Our work with differentiable programming, which enables programs to optimize themselves, is part of Facebook AI's broader efforts to build more advanced tools for machine learning (ML) programming. That's why we're extending the Kotlin compiler to make differentiability a first-class feature of the Kotlin language, as well as developing a system for tensor typing. Our work enables developers to explore Software 2.0, where software essentially writes itself, via: By enabling intuitive and performant differentiable programming in Kotlin, we're empowering developers to create powerful, flexible programs that take advantage of problem structure while seamlessly maintaining type safety and keeping debugging simple. Today, most code is either learnable (written using restrictive machine learning libraries) or explicitly programmed (using traditional coding paradigms). A major obstacle toward achieving Software 2.0 is that there's no true compatibility between these two methods.

"Neural networks represent the beginning of a fundamental shift in how we write software. The current coding paradigms nudge developers to write code using restrictive machine learning libraries that can learn, or explicitly programmed to do a specific job. But, we are witnessing a tectonic shift towards automation even in the coding department. So far, code was used to automate jobs now there is a requirement for code that can write itself adapting to various jobs. This is software 2.0 where software writes on its own and thanks to machine learning; this is now a reality. Differentiable programming especially, believes the AI team at Facebook, is key to building tools that can help build ML tools. To enable this, the team has picked Kotlin language. Kotlin was developed by JetBrains and is popular with the Android developers. Its rise in popularity is a close second to Swift. Kotlin has many similarities with Python syntax, and it was designed as a substitute for Java.

Our work with differentiable programming, which enables programs to optimize themselves, is part of Facebook AI's broader efforts to build more advanced tools for machine learning (ML) programming. That's why we're extending the Kotlin compiler to make differentiability a first-class feature of the Kotlin language, as well as developing a system for tensor typing. Our work enables developers to explore Software 2.0, where software essentially writes itself, via: By enabling intuitive and performant differentiable programming in Kotlin, we're empowering developers to create powerful, flexible programs that take advantage of problem structure while seamlessly maintaining type safety and keeping debugging simple. Today, most code is either learnable (written using restrictive machine learning libraries) or explicitly programmed (using traditional coding paradigms). A major obstacle toward achieving Software 2.0 is that there's no true compatibility between these two methods.