13 open source projects that are transforming AI and ML Computerworld

From deepfakes to natural language processing and more, the open source world is full of projects aimed at supporting software development at the frontiers of artificial intelligence and machine learning.

Open source is fertile ground for pioneering software, especially in cutting-edge areas like artificial intelligence (AI) and machine learning (ML). Open source ethics and collaboration tools give teams the ability to more easily share code and data, and build on the success of others. Here is a selection of 13 open source projects that are transforming the world of AI and machine learning.

TensorFlow and PyTorch. A list of open source tools for AI and machine learning wouldn’t be complete without a nod to Tensor Flow and PyTorch. Separately and together, these OG frameworks support some of the most experimental and important research in machine learning and artificial intelligence. At least a few of the projects discussed in this article actually use them as building blocks.

To install PyTorch, just select its preferences and run the installation command. Note that Stable represents the most currently tested and supported version of PyTorch. (Credit: PyTorch)

FalsePilot. Programmers who need a little help with coding can turn to False Pilot. The system trains on existing production code and learns enough to give structured feedback and suggestions. The project was inspired by GitHub Copilot, but FauxPilot selects the repositories the user wants to use for training. This additional layer of control prevents the use of code snippets from sources that may not approve of such use. If the user chooses their learning sources and limits them to those that have the proper permissions and licenses, the coding help and snippets used are more likely to be clean and reliable.

13 open source projects that are transforming AI and ML

With the FauxPilot project, accessible on GitHub, developers should be able to use Copilot with their own locally hosted suggestions. (Credit: GitHub)

SLAB. One of the easiest ways to get a feel for how machine learning models “think” is to start inserting words into SLAB, a very large open model built from images and text descriptions retrieved from the internet. A word is entered and an image is output, which DALL-E considers as corresponding. Open source projects like DALL-E Playground and DALL-E Mini facilitate model experimentation. It’s part game and part portal into the mind of an AI algorithm.

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(Credit: DALL-E)

YOLOv7. Real-time object detection, or finding objects in images, is a tricky area for artificial intelligence. It is also crucial for applications such as self-driving cars, robotics and assistive devices who must collect and transmit precise environmental information. YOLOv7 is one of the fastest and most accurate open source object detection tools. Just provide the tool with a collection of images full of objects and see what happens next.

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YOLOv7 helps in particular to estimate the pose of a person on a photo. (Credit: YOLOv7)

DeepFaceLab. Deepfakes are videos and images that are created, modified, or synthesized using deep learning. The most common example is the insertion of a celebrity or politician’s face into an existing video or image, usually for humorous purposes, but sometimes for more nefarious purposes as well. DeepFaceLab is an open-source morphing-like deepfake technology that runs on Python. Besides swapping one face for another, it can be used to remove wrinkles and other marks of age and experience.

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A use of DeepFaceLab where the face of Vladimir Poutine is inserted in a portrait of Kevin Spacey (actor of the series House of Cards). (Credit: DeepFaceLab)

PaddleNLP. Natural language processing (NLP) engines perform neural searches and sentiment analysis, then extract and present information to human and machine users. Although still sometimes clunky, this technology is becoming sophisticated enough to be used in a variety of applications and areas (Alexa is just one example). PaddleNLP is a popular open-source NLP library that anyone can use to glean search sentiment and flag important entities.

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PaddleNLP is an easy-to-use NLP library with a pre-trained model that supports a wide range of NLP tasks, from research to industrial applications. (Credit: PaddleNLP)

MindsDB. The traditional route to AI success is to store the data in a database and then extract it to send it to a separate machine learning algorithm. MindsDB is an SQL server that embeds machine learning algorithms directly into the database. In-database machine learning, or analyzing data where it is already stored, is a fast and efficient way to accelerate machine learning workflows.

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MindsDB offers to integrate predictions into users’ BI tools. (Credit: MindsDB)

Super-Resolution (ISR) picture. More detail is always better for photographs, and Super-Resolution Picture can add even more detail by increasing the resolution of the image. This open-source tool uses an ML model that can be trained to guess the details of a low-resolution image. With a good training set, the model can produce fine detail and a sharper image.

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Low resolution image (left), ISR output (center), bicubic scaling (right). (Credit: ISR)

DeepPavlov. Many businesses and large corporations are replacing the front lines of customer service with chatbots, which means the machines are learning to hold a conversation. DeepPavlov brings together basic machine learning tools like TensorFlow, Keras, and PyTorch to create chatbots you can learn from. The results are bizarre, strange and even, at times, with the proper training, useful.

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Machines learn to tell words apart. (Credit : DeepPavlov)

Blender. The best way to convert three-dimensional models into lavishly rendered scenes is to use blender. Although many consider it a tool for filmmakers and animators, Blender is also a great example of applied AI. A rich interface and numerous plugins make it possible to create complex animated images or cinematic views.

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With its high-quality editing and animation tools, Blender is used for short films, commercials, TV series and feature films. (Credit: Blender)

OpenCV or Open Source Computer Vision Library. One of the most fertile bases for exploring computer vision is OpenCV, the open source computer vision library. It includes many popular algorithms for identifying objects in digital images, as well as specialized routines such as the one for locating and reading car license plates.

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The opencv.org site offers an introductory course on the basics of computer vision technologies that addresses deep learning (Credit: opencv.org).

Robocode. This programming game Java-based enable is a fun hobby that can even be useful for testing strategies for self-driving vehicles.

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Robocode’s built-in robot editor. (Credit: Robocode)

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13 open source projects that are transforming AI and ML Computerworld


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