IBM Project CodeNet Employs AI Tools to Program Software

IBM’s AI research unit debuted Project CodeNet, a dataset to develop machine learning models for software programming. The name is a take-off on ImageNet, the influential dataset of photos that pushed the development of computer vision and deep learning. Creating “AI for code” systems has been challenging since software developers are constantly discovering new problems and exploring different solutions. IBM researchers have taken that into consideration in developing a multi-purpose dataset for Project CodeNet. Continue reading IBM Project CodeNet Employs AI Tools to Program Software

USC Researchers Find Bias in Deepfake Detectors’ Datasets

The advent of deepfakes, which replace a person in a video or photo by likeness of someone else, has sparked concern that the ease of using machine learning tools to create them are readily available to criminals and provocateurs. In response, Amazon, Facebook and Microsoft sponsored the Deepfake Detection Challenge, which resulted in several potential tools. But now, researchers at the University of Southern California found that the datasets used to train some of these detection systems demonstrate racial and gender bias. Continue reading USC Researchers Find Bias in Deepfake Detectors’ Datasets

CES: Panel Examines Issues of Gender and Racial Bias in AI

During a CES 2021 panel moderated by The Female Quotient chief executive Shelley Zalis, AI industry executives probed issues related to gender and racial bias in artificial intelligence. Google head of product inclusion Annie Jean-Baptiste, SureStart founder and chief executive Dr. Taniya Mishra and ResMed senior director of health economics and outcomes research Kimberly Sterling described the parameters of such bias. At Google, Jean-Baptiste noted that, “the most important thing we need to remember is that inclusion inputs lead to inclusion outputs.” Continue reading CES: Panel Examines Issues of Gender and Racial Bias in AI

Unsecured Databases Leak 235 Million Social Media Profiles

On August 1, security research firm Comparitech, led by Bob Diachenko, discovered a massive data leak of nearly 235 million Instagram, TikTok and YouTube user profiles. The leak was due to an unsecured database, which is quickly becoming a widespread cause of similar breaches. An audit of the dark web found about 15 billion stolen logins from 100,000 such unsecured database breaches. The data leak discovered by Diachenko and his team was spread across several datasets, including two of 100 million each of Instagram users. Continue reading Unsecured Databases Leak 235 Million Social Media Profiles

Google, Nvidia Train Neural Networks to Post-Process Video

Google researchers have created a machine learning system that adds color to black & white videos, and can also choose which specific objects, people and pets receive the color treatment. The technology is based on what’s called a convolutional neural network, which is architecturally suited for object tracking and video stabilization. Meanwhile, Nvidia has debuted an algorithm that slows down video, without the jitters, after it’s been captured, by using a neural network to create “in between” frames required for smooth motion. Continue reading Google, Nvidia Train Neural Networks to Post-Process Video

Nvidia’s New AI Method Can Reconstruct an Image in Seconds

Nvidia debuted a deep learning method that can edit or reconstruct an image that is missing pixels or has holes via a process called “image inpainting.” The model can handle holes of “any shape, size, location or distance from image borders,” and could be integrated in photo editing software to remove undesirable imagery and replace it with a realistic digital image – instantly and with great accuracy. Previous AI-based approaches focused on rectangular regions in the image’s center and required post processing. Continue reading Nvidia’s New AI Method Can Reconstruct an Image in Seconds