As artificial intelligence gains ground among governments, … market for disaster relief efforts tied to AI and machine learning — a market that …

A better term than "artificial" would have likely been "machine" intelligence, because we don't call other creatures artificially intelligent; they …

The Food and Drug Administration recently sought comments on the role of transparency for artificial intelligence AI and machine learning-enabled …

Abstract--Synthesizing human motion with a global structure, such as a choreography, is a challenging task. Existing methods tend to concentrate on local smooth pose transitions and neglect the global context or the theme of the motion. In this work, we present a music-driven motion synthesis framework that generates long-term sequences of human motions which are synchronized with the input beats, and jointly form a global structure that respects a specific dance genre. In addition, our framework enables generation of diverse motions that are controlled by the content of the music, and not only by the beat. Our music-driven dance synthesis framework is a hierarchical system that consists of three levels: pose, motif, and choreography. The pose level consists of an LSTM component that generates temporally coherent sequences of poses. The motif level guides sets of consecutive poses to form a movement that belongs to a specific distribution using a novel motion perceptual-loss. And the choreography level selects the order of the performed movements and drives the system to follow the global structure of a dance genre. Our results demonstrate the effectiveness of our music-driven framework to generate natural and consistent movements on various dance types, having control over the content of the synthesized motions, and respecting the overall structure of the dance. Computationally human body animation built movement transition synthesizing a dance is challenging not only because graphs that are synchronized to the beat [5], [6], [7], or motions must be continuous, smooth and expressive the emotion [8], while more recent works use either hidden locally, but also because a dance has a meaningful global Markov models [9], or recurrent neural networks [10], [11], temporal structure [2], [3]. These methods generate motions that follow the given learning using neural networks have shown promising results audio beat, while following a specific style, but show limited in controlling articulated characters and creating arbitrary variability and lack global consistency that is dictated realistic human motions, including dance.

With the growing popularity and ease of access to the internet, the problem of online rumors is escalating. People are relying on social media to gain information readily but fall prey to false information. There is a lack of credibility assessment techniques for online posts to identify rumors as soon as they arrive. Existing studies have formulated several mechanisms to combat online rumors by developing machine learning and deep learning algorithms. The literature so far provides supervised frameworks for rumor classification that rely on huge training datasets. However, in the online scenario where supervised learning is exigent, dynamic rumor identification becomes difficult. Early detection of online rumors is a challenging task, and studies relating to them are relatively few. It is the need of the hour to identify rumors as soon as they appear online. This work proposes a novel framework for unsupervised rumor detection that relies on an online post's content and social features using state-of-the-art clustering techniques. The proposed architecture outperforms several existing baselines and performs better than several supervised techniques. The proposed method, being lightweight, simple, and robust, offers the suitability of being adopted as a tool for online rumor identification.

Gil discussed the future role that artificial intelligence systems may have as authors of scientific papers to produce their own experiments and shape …

For centuries, master tasters have helped design the flavors of our favorite foods, wines, coffees and teas.

MONTRÉAL, Nov. 22, 2021 /CNW Telbec/ - The digital transformation, which has only gained speed during the pandemic, is unfolding in all sectors of our society and economy. Personalized financial product and service offerings, made possible in part by artificial intelligence (AI) systems, benefit both consumers and financial institutions, but, for the latter, they also come with ethical, legal and reputational risks. The Autorité des marchés financiers (the "AMF") believes these are important issues requiring its consideration. The AMF has therefore decided to initiate a serious dialogue about the responsible use of AI in the financial industry. To assist it in meeting this challenge, the AMF sought input from Marc-Antoine Dilhac, Associate Professor of Ethics and Political Philosophy at the Université de Montréal, and a key contributor to the work that led to the launch, in 2018, of the Montreal Declaration for a Responsible Development of Artificial Intelligence (the "Montreal Declaration").

This week, on the new-media platform Feral File, artist Refik Anadol presents Unsupervised, an exhibition of works created by training an artificial intelligence model with the public metadata of The Museum of Modern Art's collection. Spanning more than 200 years of art, from paintings to photography to cars to video games, the Museum's collection represents a unique data set for an artist who has worked with many different public archives. The AI-based abstract images and shapes in Unsupervised are interpretations of the Museum's wide-ranging collection, weighted toward the exhibition of new artworks at MoMA this fall. Starting with the exhibition opening on November 18, new artworks will be revealed and released over three days. Each work will be made available to collectors as nonfungible tokens, or NFTs. MoMA curators Paola Antonelli and Michelle Kuo sat down with Anadol and Casey Reas, the artist-founder of Feral File, to talk about the ecology of mobile images, art in the age of mechanical learning, and the question: What if a machine tried to create "modern art"? This conversation has been edited for length and clarity. Paola Antonelli: Refik, how did you start thinking of your Machine Hallucinations series, of which Unsupervised is a part? Refik Anadol: Five years ago, I was very fortunate to be one of the artists in residence at the Google Artists and Machine Intelligence program. This was the moment of DeepDream's development, the very first time we were witnessing AI algorithms making an impact on the art and technology communities.

Spotify has removed the shuffle button from all album pages after Adele pressed the company for the change in time for the launch of her album 30. According to her own tweet, albums should be listened to "as [artists] intended" as they tell "a story." If you were ever in doubt of the influence of major music artists like Adele or Taylor Swift, here's your answer. Adele's debut single from 30, "Easy On Me," broke a single-day Spotify streaming record previously held by K-pop megagroup BTS. Halo Infinite's opening had Engadget Senior Editor Devindra Hardawar worried, especially after its year-long delay.