Open-source AI software has emerged as a key driver in the digital era, with the potential to revolutionize industries. This powerful tool transforms how intelligent systems are developed, thereby shaping the future of various industries.
The open-source software movement produced iconic innovations like the Firefox web browser, Apache server software, and the Linux operating system—the genesis of the Android OS that currently powers 86% of the world’s smartphones. It also fostered a mindset around the continuous improvement of tools that can be collaboratively shared, improved upon, and distributed.
Today, machine learning and artificial intelligence (AI) are beginning to enmesh themselves in the open-source world.
What is open-source AI?
Open-source artificial intelligence is the application of open-source practices to the development of artificial intelligence resources.
Many open-source artificial intelligence products are variations of other existing tools and technology that major companies have shared as open-source software. Companies often develop closed products in an attempt to keep a competitive advantage in the marketplace.
Popular open-source artificial intelligence project categories include large language models, machine translation tools, and chatbots. However, for software developers to produce open-source artificial intelligence resources, they must trust the various other open-source software components they use in their development.
Understanding the concept of open-source AI software
To gain a better grasp of this term requires understanding a few others.
Open source is a software development term that means any programmer can jump in and work with it, the goal being to develop robust software in a shorter amount of time. This is a great way to leverage the novel ideas of the best minds to fuel progress in technology.
Artificial Intelligence is a branch of computer science that develops programs and algorithms (step-by-step processes designed to solve a problem or answer a question) that help make various machines operate in more human-like ways.
There are several subfields of this science, including:
- Natural language processing (NLP). Focuses on developing natural interactions between humans and computers. Specialized software helps machines process human language, create understandable words, and interact with humans through language.
- Machine learning (ML). Prioritizes a machine’s ability to analyze information and use it to make recommendations or decisions based on the data sets it has provided.
- Computer vision. This is all about creating machines that can understand and then interpret visual information. Robotics that can physically perform tasks without human micromanagement, including interaction with humans.
At the moment, companies verify we are human by having us choose photos from a set with one thing in common, such as cars or volcanoes. If we were to see a car at the base of an active volcano, we can extrapolate that the car will be damaged. Machines are still developing these abilities.
In a nutshell, open-source AI can be defined as software engineers collaborating on various artificial intelligence projects that are open to the public to develop. The goal is to better integrate computing with humanity.
10 open-source AI software to try out today
OpenAI
OpenAI is all over the news, and for good reason — it’s changing the game of natural language processing (NLP) AI programs. They offer a model called Codex that changes natural language into code in the programming language you specify.
Additionally, like other open-source AI projects, you can access their models and customize the code yourself.
OpenAI is mastering what Alexa/Siri does and taking the next step in Industry 4.0. This AI can synthesize its own natural language answers from the information it finds instead of just pointing to a website and reading it.
Amazon Web Services
If you have code to run or want a familiar place to start building, you can do it for free on AWS. The platform also stores the results/output of your programs. In addition, AWS offers numerous value-added features for business marketing, such as customizing your code for their content delivery network and managing task coordination for your various cloud applications, all for free.
It is best for those who have a handle on coding but could use some support services adjacent to development — including business features to help you level up toward Industry 4.0.
TensorFlow
TensorFlow is an entire support structure for programmers who want to help each other create something novel while reaping the benefits of other experts’ existing models.TensorFlow is one of the most robust AI platforms and offers training videos to help jumpstart your success.
This platform supports several programming languages, including Swift, Python, and JavaScript — the most common programming language used on Earth.
RNN
RNN is an emerging framework for supervised learning and has an extremely flexible and intuitive interface. It’s also suitable for designing algorithms for “deep learning,” which can be used to distinguish between “like” and “dislike” in data sets.
RNN is the second most popular deep learning framework for neural processing and natural language processing. The user community has been extraordinarily active and very helpful, and the project is under active development. According to neural processing experts, it’s not the right choice for general ML coding because of the extra layers of abstraction.
Neural processing expert Joe Callaghan compared RNN to WATM and said, “RNN is too hard to learn, but a lot of fun to experiment with.”
PyTorch
PyTorch, like TensorFlow, is a one-stop shop for transforming ideas into functional applications. It’s an entire framework created to support various aspects of open-source AI project development, including vast libraries and datasets to pull from.
This platform is easy to use for developers who already code with Python. Its object-oriented approach helps bundle up usable chunks of code that do just one job. This known and reliable “object” can then be plugged into a more extensive sequence to do a more complicated job, helping programmers help each other.
Pro tip: Programmers fluent in Python flourish here, but it also has a C++ interface for those who don’t code with Python.
Theano
Theano is an open-source Python AI software for deep learning and is also popular in the neural processing and data science communities. It’s widely known for making it easy to implement complex neural networks by abstracting away the neural network components (such as the layers and hidden layers).
It’s often used to build and train AI models on graphics processing units (GPUs) and has been adopted by Facebook for both training and deploying AI applications.
Theano comes with a library of algorithms that perform neural network operations on data frames. It works with Python, C++, Java, Julia, Scala, and Tensorflow and is currently the most popular AI framework used by developers who use either Tensorflow or Theano. Theoretically, Theano can be used on any platform, but most Theano developers use Tensorflow and Tensorboard.
Theano is a deep-learning framework with a comprehensive library of complex algorithms. It is used for training models to perform image classification, object detection, language translation, and speech recognition. The software has the most extensive library of popular machine learning algorithms, easily combined with Tensorflow.
Tensorflow and Theano are used for most deep-learning applications. However, they are not the best choice for NLP.
Rasa
Rasa is great for building conversational AI (chatbots) and deploying it via the cloud for free. It’s flexible and touted as “future proof” because it’s been designed so you can plug any NLP or ML model into Rasa to give you increasingly accurate results as technology improves with time.
It is best for branded conversational AI for enterprises that come with built-in integrations for social messaging like Slack and Facebook.
Keras
Billed as ‘designed for humans’, Keras is an application programming interface (API) that allows you to quickly and easily share the front end of your deep learning models. You can export your models from Keras and run them in browsers, iOS, and Android. Their Python libraries tend to focus on artificial neural networks.
Keras is best for programmers who prefer a more streamlined user interface while working with the newest versions of TensorFlow. It is also perfect for those who want to simplify interaction with the software as they build it.
Caffe2
Caffe2 is a powerful open-source library that makes it easy to create deep-learning models using the PyTorch framework. You can quickly develop scalable models and get rid of the typical computations involved in traditional models. That is why with Caffe2, you can make the most of our machines and get maximum efficiency out of them.
However, it is essential to understand that Caffe2 is not a traditional framework for AI training. Instead, it is a trained inference engine based on Neural Networks. The end goal of Caffe2 is to provide the best results in a highly efficient manner compared to Caffe.
It is a Python library, and there is no need to worry about any other libraries or third-party applications as you can rely on it to furnish the whole framework you will use in the project.
OpenCV
OpenCV is well-known for its open-source AI platform for computer vision. If TensorFlow has an undergrad degree in general AI, OpenCV holds a master’s in AI vision. And it works pretty much everywhere because its library was written in C, which it claims can be ported to everything from “PowerPC Macs to robotic dogs.”
It includes a new C++ interface, and wrappers have been developed for Java, Python, and other languages to encourage cross-language development. It is best for developing AI specifically for computer vision applications.
How do open-source AI software differ from proprietary AI systems?
Open source AI refers to AI software whose source code is freely available for modification and enhancement by anyone. This starkly contrasts with proprietary AI systems, where the source code is kept secret and controlled by the company that developed it.
The open nature of open-source AI fosters a collaborative environment, enabling developers worldwide to contribute. This results in the acceleration of innovation and development.
Open-source AI is a significant catalyst in intelligent systems’ rapid and collaborative development. Its democratizing nature has opened up opportunities for developers across the globe, regardless of their affiliation with large corporations or research institutions.
Here are three ways open-source AI is accelerating the development of intelligent systems:
Provides readily available frameworks
One of the primary ways open-source AI contributes to faster development is by providing readily available frameworks and tools. These resources eliminate the need to build systems from the ground up, saving developers considerable time and effort. They can instead focus on refining and customizing these tools to suit their specific project requirements, thereby accelerating the development process.
Harnesses a collaborative process
Open-source AI promotes a collaborative environment. Moreover, developers can contribute to the code, suggest improvements, and share unique solutions to common problems. This collaborative approach not only speeds up problem-solving but also creates more robust and efficient systems.
It is a global brainstorming session where the best minds in AI work together to push the boundaries of what is possible.
Encourages continuous learning
Additionally, open-source AI encourages continuous learning and improvement. The entire community benefits as developers from diverse backgrounds contribute their knowledge and skills. Moreover, they can learn from each other’s successes and failures, leading to a rapid iteration of ideas and faster advancement in AI technologies.
The future of open-source AI software
The future of open-source AI software looks promising as it continues to gain popularity and acceptance. With the rise of cloud computing, big data, and artificial intelligence, open-source projects in these domains are likely to play a pivotal role in shaping the technological landscape. Furthermore, as more organizations recognize the strategic value of open source, contributions from the corporate sector are expected to grow, providing more resources for the development and sustainability of open-source projects.
In conclusion, open source is more than just a way of developing and distributing software; it’s a culture that promotes openness, collaboration and shared progress. As the digital world evolves, open-source principles are likely to influence not only software development but also other areas of innovation and community building.
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