Translator | Bugatti
Reviewer | Sun Shujuan
Open source is fertile ground for brewing revolutionary software, especially in cutting-edge fields such as artificial intelligence and machine learning. The open source ethos and collaboration tools make it easier for teams to share code and data and build on the success of others.
This article introduces 13 open source projects that are reshaping the world of artificial intelligence and machine learning. Some are sophisticated packages supporting new algorithms, others are more low-key. All of these are worth keeping an eye on.
A list of open source tools for AI and machine learning is incomplete without TensorFlow and PyTorch. Individually and collectively, these frameworks support some of the most experimental and important research in machine learning and artificial intelligence. At least several of the projects discussed in this article use them as base modules.
Programmers who need a little help with programming can benefit from FauxPilot. The system trains itself on existing production code and learns enough from it to make meaningful comments and suggestions. The project was inspired by GitHub Copilot, but FauxPilot lets you choose the codebase used for training. This extra layer of control prevents you from using code snippets from sources that may not approve their use. If you choose a training source and limit its use to those with the appropriate permissions and licenses, you're more likely to use programming help and code snippets that are clean and reliable.
One of the easiest ways to understand how a machine learning model “thinks” is to start feeding words into DALL-E, a very large open model using data collected from the web. Constructed from images and text descriptions. Enter a word and out comes an image that DALL-E thinks matches. Open source projects such as DALL-E Playground and DALL-E Mini make it easier for users to experiment with models. It’s part game and part portal into the underbelly of AI algorithms.
Real-time object detection (finding objects in images) is a tricky area of artificial intelligence. It is also critical for areas such as autonomous vehicles, robots and assistive devices that need to collect and transmit accurate environmental information. YOLOv7 is one of the fastest and most accurate open source object detection tools. Just feed the tool a bunch of object-rich images and see what happens next.
Deepfakes are videos and images that are created, altered, or synthesized with the help of deep learning. The most common example is replacing the face of a celebrity or politician with an existing video or image, often for fun but sometimes for more nefarious purposes. DeepFaceLab is open source deepfakes technology running on Python. In addition to replacing one face with another, it can also be used to remove wrinkles and other signs of aging.
Natural language processing (NLP) engines perform neural search and sentiment analysis, then extract and present information for human and machine users. While still clunky at times, the technology has become advanced enough to be used in a variety of applications and fields (Alexa is just one example). PaddleNLP is a popular open source NLP library that you can use to collect search sentiment and label important entities.
The traditional approach to AI success is to store data in a database and then extract and send it to a separate machine learning algorithm. MindsDB is a SQL server that integrates machine learning algorithms directly into the database. In-database machine learning (i.e. analyzing data where it is already stored) is a fast and efficient way to speed up your machine learning workflow.
The more details in a photo, the better, and Image Super-Resolution can add more details by increasing the image resolution. This open source tool uses a machine learning model that you can train to guess details in low-resolution images. With a good training set, the model can produce accurate details and clearer images.
Many businesses and large companies are replacing frontline customer service employees with chatbots, which means machines are learning to hold conversations. DeepPavlov combines essential machine learning tools like TensorFlow, Keras, and PyTorch to create chatbots for you to learn from. The results are a little weird, but with the right training, sometimes the results are practical.
A great way to turn your 3D models into gorgeous rendered scenes is to fire up Blender. While many think of Blender as a tool for filmmakers and animators, it is also a great example of applied AI. A rich interface and numerous plug-ins make it possible to create complex motion graphics or cinematic scenes. All it takes is a little creativity, and the Oscar nominating committee will soon be calling. The AI won't even ask for a share of the victory.
One of the most reliable foundations for exploring machine vision is the open source computer vision library OpenCV. It includes many popular algorithms for identifying objects in digital images, as well as specialized routines, such as one that can recognize and read car license plates.
Robocode is like The Hunger Games for your algorithm. This Java-based programming game pits your tank against other tanks in a battle for dominance. It's a fun pastime and might even be useful for testing new strategies for self-driving cars.
Original link: https://www.infoworld.com/article/3673976/13-open-source-projects-transforming-ai-and-machine-learning.html
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