Answers were provided by the OpenAI ChatGPT.

Q: Where is the Jax programming language used?

Jax is a programming language and library for machine learning (ML) research and development. It is developed and maintained by Google, and is designed to be used for writing and running ML algorithms and models that can be executed on a wide range of hardware platforms, including CPUs, GPUs, and TPUs.

Jax is written in the Python programming language, and it uses the XLA (Accelerated Linear Algebra) compiler to automatically compile and optimize ML algorithms and models for execution on different hardware platforms. It provides a high-level, functional API that allows users to write ML algorithms and models in a simple and expressive manner, while still leveraging the performance and capabilities of modern hardware accelerators.

Jax is used by ML researchers and developers at Google and other organizations for a wide range of applications, including computer vision, natural language processing, robotics, and scientific computing. It is an open-source project, and its source code is available on GitHub for anyone to use and contribute to.

In summary, Jax is a programming language and library for ML research and development, developed and maintained by Google. It is used for writing and running ML algorithms and models that can be executed on a wide range of hardware platforms, and is widely used by ML researchers and developers for a variety of applications.

Q: Why did Google start using Jax when it already had Tensorflow and Keras?

Google developed and started using Jax for several reasons, despite already having established ML libraries such as TensorFlow and Keras. First, Jax is specifically designed to be used for writing and running ML algorithms and models that can be executed on a wide range of hardware platforms, including CPUs, GPUs, and TPUs. This makes it well-suited for running ML algorithms and models on the diverse hardware infrastructure that is used by Google and other organizations for large-scale ML research and development.

Second, Jax uses the XLA (Accelerated Linear Algebra) compiler to automatically compile and optimize ML algorithms and models for execution on different hardware platforms. This allows developers to write high-level, expressive code that is easy to understand and maintain, while still leveraging the performance and capabilities of modern hardware accelerators.

Third, Jax provides a functional API that allows developers to write ML algorithms and models using functional programming constructs, such as higher-order functions, closures, and lazy evaluation. This makes it easier to write modular, composable, and reusable ML algorithms and models, and to avoid some of the common pitfalls of object-oriented programming, such as tight coupling and mutable state.

In summary, Google developed and started using Jax because it is specifically designed for writing and running ML algorithms and models on a wide range of hardware platforms, it uses the XLA compiler to optimize code for hardware accelerators, and it provides a functional API for writing modular and composable ML algorithms and models. These features make Jax a useful and powerful tool for ML research and development, in addition to the existing libraries such as TensorFlow and Keras.