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Try to get your own cutie portrait using MMGEN-FaceStylor

#artificialintelligence

We attach the github link below at the beginning of the article. When you are watching cartoon movies or comic books, have you ever wondered about your figure in the anime world? Now, getting your own cartoon image can be realized through deep learning technology. OpenMMLab has opened source a face stylization project: MMGEN-FaceStylor,which can not only change the characters into images of various styles, but also controls the stylization intensity. If you are not satisfied with the style provided and want to use your own data for training, we also provide a complete model training code.


Most Shocking Deepfake Videos Of 2021

#artificialintelligence

Only, it was a deepfake. So was the video of Donald Trump taunting Belgium for remaining in the Paris climate agreement and Barack Obama's public service announcement as posted by Buzzfeed. These great examples of deepfakes are the 21st Century's answer to Photoshopped images and videos. Synthetic media, deepfakes, use artificial intelligence (AI) -- deep learning technology, to replace an existing person in an image or video with someone else. One reason for the widespread use of deepfake technology in popular celebrities is that these personalities have a large number of pictures available on the internet, allowing AI to train and learn from.


Music Genre Classification using Deep Learning (Audio and Video)

#artificialintelligence

If someday, we all go to prison for downloading music, I hope they separate us by genres. Automatic music classification is an area of research that has been receiving a great deal of attention lately. With the breadth of artists and songs being released in current times, it has become increasingly difficult to manually classify music genres. There are very rarely precise, clear, and consistent heuristics delineating the musical qualities and characteristics of each genre. The task of defining and implementing measures of musical similarity can be extremely challenging, especially for a human.


Artificial Intelligence Projects with Python

#artificialintelligence

In this course, we aim to specialize in artificial intelligence by working on 14 Machine Learning Projects and Deep Learning Projects at various levels (easy - medium - hard). Before starting the course, you should have basic Python knowledge. Our aim in this course is to turn real-life problems that seem difficult to do into projects and then solve them using latest versions of artificial intelligence algorithms (machine learning algortihms and deep learning algorithms) and Python(3.8). This course was prepared in August 2021. We will carry out some of our projects using machine learning and some using deep learning algorithms.


Box introduces new anti-ransomware capabilities and other new features at BoxWorks 2021 …

#artificialintelligence

"Deep learning technology complements traditional hash-based or … The machine learning capabilities coming to Box Shield are also being put to use …


On the Opportunities and Risks of Foundation Models

arXiv.org Artificial Intelligence

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.


The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI

arXiv.org Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.


Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence

arXiv.org Artificial Intelligence

Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices. MAR systems enable users to interact with MAR devices, such as smartphones and head-worn wearables, and performs seamless transitions from the physical world to a mixed world with digital entities. These MAR systems support user experiences by using MAR devices to provide universal accessibility to digital contents. Over the past 20 years, a number of MAR systems have been developed, however, the studies and design of MAR frameworks have not yet been systematically reviewed from the perspective of user-centric design. This article presents the first effort of surveying existing MAR frameworks (count: 37) and further discusses the latest studies on MAR through a top-down approach: 1) MAR applications; 2) MAR visualisation techniques adaptive to user mobility and contexts; 3) systematic evaluation of MAR frameworks including supported platforms and corresponding features such as tracking, feature extraction plus sensing capabilities; and 4) underlying machine learning approaches supporting intelligent operations within MAR systems. Finally, we summarise the development of emerging research fields, current state-of-the-art, and discuss the important open challenges and possible theoretical and technical directions. This survey aims to benefit both researchers and MAR system developers alike.


Neurocle, a Developer of Deep Learning Software

#artificialintelligence

Neurocle aims to enable anyone to use artificial intelligence or AI technology. We focus on the field of deep learning vision, in particular.