Whats ahead for Machine Programming

A long time idea, and likely to emerge strongly.   Hand coding will eventually be rare.    Podcast on the topic below

Machine Programming: What Lies Ahead?

Podcast and transcript at the link
Intel’s Justin Gottschlich discusses how machine programming is at an inflection point.

Imagine software that creates its own software. That is what machine programming is all about. Like other fields of artificial intelligence, machine programming has been around since the 1950s, but it is now at an inflection point.

Machine programming potentially can redefine many industries, including software development, autonomous vehicles or financial services, according to Justin Gottschlich, head of machine programming research at Intel Labs. This newly formed research group at Intel focuses on the promise of machine programming, which is a fusion of machine learning, formal methods, programming languages, compilers and computer systems.

In a conversation with Knowledge@Wharton during a visit to Penn, Gottschlich discusses why he believes the historical way of programming is flawed, what is driving the growth of machine programming, the impact it can have and other related issues. He was a keynote speaker at the PRECISE Industry Day 2019 organized by the PRECISE Center at Penn Engineering. 


Following is an edited transcript of the conversation.

Knowledge@Wharton: Given the buzz around AI, a lot of people are familiar with machine learning. However, most of us don’t have a clue about what “machine programming” means. Could you explain the difference between the two?

Justin Gottschlich: At the highest level, machine learning can be considered a subset of artificial intelligence. There are many different types of machine learning techniques. One of the most prominent at present is called “deep neural networks.” This has contributed a lot towards the tremendous progress that we’re seeing over the last decade. Machine programming is about automating the development and maintenance of software. You can think of machine learning being a subset of machine programming. But in addition to using machine learning techniques, which are approximate types of solutions, in machine programming we also use other things like formal program synthesis techniques that provide mathematical guarantees to ensure precise software behavior. You can kind of think of these two points as a spectrum. You have approximate solutions at one end and precise solutions at the other end and in between there’s a fusion of several different ways that you can combine these. Every one of these things is part of the bigger landscape of machine programming.

Knowledge@Wharton: So machine programming is when you create software that can create more software?

Gottschlich: Right.

Knowledge@Wharton: How would that happen? Could you give an example?  ....  "