Competition

Gen is not the only language trying to address the challenges of programmable inference. In recent years, the PPL space has exploded producing a number of strong alternatives:

Edward

Edward is a Turing-complete probabilistic programming language(PPL) written in Python. Edward was originally championed by the Google Brain team but now has an extensive list of contributors. The original research paper of Edward was published in March 2017 and since then the stack has seen a lot of adoption within the machine learning community. Edward fuses three fields: Bayesian statistics and machine learning, deep learning, and probabilistic programming. The library integrates seamlessly with deep learning frameworks such as Keras and TensorFlow.

Pyro

Pyro is a deep probabilistic programming language(PPL) released by Uber AI Labs. Pyro is built on top of PyTorch and is based on four fundamental principles:

• Universal: Pyro is a universal PPL — it can represent any computable probability distribution. How? By starting from a universal language with iteration and recursion (arbitrary Python code), and then adding random sampling, observation, and inference.

• Scalable: Pyro scales to large data sets with little overhead above hand-written code. How? By building modern black box optimization techniques, which use mini-batches of data, to approximate inference.

• Minimal: Pyro is agile and maintainable. How? Pyro is implemented with a small core of powerful, composable abstractions. Wherever possible, the heavy lifting is delegated to PyTorch and other libraries.

• Flexible: Pyro aims for automation when you want it and control when you need it. How? Pyro uses high-level abstractions to express generative and inference models, while allowing experts to easily customize inference.

Infer.Net

Microsoft recently open sourced Infer.Net a framework that simplifies probabilistic programming for .Net developers. Microsoft Research has been working on Infer.Net since 2004 but it has been only recently, with the emergence of deep learning, that the framework has become really popular. Infer.Net provides some strong differentiators that makes it a strong choice for developers venturing into the Deep PPL space.

Gen is one of the newest but also one of the most interesting additions to the PPL space. The combination of statistics and deep learning is a key element of the future of the artificial intelligence space. Efforts like Gen are attempting to democratize PPLs in the same way TensorFlow did for deep learning.