How much digital content do you consume live? Be it a regular TV or Movie show, podcast of YouTube episode, just about everything outside of the live local news, am radio, and big events (and even those, I usually DVR to get some fast-forward time in cue). The triad of creation, distribution, and consumption are no longer bound by time and space. Not for everything, nothing like the shared experience at a live show. But for most of the things we watch, read, and listen too, it's not synchronous, appointment consumption.

Fixing Bias Creep in Artificial Intelligence Systems. How Meta-Learning and a counter intuitive approach to creating an AI Ethical Framework can counter …

"The goal of ExplOR was to familiarize students, specifically those from underserved communities, with operations research and artificial intelligence …

One of the growing problems today is misinformation: the proliferation of fake news and misleading content across social media platforms. While artificial intelligence (AI) helps in its spread, there has been growing proof of how it can be used to curb this problem. However, more than just the daily news article, misinformation has far-reaching - and often fearsome - implications in more critical fields such as cybersecurity, public safety, medicine, and even science. In fact, there have been published collaborative papers, one appearing in the April 2021 issue of PNAS, tackling misinformation as a result of common human biases and prevailing practices in the critique and release of scientific papers. This even includes respected, peer-reviewed journals.

Over the past three years, celebrities have been appearing across social media in improbable scenarios. You may have recently caught a grinning Tom Cruise doing magic tricks with a coin or Nicolas Cage appearing as Lois Lane in Man of Steel. Most of us now recognize these clips as deepfakes--startlingly realistic videos created using artificial intelligence. In 2017, they began circulating on message boards like Reddit as altered videos from anonymous users; the term is a portmanteau of "deep learning"--the process used to train an algorithm to doctor a scene--and "fake." Deepfakes once required working knowledge of AI-enabled technology, but today, anyone can make their own using free software like FakeApp or Faceswap. All it takes is some sample footage and a large data set of photos (one reason celebrities are targeted is the easy availability of high-quality facial images) and the app can convincingly swap out one person's face for another's.

Building on a project she began as an undergraduate, Higgins started the data-gathering process at Concordia's Building Aerodynamics/Wind Tunnel Lab. It can simulate wind gusts on large buildings with a 1 to 100 or smaller-scale model of a block of downtown Montreal, as well as on individual buildings of different shapes -- square, rectangular, U-shaped, T-shaped or L-shaped, and in different configurations. The lab also has a scale model of a section of the Louis-Hippolyte Lafontaine Bridge-Tunnel in east-end Montreal. "This preliminary work involved a lot of wind tunnel experiments with various building configurations," explains Stathopoulos, a professor in the Department of Building, Civil and Environmental Engineering at the Gina Cody School of Engineering and Computer Science. "Stéphanie ran tests for each of them, with wind coming from different directions, as it would in real life, and tried to predict what the amplification of the wind would be at each location. This particular experimentation was interesting because we are trying to see where we can get the highest wind speed. This is the opposite of what we usually do, which is to try to reduce exposure to wind to protect buildings from natural disasters."

Graph Neural Networks (GNNs) have been widely applied to various fields due to their powerful representations of graph-structured data. Despite the success of GNNs, most existing GNNs are designed to learn node representations on the fixed and homogeneous graphs. The limitations especially become problematic when learning representations on a misspecified graph or a heterogeneous graph that consists of various types of nodes and edges. To address this limitations, we propose Graph Transformer Networks (GTNs) that are capable of generating new graph structures, which preclude noisy connections and include useful connections (e.g., meta-paths) for tasks, while learning effective node representations on the new graphs in an end-to-end fashion. We further propose enhanced version of GTNs, Fast Graph Transformer Networks (FastGTNs), that improve scalability of graph transformations. Compared to GTNs, FastGTNs are 230x faster and use 100x less memory while allowing the identical graph transformations as GTNs. In addition, we extend graph transformations to the semantic proximity of nodes allowing non-local operations beyond meta-paths. Extensive experiments on both homogeneous graphs and heterogeneous graphs show that GTNs and FastGTNs with non-local operations achieve the state-of-the-art performance for node classification tasks. The code is available: https://github.com/seongjunyun/Graph_Transformer_Networks

To help you fast track your company's adoption of machine learning (ML), AWS offers educational solutions for developers to get hands-on experience. We like to think of these programs as a fun way for developers to build their skills using ML technologies in real world scenarios. In this post, we walk you through how to prepare for and run an AI music competition using AWS DeepComposer. Through AWS DeepComposer, you can experience Generative AI in action and learn how to harness the latest in ML and AI. We provide an end-to-end kit that contains tools, techniques, processes, and best practices to run the event. Designed specifically to educate developers on generative AI, AWS DeepComposer includes tutorials, sample code, and training data in an immersive platform that can be used to build ML models with music as the medium of instruction. Developers, regardless of their background in ML or music, can get started with applying AI techniques including Generative Adversarial Networks (GANs), Autoregressive Convolutional Neural Networks (AR-CNN) and Transformers to generate new musical notes and accompaniments.

The key to successful deployment is asking the right questions before making any investments. "AI is already prevalent in many business processes and applications used daily, and there are almost limitless other opportunities where it can be utilized," said Annette Taber, senior vice president for industry outreach and relations at CompTIA. "However, AI processes are complex. The key to a successful deployment is asking the right questions and understanding what's involved before making any investments." The guide identifies more than two dozen factors that should be thoroughly considered and addressed by business decision makers and AI practitioners.

I've been writing a few articles in the last months where I've tackled the subject of artificial intelligence (AI) and its incorporation into digital business processes and our daily life. As I was carrying out my search, I came across some resources about the usage of AI to produce art, like painting and music. By letting machines learn from the human artistic work, Artificial Intelligence Virtual Artists like AIVA can compose classical and symphonic music. Today, AIVA's YouTube channel has over 18K subscribers. In her post "Top 10 AI Music Composers in 2021," Lisa Brown has listed more examples of non-human music composers.