Docebo uses artificial intelligence to improve corporate learning. PagerDuty helps companies prevent downtime in digital systems.

In todays comfortable lifestyle, where all the work is done by robots and artificial intelligence, we do not do any kind of physical exercise.

She was speaking after SingularityNET unveiled Grace, a new robot nursing assistant at the 14th Conference on Artificial General Intelligence …

The Artificial Intelligence for Healthcare Applications market report provides with a comprehensive analysis of this business space and comprises …

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Social media users took to the internet following President Biden's recent town hall, drawing comparisons between his behavior and that of the cartoon character Beavis from "Beavis and Butt-head" At one point during the town hall, Biden was shown holding his arms bent out in front of him with his fists clinched. That moment was clipped and shared to social media by several people, including political commentator Mike Cernovich, who questioned, "What is Biden doing?" "Biden is straight comedy," wrote former NBA player Andrew Bogut. Other users simply shared photos of the president in the moment alongside pictures of Beavis, who is known for a hyperactive alter-ego, The Great Cornholio, that exhibits the same behavior.

Artificial intelligence and machine learning are playing increasing roles in drug discovery, potentially saving significant time and money.

Is music generation even something that a [deep] neural network can do? The goal of this project is to artificially generate piano music. Often neural networks are used to solve either a classification or a regression problem. Music on the other hand is a sequence of notes, each note played for a given duration. The notes can be interpreted as classes and their duration are numerical values.

Although conceptualization has been widely studied in semantics and knowledge representation, it is still challenging to find the most accurate concept phrases to characterize the main idea of a text snippet on the fast-growing social media. This is partly attributed to the fact that most knowledge bases contain general terms of the world, such as trees and cars, which do not have the defining power or are not interesting enough to social media app users. Another reason is that the intricacy of natural language allows the use of tense, negation and grammar to change the logic or emphasis of language, thus conveying completely different meanings. In this paper, we present TAG, a high-quality concept matching dataset consisting of 10,000 labeled pairs of fine-grained concepts and web-styled natural language sentences, mined from the open-domain social media. The concepts we consider represent the trending interests of online users. Associated with TAG is a concept graph of these fine-grained concepts and entities to provide the structural context information. We evaluate a wide range of popular neural text matching models as well as pre-trained language models on TAG, and point out their insufficiency to tag social media content with the most appropriate concept. We further propose a novel graph-graph matching method that demonstrates superior abstraction and generalization performance by better utilizing both the structural context in the concept graph and logic interactions between semantic units in the sentence via syntactic dependency parsing. We open-source both the TAG dataset and the proposed methods to facilitate further research.

This paper presents a novel keypoints-based attention mechanism for visual recognition in still images. Deep Convolutional Neural Networks (CNNs) for recognizing images with distinctive classes have shown great success, but their performance in discriminating fine-grained changes is not at the same level. We address this by proposing an end-to-end CNN model, which learns meaningful features linking fine-grained changes using our novel attention mechanism. It captures the spatial structures in images by identifying semantic regions (SRs) and their spatial distributions, and is proved to be the key to modelling subtle changes in images. We automatically identify these SRs by grouping the detected keypoints in a given image. The ``usefulness'' of these SRs for image recognition is measured using our innovative attentional mechanism focusing on parts of the image that are most relevant to a given task. This framework applies to traditional and fine-grained image recognition tasks and does not require manually annotated regions (e.g. bounding-box of body parts, objects, etc.) for learning and prediction. Moreover, the proposed keypoints-driven attention mechanism can be easily integrated into the existing CNN models. The framework is evaluated on six diverse benchmark datasets. The model outperforms the state-of-the-art approaches by a considerable margin using Distracted Driver V1 (Acc: 3.39%), Distracted Driver V2 (Acc: 6.58%), Stanford-40 Actions (mAP: 2.15%), People Playing Musical Instruments (mAP: 16.05%), Food-101 (Acc: 6.30%) and Caltech-256 (Acc: 2.59%) datasets.

Right now, there is a whole body of researchers debating the extent to which artificial general intelligence could mimic the human brain. Digital life continues to augment human capacities and disrupt eons-old human activities. Are we witnessing the new world order already? There are examples of AI everywhere we look. However, Artificial General Intelligence is still in its primary stages.