Machine learning speeds up vehicle routing … Machine-learning system flags remedies that might do more harm than good.

The group will define the requirements of Machine Learning as they relate to technology, data formats and network architectures.

The company intends to use the funds to expand the team of machine learning engineers and CG artists in order to expedite the development of …

When a video purporting to show New Zealand Prime Minister Jacinda Ardern smoking drugs surfaced on social media in recent months, experts quickly dismissed it as a fake. The video, which was viewed and shared thousands of times, showed a woman smoking from what appeared to be a crack pipe. The PM's face had been superimposed using artificial intelligence. But the video, created for YouTube, was convincing enough to the many who shared it. It was the latest example of how disturbingly authentic-looking videos can blur the lines between reality and fantasy.

AI generated "in a nutshell" movies and pictures as NFT's: - Countries, Cities, Decades, Identities, Sports, Music, Movies, Movements.... - ie: Japan, United States, 80s, History of Olympics, Chronology of Rock Music, Dissection of Fintech, Theory of Evolution, Story of Art, Religion over Centuries, Renaissance in Europe ... Deep Learning generated fusion of art and knowledge distilled into a short movie.

Population-level societal events, such as civil unrest and crime, often have a significant impact on our daily life. Forecasting such events is of great importance for decision-making and resource allocation. Event prediction has traditionally been challenging due to the lack of knowledge regarding the true causes and underlying mechanisms of event occurrence. In recent years, research on event forecasting has made significant progress due to two main reasons: (1) the development of machine learning and deep learning algorithms and (2) the accessibility of public data such as social media, news sources, blogs, economic indicators, and other meta-data sources. The explosive growth of data and the remarkable advancement in software/hardware technologies have led to applications of deep learning techniques in societal event studies. This paper is dedicated to providing a systematic and comprehensive overview of deep learning technologies for societal event predictions. We focus on two domains of societal events: \textit{civil unrest} and \textit{crime}. We first introduce how event forecasting problems are formulated as a machine learning prediction task. Then, we summarize data resources, traditional methods, and recent development of deep learning models for these problems. Finally, we discuss the challenges in societal event forecasting and put forward some promising directions for future research.

A major challenge in collaborative filtering methods is how to produce recommendations for cold items (items with no ratings), or integrate cold item into an existing catalog. Over the years, a variety of hybrid recommendation models have been proposed to address this problem by utilizing items' metadata and content along with their ratings or usage patterns. In this work, we wish to revisit the cold start problem in order to draw attention to an overlooked challenge: the ability to integrate and balance between (regular) warm items and completely cold items. In this case, two different challenges arise: (1) preserving high quality performance on warm items, while (2) learning to promote cold items to relevant users. First, we show that these two objectives are in fact conflicting, and the balance between them depends on the business needs and the application at hand. Next, we propose a novel hybrid recommendation algorithm that bridges these two conflicting objectives and enables a harmonized balance between preserving high accuracy for warm items while effectively promoting completely cold items. We demonstrate the effectiveness of the proposed algorithm on movies, apps, and articles recommendations, and provide an empirical analysis of the cold-warm trade-off.

Medical innovation and sportswear makers can also do well.

Chinese artificial-intelligence company SenseTime Group Inc. is trying to keep its initial public offering alive, according to a person familiar …

A math professor from American University, along with his team of collaborators, developed a statistical model that can detect misinformation in social media posts. Machine learning is increasingly being used to stop the spread of misinformation, but there is still a major hurdle involving the problem of black boxes that occur. This refers to when […]