Media

Media

Intel Blog: The Three Pillars of Machine Programming Provide Core Concepts for Research Advances

In the future, Intel Labs scientists believe that computers will become programmers, inventing algorithms and data structures through an emerging technology known as machine programming. Based on a research framework known as the three pillars of machine programming — jointly envisioned by Intel Labs and Massachusetts Institute of Technology (MIT) scientists — this novel field is making notable research advances in the automation of software and hardware development.

ZDNet: Software developers: How plans to automate coding could mean big changes ahead

For the vast majority of humans, writing code is akin to learning a new language – but researchers from Intel and MIT are on a mission to change that. And the solution they are coming up with is to build code… that can code.

The Singularity: This AI Could Bring Us Computers That Can Write Their Own Software

When OpenAI first published a paper on their new language generation AI, GPT-3, the hype was slow to build. The paper indicated GPT-3, the biggest natural language AI model yet, was advanced, but it only had a few written examples of its output. Then OpenAI gave select access to a beta version of GPT-3 to see what developers would do with it, and minds were blown.

The Register: Intel, boffins invent an AI Clippy for code: Hi, I see you're writing another lock-free bloom filter. Can I help?

Intel engineers, and academics from MIT and Georgia Tech, have built a neural network that predicts whether two snippets of code intend to achieve the same aim even if they're written differently.

Intel Newsroom: Intel, MIT and Georgia Tech Deliver Improved Machine-Programming Code Similarity System

Intel Corp., MIT and the Georgia Institute of Technology today detailed an artificial intelligence code analysis system that is being hailed as a “solid step” toward teaching computers how to program.

Intel is also looking to turn the system into a coding assistance tool for developers.

VentureBeat: Intel researchers create AI system that rates similarity of 2 pieces of code

In partnership with researchers at MIT and the Georgia Institute of Technology, Intel scientists say they’ve developed an automated engine — Machine Inferred Code Similarity (MISIM) — that can determine when two pieces of code perform similar tasks, even when they use different structures and algorithms. MISIM ostensibly outperforms current state-of-the-art systems by up to 40 times, showing promise for applications from code recommendation to automated bug fixing.

SiliconANGLE: Intel, MIT and Georgia Tech move ‘solid step’ closer to teaching AI how to code

Intel Corp., MIT and the Georgia Institute of Technology today detailed an artificial intelligence code analysis system that is being hailed as a “solid step” toward teaching computers how to program.

Intel is also looking to turn the system into a coding assistance tool for developers.

MIT Technology Review: A neural network that spots similarities between programs could help computers code themselves

Computer programming has never been easy. The first coders wrote programs out by hand, scrawling symbols onto graph paper before converting them into large stacks of punched cards that could be processed by the computer. One mark out of place and the whole thing might have to be redone.

Communications with the ACM : Your Wish Is My CMD

As artificial intelligence (AI) techniques advance, they are beginning to automate tasks that, until recently, only humans could perform—tasks such as translating text from one language to another or making medical diagnoses. It seems only logical to turn that computer power on computers themselves and use AI to automate programming...

SD Times: Computer scientists tackle performance regressions with new tool

Researchers from Texas A&M University have teamed up with computer scientists from Intel Labs to create a tool that will help identify the source of software bugs. According to the researchers, software updates are supposed to make applications run faster, but sometimes they end up doing the opposite. Bugs known as performance regressions pop up in software updates and are time-consuming to fix...

Venturebeat: Intel previews AI advances in software testing, sequence models, and explainability

This week marks the kickoff of Neural Information Processing Systems (NeurIPS), one of the largest AI and machine learning conferences globally. NeurIPS 2017 and NeuIPS 2018 received 3,240 and 4,854 research paper submissions, respectively, and this year’s event — which takes place from December 8 to December 14 in Vancouver — is on track to handily break those records. (Submissions this year overwhelmed NeurIPS’ website, which crashed minutes before the official deadline.)

Researchers from Intel will be in attendance, as will those from tech giants like Google, Facebook, Apple, Uber, Alibaba, Baidu, and countless others...

Intel Labs Gets Machine Programming Research For Automating Software Development, Reduce Coding Errors And Address Skill Shortage

Intel has set up an interesting program at its Intel Labs, the company’s hub for experimental projects with long-term prospects. The Machine Programming Research (MPR) project will attempt to automate software development for complex platforms while reducing coding errors. The primary reasoning behind the project appears to be the increasing shortage of trained or expert programmers who can write complex software code reliably and consistently.

Eye On A.I. : Podcast Episode 32

Eye on A.I. tracks new developments in artificial intelligence research, hosted by longtime New York Times journalist Craig S. Smith. In each episode, Craig will discuss aspects of AI with some of the people making a difference in the space, putting incremental advances into a broader context. AI is about to change your world, so pay attention.

The Economic Times: Machine Learning

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 are seeing over the last decade.

Machine programming is about automating the development and maintenance of software.

Intel Press Release: Why More Software Development Needs to Go to the Machines

Justin Gottschlich leads the Machine Programming Research (MPR) team in the Systems and Software Research Lab. Justin’s newly-formed research group focuses on the pioneering promise of machine programming, which is a fusion of machine learning, formal methods, programming languages, compilers and computer systems.

Next Platform TV interview – Current & Future Directions for Machine and Neural Programming (commentary begins @ 27:15 minute mark) [October 16, 2020 episode]

Next Platform TV interview – Current & Future Directions for Machine and Neural Programming (commentary begins @ 27:15 minute mark) [October 16, 2020 episode] [YouTube]

Invited talk @ UWisc’s Machine Learning Optimized Systems Fall ’20 Seminar: Machine Programming – Challenges and Opportunities [YouTube]

Invited talk @ UWisc’s Machine Learning Optimized Systems Fall ’20 Seminar: Machine Programming – Challenges and Opportunities [YouTube]

Knowledge@Wharton: Machine Programming – What Lies Ahead? [Podcast & Transcript] [YouTube]

Knowledge@Wharton: Machine Programming – What Lies Ahead? [Podcast & Transcript]
Keynote Address @ Program Synthesis For Scientific Computing (Department of Energy): Machine Programming – Challenges & Opportunities

Keynote Address @ Program Synthesis For Scientific Computing (Department of Energy): Machine Programming – Challenges & Opportunities [YouTube]

Keynote Address @ PRECISE Industry Day 2019 (Penn Engineering): Machine Programming – The Future of Autonomy [Event Page | Program]

Keynote Address @ PRECISE Industry Day 2019 (Penn Engineering): Machine Programming – The Future of Autonomy [Event Page] [Program] [YouTube]

Invited Talk @ GRASP-PRECISE Industry Day 2018 (Penn Engineering): Deep Learning for Autonomous Driving [Event Page] [Program] [YouTube]

Invited Talk @ GRASP-PRECISE Industry Day 2018 (Penn Engineering): Deep Learning for Autonomous Driving [Event Page] [Program]
InsideHPC Report: Using Machine Learning To Avoid The Unwanted [Powerpoint]

InsideHPC Report: Using Machine Learning To Avoid The Unwanted [Powerpoint] [YouTube]