One lunchtime about three years ago, Hazel Savage and Aron Pettersson set a new piece of software running on a laptop then went to a nearby mall for a sandwich. They hoped, on their return, to have the answer to a question that would change the music industry: can a computer pick a hit record? The pair had just founded their firm, Musiio, in Singapore's Boat Quay district. Pettersson, who is Swedish, was a specialist in artificial intelligence (AI) with a background in neuroscience; Savage, a British music industry professional with tech pedigree, had worked for Shazam and the Pandora streaming service. These are written by little-known artists and commonly used for soundtracks and podcasts.

Interestingly, 32% of them have implemented virtual assistants or conversational interfaces for customer service, and 25% use machine learning (ML) …

… a leading travel technology company that combines proprietary artificial intelligence and machine learning capabilities, and a 24/7 global gig-based …

AI and live coding has been little explored. This article contributes with a short review of different perspectives of using virtual agents in the practice of live coding looking at past and present as well as pointing to future directions.

If you use such social media websites as Facebook and Twitter, you may have come across posts flagged with warnings about misinformation. So far, most misinformation – flagged and unflagged – has been aimed at the general public. Imagine the possibility of misinformation – information that is false or misleading – in scientific and technical fields like cybersecurity, public safety and medicine. There is growing concern about misinformation spreading in these critical fields as a result of common biases and practices in publishing scientific literature, even in peer-reviewed research papers. As a graduate student and as faculty members doing research in cybersecurity, we studied a new avenue of misinformation in the scientific community.

New Jersey, United States,- The Artificial Intelligence in Manufacturing Market report is a research study of the market along with an analysis of its key …

We also worked with trained third-party annotators to manually transform a smaller subset of the images to ensure we have even more selections representative of the way a human user would transform images. The annotators used image manipulation software GIMP to manually alter images in diverse ways that we cannot easily automate, for example handwriting or drawing on the images or cropping to leave only the part of the image most salient to the human eye. The Image Similarity Challenge invites participants to test their image matching techniques on the Image Similarity data set. More information for researchers is available here, and the accompanying paper is available here. For researchers considering attending NeurIPS 2021 in December, we're also pleased to announce that the Image Similarity Challenge has been accepted for the NeurIPS 2021 competition track, where we will be announcing the winners of this challenge (The competition is subject to official rules.

… approved for dermatologists to use in the US, says Roxana Daneshjou, a Stanford dermatologist and researcher in machine learning and health.

Artificial intelligence is a data hog; effectively building and deploying AI and machine learning systems require large data sets.

As medical AI moves into the mainstream, machine learning experts are learning that it's easier to deal with petabytes than people.