JAXenter: When you created Julia, you stated your reasons for developing the language in a blog post from 2012. Looking at it now, has Julia lived up to--or even exceeded--your expectations? Native Julia programs are often 10x-100x faster than similar programs in R, Python, Matlab, etc. Julia team: We certainly believe that Julia has lived up to the expectations of the original blog post. Today, Julia achieves performance that is orders of magnitude better than other dynamic languages for technical computing. Native Julia programs are often 10x-100x faster than similar programs in R, Python, Matlab, etc. Here's a recent machine learning example discussed on Twitter, where Julia is 10x-100x faster than Python.

The field of medicine faces a global shortage of radiologists, combined with increasing workloads and complex diagnoses, leading to delays, missed findings and huge overtime expenses in healthcare. A radiologist will upload a scan and mark a region of interest. Each result is a full volume, so radiologists can scroll through or change the contrast or brightness. Radiologists can even restrict results based on age, gender or pathological findings in the report via text search. How does Julia help make this possible?

We are excited to share several research papers on the Julia and Flux machine learning ecosystem, to be presented at the NIPS Systems for ML Workshop. Since initially proposing the need for a first-class language and ecosystem for machine learning (ML), we have made considerable progress, including the ability to take gradients of arbitrary computations by leveraging Julia's compiler, and compiling the resulting programs to specialized hardware such as Google's Tensor Processing Units. Here we talk about these papers and the projects that have brought these to life, namely: Flux.jl [paper], Zygote.jl Flux.jl is a library that gives a fresh take on machine learning as it exposes powerful tools to the user in a non-intrusive manner while remaining completely hackable, right to its core. "Careful design of the underlying automatic differentiation allows freely mixing mathematical expressions, built-in and custom layers and algorithms with control flow in one model. This makes Flux unusually easy to extend to new problems."

The new technological era is one where task-specific hardware and software are on the rise. This year at Google I/O 2018, Google launched a new generation of Tensor Processing Unit (TPU), already in use to turbocharge a set of products. Now the MountainView search giant has announced enhanced Julia capabilities to the TPU ecosystem. To remain relevant in the new era, Julia Computing has developed a method for running suitable sections of Julia programs to TPUs using an API and the Google XLA compiler. This development has added more options alongside Tensorflow to leverage Google Cloud.

Julia is a free open source, high-level, high-performance, dynamic programming language for numerical computing. It has the development convenience of a dynamic language with the performance of a compiled statically typed language, thanks in part to a JIT-compiler based on LLVM that generates native machine code, and in part to a design that implements type stability through specialization via multiple dispatch, which makes it easy to compile to efficient code. In the blog post announcing the initial release of Julia in 2012, the authors of the language--Jeff Bezanson, Stefan Karpinski, Viral Shah, and Alan Edelman--stated that they spent three years creating Julia because they were greedy. They were tired of the trade-offs among Matlab, Lisp, Python, Ruby, Perl, Mathematica, R, and C, and wanted a single language that would be good for scientific computing, machine learning, data mining, large-scale linear algebra, parallel computing, and distributed computing. In addition to being attractive to research scientists and engineers, Julia is also attractive to data scientists and to financial analysts and quants.

This post is authored by Viral B. Shah, co-creator of the Julia language and co-founder and CEO at Julia Computing, and Avik Sengupta, head of engineering at Julia Computing. The Julia language provides a fresh new approach to numerical computing, where there is no longer a compromise between performance and productivity. A high-level language that makes writing natural mathematical code easy, with runtime speeds approaching raw C, Julia has been used to model economic systems at the Federal Reserve, drive autonomous cars at University of California Berkeley, optimize the power grid, calculate solvency requirements for large insurance firms, model the US mortgage markets and map all the stars in the sky. It would be no surprise then that Julia is a natural fit in many areas of machine learning. And the powers of Julia make it a perfect language to implement these algorithms.