5 Most Popular Google AI Projects

The amount of data produced by humans and machines nowadays, outnumbers humans’ ability to absorb, understand, and execute complex decisions based on that data. Artificial intelligence is the foundation for all computer learning and represents the future of all complex decision-making.

Since we cannot really escape AI, why not familiarize ourselves with AI by trying our hands on some exciting Google AI projects? Yes, you read it right! AI projects by Google. In this post, we’ll go through some of the most relevant Google AI projects you should be aware of. We have a variety of options to consider, ranging from TensorFlow to DeepMind Lab. Let’s get started.

1. TensorFlow

TensorFlow is undoubtedly the most popular AI project from Google. It’s an open-source platform for machine learning applications. TensorFlow makes model construction easier, ML deployment more flexible, and research experimentation more robust. You should be familiar with this platform if you wish to work in the field of machine learning.

TensorFlow provides a large set of tools and frameworks that make constructing machine learning models easier. Furthermore, you may access it from anywhere at any time, thus enhancing its availability.

It offers a number of APIs, including some of the most prominent, to assist you in creating various types of machine learning models. For example, the Keras API, which is appropriate for beginners owing to its simple interface, may be used to construct and train models. If you wish to do ML training on a broader scale, however, you may utilise the Distribution Strategy API.

2. Bullet Physics

Bullet Physics is one of Google’s most specialised AI projects. It’s a software development kit that focuses on body dynamics, collisions, and interactions between rigid and soft bodies. Bullet Physics is written in the C++ programming language.

This library may be used to create games, robotic simulations, and visual effects. pybullet, a Python package that utilises machine learning, physical simulations, and robotics, is also included in the Bullet Physics SDK. Users may utilise pybullet for a variety of different things, such as collision detection, inverse dynamics, and kinematics. Bullet SDK is used by Google for virtual reality, robotics simulations, game creation, and machine learning.

3. Magenta

Artificial intelligence is used in a variety of disciplines, although it is rarely seen in the creative industries. Magenta is a one-of-a-kind AI application. It focuses on utilising deep learning and reinforcement learning to generate art and music. If you’ve ever wondered how artificial intelligence may influence the creative industries, you should take a look at this project.

Magenta focuses on finding solutions and simplifying things for artists and musicians. It is built on TensorFlow and is a Google Brain Team product. They have a discussion group where they may exchange information and comment on the project’s growth and development.

4. DeepMind Lab

Deep reinforcement learning is difficult to study and implement. In this respect, Google’s DeepMind Lab can assist you. It gives you a three-dimensional platform to explore and develop machine learning and artificial intelligence systems. DeepMind Lab’s simple API allows you to experiment with various AI architectures and learn about their capabilities.

If you’re a beginner and have little experience with reinforcement learning algorithms, you should give it a shot. On the other hand, even an expert might benefit from this project when it comes to experimenting with new and innovative AI concepts. It also includes a variety of puzzles to help you with deep reinforcement learning. Google utilises DeepMind Lab to train and research learning agents at DeepMind.

5. Open Images Dataset

Computer vision is one of the most well-known AI sub-field, and it involves utilising AI-based models to analyse images and videos. The Open Images Dataset is a great project to work on if you wish to explore computer vision applications. It’s a database of around 9 million pictures with annotations.

Because of its vastness, detail, object segmentation, localised narratives, object bounding boxes, and other features, the Open Images Dataset is one of the most popular Google AI projects. This database may be used to train an object recognition model.

We’ve just described the most well-known of Google’s AI projects in this article. Gemmlowp, for example, is also a Google AI project that focuses on matrix multiplication. Other projects worth mentioning include: Google Dialog Flow, DeepVariant, MentorNet, and SLING.

We hope that you find these projects informative. Don’t wait and get started today by experimenting with these projects!