned by hand according to specifications. Prophet is mainly for forecasting non-linear trends that fit into the daily, weekly, and yearly mold and also with traces of historical data. Facebook also uses Prophet in-house in many different applications for producing reliable and fast forecasts that are useful in planning and goal setting. Since Prophet is open-source software, it can be downloaded on CRAN and PyPI. It was released by Facebook’s Core Data Science team and Facebook Open Source provides links to both its website and the Git repository.
3. Microsoft Open Source Projects
Microsoft provides many open source projects that developers can contribute to. Some of these include the following:
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Microsoft Cognitive Toolkit: Microsoft Cognitive Toolkit is an open-source framework that allows developers to understand their data sets and harness the intelligence within them using Deep Learning. This framework was developed by Microsoft Research and initially released on 25 January 2016. It allows you to develop popular deep learning models such as feed-forward DNNs, convolutional neural networks, and recurrent neural networks easily while providing access to multiple GPUs and servers providing parallelization across the backend. There are many companies that use Microsoft Cognitive Toolkit to create AI solutions including Bing, Skype, Cortana, Xbox, etc. These companies can use the Toolkit in a customizable manner as per their requirements with their individual networks, and algorithms.
- Open Neural Network Exchange: The Open Neural Network Exchange is an open-source artificial intelligence ecosystem that was developed by Facebook and Microsoft. The ONNX is necessary because once a Neural Network is trained and evaluated on a particular framework, it is extremely difficult to port this on a different framework. While there are various choices for an initial framework such as PyTorch, Microsoft Cognitive Toolkit, TensorFlow, Apache MXNet, etc. porting the network later is an issue. This somewhat reduces the capabilities of Machine Learning but the Open Neural Network Exchange is the perfect solution to this problem. It allows for the reuse of trained neural network models across multiple frameworks. Now ONNX will become an essential technology that will lead to increased interoperability among Neural Networks. ONNX is available both on the Facebook and Microsoft open-source project pages along with its Git repository.
4. IBM Open Source Projects
IBM has open source projects across a wide breadth of technology. These are critical in pushing innovation and growth in technology into the future. The most popular IBM open-source projects include:
- Watson Developer Cloud: Java SDK: The IBM Watson Cloud allows companies to inject Artificial Intelligence into their applications so that they can make more accurate predictions, automate the company decisions and processes, and obtain optimized solutions. The Watson Cloud Java SDK provides access to all the Watson Developer Cloud services and users can use these facilities without becoming a REST expert. So you can easily add cognitive capabilities to your Java applications using the Watson Developer Cloud Java SDK. Ans this is totally open source and available under the Apache 2 license for free. Developers can also use the Java SDK as the beginning point to access the whole gamut of Watson Developer Cloud services and add them to their company applications.
All of these open source projects given above have contributed a lot in the field of Artificial Intelligence. They have radically changed the way that Artificial Intelligence is used in the modern tech industry. And even more importantly, they have provided an equal footing to smaller and medium-sized companies who can use this open-source technology to enhance their AI infrastructure and compete with even the tech giants on a global level.