Artificial intelligence is a promising technology that has the potential to radically change human perception of the world, just as it did after the advent of the steam engine and the personal computer.

Top 7 ranking of the best open source artificial intelligence

1. Stable Diffusion - Artificial intelligence for text to image conversion

Stable Diffusion is an innovative neural network text-to-picture engine that is open source and can be downloaded onto a personal computer. It uses artificial intelligence-based algorithms and latent diffusion techniques to create unique images based on text descriptions and specified parameters.

What Stable Diffusion can do

In addition to creating images from text, Stable Diffusion can perform a variety of tasks, such as transforming images into artistic styles, generating 3D objects, adding details to photos and creating animations for videos. This artificial intelligence can be trained using your own work and examples to solve more complex and specific tasks.

2. ComfyUI - Artificial intelligence for generating images from text

ComfyUI is a powerful and modular graphical user interface for steady-state diffusion with a GUI and node-based interface. This tool allows users to design and run complex steady-state diffusion pipelines using a flowchart based interface. Compatible with both SD1.x and SD2.x, ComfyUI includes numerous enhancements such as the ability to re-run only the modified parts of the workflow.

What is ComfyUI for?

Key features of ComfyUI include support for loading control point models and safe tensors, as well as a variety of scaling models including ESRGAN, SwinIR, Swin2SR and others. The tool also allows you to save and load workflows in JSON format, as well as create and load complete workflows from PNG files.

In addition to generating images from text, ComfyUI can process artistic images, generate 3D objects and graphics, add details to photos and create animations for videos. With the ability to train artificial intelligence based on its own work and examples, ComfyUI opens up new horizons for solving more complex image creation tasks.

3. Invoke AI - Artificial intelligence for image generation from text descriptions

Invoke AI is an advanced tool for working with neural networks based on Stable Diffusion technology. The main features of Invoke AI are

1. Choice of neural network versions - users can choose from different versions of neural networks to achieve the best results in image retrieval.
2. Image generation from text description - the ability to generate detailed images from text descriptions.
3. Creating new images from existing images - users can use their own images as a base and add new elements according to textual queries.
4. Create new drawings from simple sketches - the ability to transform simple drawings into more detailed and sophisticated works.

Invoke AI has a full graphical interface that allows users to choose from 12 trained neural networks and configure the necessary parameters in a convenient and intuitive way. Although under the hood the processes are still translated into commands in the terminal, this process is made as simple and accessible as possible for the user: just enter a query, adjust the parameters with the mouse and get the desired result.

4. Whisper AI - Artificial intelligence for speech-to-text conversion

Whisper AI is a free tool that can convert speech to text in more than 96 different languages with extremely high quality.

What Whisper AI can do

1. Whisper AI can transcribe different types of audio and video files, making it a versatile tool for transcription needs.
2. Whisper AI's ability to automatically detect the language used in a file can save time and improve transcription accuracy.
3. The ability to transcribe and translate audio using artificial intelligence technology has the potential to break down language barriers and facilitate communication between people from different cultures.

Whisper AI's accuracy is impressive, but still requires some manual adjustment to achieve full accuracy.

5. DeepFaceLab - Artificial Intelligence for Video Editing

DeepFaceLab differs from traditional video editing tools in that it uses advanced deep learning algorithms to achieve realistic face replacement effects in video. The process of working with DeepFaceLab is as follows:

1. Selection of video material: The user selects the source video (face source) and the target video (where the face will be inserted).
2. Face analysis: The software analyses the facial features, expressions and movements in both videos. This includes identifying key points of the face, facial expressions and other important features.
3. Face Transfer: Using deep learning algorithms, DeepFaceLab seamlessly transfers the face from the source video to the body in the target video. This includes adjusting lighting, skin colour and other factors to achieve maximum realism.

DeepFaceLab is a powerful deepfake creation tool that enables users to create high-quality, realistic face replacement videos. Its ability to accurately analyse and adjust faces makes it an ideal choice for a wide range of applications, from creative projects to more serious applications such as film production.

6. Coqui-ai - Artificial intelligence for video creation

Coqui is an innovative AI tool dedicated to the creation of video content. This tool is an ideal solution for those looking for an easy and efficient way to create AI-assisted videos, convert text to speech, create voice-overs and convert text to video with AI avatars.

Coqui features and benefits

1. Easy to use: Coqui allows users to create videos without in-depth knowledge of video editing.
2. Process automation: Users simply upload their images or videos and Coqui automatically converts them into videos with the right length and structure.
3. Wide range of templates and styles: Coqui offers a wide variety of templates and styles, allowing users to customise their videos according to their personal preferences.
4. Artificial Intelligence: The tool uses advanced AI technology to convert text to voice and video, ensuring high quality and realistic voiceovers and avatars.

Coqui offers great opportunities for audiovisual projects, providing users with a powerful tool to create high quality video content. This makes it an ideal solution for a wide range of applications, from personal projects to professional video production.

7. Oobabooga: Artificial Intelligence Voice Chat

Oobabooga represents a fresh and innovative departure from the traditional ways in which open source software developers approach image creation. It stands out as one of the leading and unrivalled systems for efficient and rapid testing with text-based AI models. Unlike others, Oobabooga focuses on the development of AI-generated dialogue and characters rather than image generation.

Top 10 ranking of artificial intelligence frameworks

1. TensorFlow: a reliable neural network framework

TensorFlow is a machine learning library, a set of technologies that allows you to train artificial intelligence to solve various tasks. The library was originally developed for Python and is most commonly used with it.

TensorFlow implementations exist for other languages: C#, C++, Go, Java, Swift, etc. They are used less frequently than the main one, mainly to write code for specific platforms. The library itself is written in Python, using fast and efficient C++ for solving mathematical problems. It therefore works efficiently with complex calculations.

The library was developed by Google as an extension of their internal library. TensorFlow is free and open source, available on GitHub, and is actively supported by a community of enthusiasts. The name is derived from "flow tensor" and is made up of two concepts: a tensor and a data stream.

What is TensorFlow used for?

The library itself contains many tools for different areas of ML, but is most commonly used to work with neural networks. These are structures inspired by the structure of neural networks in the human nervous system. Neural networks are made up of software elements - "neurons" - and connections between them, which allows them to learn. TensorFlow works with conventional and deep neural networks of various types: recurrent, convolutional and others. It is also used in machine learning.

Examples of where the technology is used include natural language, image and handwriting recognition, various classification or clustering tasks, and large data processing.

What we like: The TensorFlow platform supports multiple programming languages including Swift, Python and JavaScript.

Price: Free.

2. PyTorch: The Best Choice for Machine Learning

PyTorch is a machine learning framework for the Python programming language. It includes a set of tools for working with models and is used in natural language processing, computer vision and other similar fields.

The framework is based on Torch, a library for the Lua language designed for mathematical computation and machine learning. However, it is unique in that it follows the Python style and programming philosophy. Some say that PyTorch is one of the most "Python-like" ML frameworks.

PyTorch is open source and distributed free of charge. It is currently mainly supported and developed by Facebook's artificial intelligence team.

A whole ecosystem of libraries for different purposes has been built around the framework. The result is a comprehensive and powerful tool for solving machine learning problems.

Who needs PyTorch and why?

The PyTorch framework is used by machine learning specialists working with Python, one of the most popular languages in the industry. PyTorch is specifically focused on deep learning, a subset of ML that uses multi-layer models and neural networks. The model can learn from its own data processing, allowing it to solve more diverse tasks than classical ML with highly specialised models.

Deep learning, and PyTorch in particular, is most commonly used in the following industries

• recognising patterns in images;
• computer vision, detection of moving objects;
• search for patterns, analysis of data, including unstructured data;
• natural language processing, speech recognition and machine translation;
• create machine-generated descriptions for images;
• analysing texts and searching for information in them;
• generation of textual content and images.

All of these roles are relevant and needed in a variety of industries, from analysis to creativity.

What I like: Programmers with deep knowledge of Python are actively working here. And for those not limited to Python, there is a C++ interface.

Price: free.

3. Keras: simplifying interaction with artificial intelligence models

Keras is a library for the Python programming language designed for deep machine learning. It allows you to quickly create and configure models - schemes that distribute and compute information during training. However, Keras does not perform complex mathematical calculations and is used as an add-on to other libraries.

As of version 2.3, Keras is an add-on to the TensorFlow library, which is needed for machine learning. TensorFlow performs all the low-level computations and transformations and acts as an engine, a mathematical core. Keras, on the other hand, manages the models used for the computations.

Up until version 2.3, Keras could use the Theano or CNTK math libraries as its engine. But in newer versions, support has been dropped, and now the library only works with TensorFlow.

Keras is designed to be a flexible, modular library that is easy to customise and modify. It is free and open source for everyone to see.

The name "Keras" means "horn" in Greek. This is a reference to the lines from the Odyssey.

Keras has a narrow focus. It is a tool for machine learning specialists working in Python: the most widely used language because of the convenience of mathematical calculations. Keras is used by developers who create, configure and test machine learning and artificial intelligence systems, especially neural networks.

What you need Keras for:

• Convenient creation of models for training.
• Setting up layers in models - usually, selecting the right number of layers is necessary for accuracy.
• Processing input and output of information from the model.
• Conversion of input data to the learning model.
• Convenient selection of datasets for training.
• Visualisation of the model.
• Preparing the model for operation, defining its error functions and optimisers.
• Training and testing of the model.
• Building and initial launch of a machine learning application.

You can do all of the above without Keras, but it will take longer and be more complicated. Keras acts as a software interface that simplifies things. Some call it an API.

Pros: This option is ideal for programmers who prefer a more intuitive and simpler interface when using the latest versions of TensorFlow, making it easier to work with the software while creating it.

Price: free.

4. OpenAI: a first in natural language processing

The OpenAI platform is a truly revolutionary breakthrough in natural language processing (NLP). Its Codex model can transform natural language into code in any programming language. OpenAI models are open source and customisable, making them an ideal tool for collaboration and customisation.

**Why is OpenAI important?

• Research: OpenAI conducts cutting-edge research in the field of artificial intelligence. Natural language processing and machine learning are the areas in which OpenAI has been most successful.
• Tools and models: Thanks to the creation of GPT-3 and DALL-E tools, researchers and developers have the opportunity to improve AI systems and solve various problems.
• Education and outreach: To facilitate the understanding of artificial intelligence and its potential impact on society, OpenAI is engaged in educational activities. This includes organising events and conferences, publishing articles and research, and providing resources and materials to educate students at various levels.
• Responsible AI: OpenAI is actively working to ensure that artificial intelligence is developed in a responsible and safe way. It identifies and promotes best practices in the development of artificial intelligence and works to address potential ethical and social issues that arise from the development of artificial intelligence.

What we like: The Codex model can convert natural language into code in any programming language.

Price: $20 per month for access to GPT-4.

5. OpenCV: mastering computer vision

OpenCV (Open Source Computer Vision Library) is an open source library for working with computer vision algorithms, machine learning and image processing. It is written in C++, but also exists for Python, JavaScript, Ruby and other programming languages. It runs on Windows, Linux and MacOS, iOS and Android.

Where OpenCV is used

OpenCV can be used wherever computer vision is needed. This branch of computer science uses technologies that allow a device to "see", recognise and describe images. Computer vision provides precise information about what is in the image, with a description, characteristics and dimensions (with a degree of accuracy).

The library also works with machine learning, a field that teaches algorithms to behave in a certain way.

OpenCV is used for

• Robotics - for robot orientation, object recognition and interaction;
• Medical technologies - to develop accurate diagnostic methods, such as 3D organ visualisation during MRI;
• Industrial technologies - for automated quality control, label reading, product sorting, etc;
• Security - to create 'smart' video surveillance cameras that respond to suspicious activity, read and recognise biometrics;
• Security - to create 'smart' video surveillance cameras that respond to suspicious activity, read and recognise biometrics;
• Transport - for the development of autopilots.

What we like: Developing artificial intelligence specifically for computer vision applications.

Price: free.

6. H2O.ai: Accelerating the development of artificial intelligence

Fast, scalable and reliable solutions are considered essential for business success. The developers of H2O.ai strive to create a fast, scalable and open machine learning platform.

H2O.ai supports multiple deployment options, including single node, multi-node cluster, and Hadoop or Apache Spark clusters. H2O.ai is written in Java and therefore supports the Java API. Since a Scala server typically runs on a Java VM, H2O.ai also supports the Scala API. In addition, multi-purpose interfaces are available for Python and R. R and Python programmers can take advantage of H2O.ai's algorithms and features with the h2o R and h2o Python packages. R and Python scripts using the h2o library interact with H2O clusters using REST API calls.

What is H2O.ai for?

H2O Driverless AI is an artificial intelligence platform that automates complex data processing and machine learning steps, including feature creation, model analysis, model parameter tuning, optimal variant selection, and result deployment. The main goal of this platform is to achieve maximum prediction accuracy comparable to that of experts, but in a much shorter time frame due to the high level of automation. Another important aspect of unmanned AI is the ability to automatically visualise and interpret machine learning (MLI) results. This is particularly relevant in areas where transparency and comprehensibility of models are important, along with their predictive performance.

What we like about it: It is suitable for companies that prioritise speed of development and improvement of their business model through artificial intelligence.

Price: It's free to develop open source software and use the H20 Wave API.

7. Rasa: Building Conversational Artificial Intelligence

Rasa is a free AI platform for building, improving and deploying contextual virtual assistants across multiple channels and languages, based on an open source and machine learning environment. It is a conversational AI tool that allows you to build chatbots.

It's a great tool for building conversational AI, such as chatbots, and it's designed for the future. This means that you can connect any NLP or ML model to Rasa to get more and more accurate results as the technology improves.

What Rasa is used for

• Understands messages: converts text in any language into structured data. The chatbot platform supports single and multiple intentions, as well as pre-configured and custom objects.
• Conducts conversations: Conduct conversations that preserve context and use a machine learning-based dialogue management system.
• Підключення до каналів обміну повідомленнями: запустіть свого помічника в Slack, Facebook, Google Home, користувацьких каналах тощо.
• Integrate your APIs: interact with other APIs.

What we like: Conversational enterprise AI with built-in integration for social media messaging, such as Slack and Facebook.

Price: Free. There are also paid options for businesses.

8. Amazon Web Services (AWS)

Amazon Web Services (AWS) is a developer-focused platform where you can run your code and store the results and output of your programmes. AWS also provides many additional features for business marketing, making it an integrated tool for developing AI systems.

With AWS, you can choose the operating system, programming language, web application platform, databases, and other services you need.

Who needs Amazon Web Services and why?

AWS also provides additional business marketing capabilities, such as configuring your code to use a content delivery network and managing task coordination for different cloud applications. It is important to note that the cost of these additional features may vary depending on the specific services and usage.

What we like: If you have programming skills but need support to develop and implement business functions to move to Industry 4.0, AWS can be a good choice.

Price: Costs vary. There are short-term offers, 12 months free and always free options.

9. GitHub: streamlining teamwork

Software developers use different platforms to share, store and distribute source code. One of the most popular is GitHub. This platform is so popular that even large companies such as Microsoft and RedHat use its resources and capabilities.

GitHub provides developers with a wide range of tools, including code review, distribution with documentation, and releases. The platform allows developers to collaborate on projects, maintain version control, make changes and fix bugs, making it an important tool for development teams around the world.

Who needs GitHub and why?

The developers of the system call GitHub a "social network" for programmers. This is where they connect repositories, comment on examples of other people's code and use the platform as cloud storage with the ability to quickly transfer it to customers.

What we like: Suitable for developers or teams who don't know each other but want to work together productively on a project.

Price: Basic $0, Team $44, Enterprise $231.

10. Scikit-Learn: a tool for predictive data analysis

Scikit-Learn is one of the most widely used open source Python packages for data science and machine learning. It allows you to perform many operations and provides many algorithms. Scikit-learn also provides excellent documentation on its classes, methods and functions, as well as a description of the algorithms used.

Scikit-Learn supports

• Preliminary data processing;
• Dimensionality reduction;
• Model selection;
• Regressions;
• Classifications;
• Cluster analysis.

Who needs Scikit-learn and why?

You can use scikit-learn to prepare your data for machine learning algorithms: standardise or normalise data, encode categorical variables, and more.

Why we liked it: It offers a variety of supervised and unsupervised learning algorithms suitable for different tasks such as classification, regression, clustering, dimensionality reduction and feature extraction.

Price: Free.