Purely AI News: For AI professionals in a hurry
July 31, 2020
Intel joins hands with researchers from MIT and Georgia Tech to work on a code improvement recommendation system, develops "An End-to-End Neural Code Similarity System"
Imagine an AI that assists software developers to write better software and eventually write some on its own. That is what the nascent field of "Machine Programming" is all about. Like many other sub-fields of artificial intelligence, Machine Programming has been around for a long time, but it is now reaching an inflection point. A newly formed research group at Intel has been tasked with further pushing the limits of such systems which are said to be a fusion of machine learning, formal methods, programming languages, compilers, and computer systems. Such a system could reduce the number of bugs and increase efficiency as blocks of code get replaced by those evaluated to be known good ones. Programmers could also be automatically guided to use library functions instead of reinventing the wheel again.

As the first step towards a fully functional Machine Programming system, the group has released a new paper detailing a new code similarity algorithm based on graph neural networks.
The proposed system, called "Machine Inferred Code Similarity (MISIM)" is able to figure out whether or not two given pieces of code were designed to do the same thing regardless of their implementation.
This is achieved using a novel structural representation of code designed by the group specifically to lift semantic meaning from code syntax. This structural representation is then vectorized and fed into a Graph Neural Network which takes two such pieces of code as input and learns to produce a similarity score for them. While the MISIM system has been tested on code written in C/C++ only, the researchers claim that it has been designed to support a multitude of scenarios including partial/non-compilable code or code written in high-level programming languages like Python and JavaScript.

The paper also compares MISIM to three other state-of-the-art code similarity systems, code2vec, NeuralCode Comprehension, and Aroma. They conclude that experimental evaluation across 45,780 programs shows that MISIM outperforms these systems, often by a large factor (upwards of 40.6×). One shortcoming of the proposed system is said to be that it needs reconfiguration of its various components for a new code corpus. Addressing this, the researchers say "To realize this, we may first need to design a new semantics analysis system that can automatically characterize a given code corpus in some meaningful way." Adding, "Such characterizations may then be useful to guide the learning process and help identify optimal MISIM components."

Aug. 2, 2020

Sample Factory, a new training framework for Reinforcement Learning slashes the level of compute required for state-of-the-art results

July 25, 2020

Google's tensorflow-lite framework for deep learning is now more than 2x faster on average, using operator fusion and optimizations for additional CPU instruction sets

July 23, 2020

Fawkes: An AI system that puts an 'invisibility cloak' on images so that facial recognition algorithms are not able to reveal identities of people without permission

July 22, 2020

Researchers from Austria propose an AI system that reads sheet music from raw images and aligns that to a given audio accurately

July 21, 2020

WordCraft: A Reinforcement Learning environment for enabling common-sense based agents

July 20, 2020

A designer who worked on over 20 commercial projects for a year turns out to be an AI built by the Russian design firm Art. Lebedev Studio

July 19, 2020

Microsoft is developing AI to improve camera-in-display technology for natural perspectives and clearer visuals in video calls

July 18, 2020

Microsoft and Zhajiang Univ. researchers create AI Model that can sing in several languages including both Chinese and English

July 18, 2020

New event-based learning algorithm 'E-Prop' inspired by the Human brain is more efficient than conventional Deep Learning

July 17, 2020

Scientists from the University of California address the false-negative problem of MRI Reconstruction Networks using adversarial techniques

July 16, 2020

A new technique of exposing DeepFakes uses the classical signal processing technique of frequency analysis

July 16, 2020

New AI model By Facebook researchers can recognize five different voices speaking simultaneously, pushes state-of-the-art forward

July 15, 2020

Researchers from Columbia Univ. and DeepMind propose a new framework for Taylor Expansion Policy Optimization (TayPO)

July 14, 2020

Federated Learning is finally here; Presagen's new algorithm creates higher performing AI than traditional centralized learning

July 14, 2020

Fujitsu designed a new Deep Learning based method for dimensionality reduction inspired by compression technology

July 12, 2020

Databricks donates its immensely popular MLflow framework to the Linux Foundation

July 12, 2020

Microsoft Research restores old photos that suffer from severe degradation with a new deep learning based approach

July 12, 2020

Amazon launches a new AI based automatic code review service named CodeGuru

July 12, 2020

IBM launches new Deep Learning project: Verifiably Safe Reinforcement Learning (VSRL) framework

July 12, 2020

DevOps for ML get an upgrade with new open-source CI/CD library, "Continuous Machine Learning (CML)"

July 12, 2020

DeepMind's new open-sourced Reinforcement-Learning library, dm_control, packs a simple interface to common RL utilities

July 12, 2020

Learning to learn: Google's AutoML-Zero learns to evolve new ML algorithms from scratch