Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence Artificial Intelligence

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.

Try to get your own cutie portrait using MMGEN-FaceStylor


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.

Music Genre Classification using Deep Learning (Audio and Video)


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


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.

When Creators Meet the Metaverse: A Survey on Computational Arts Artificial Intelligence

The metaverse, enormous virtual-physical cyberspace, has brought unprecedented opportunities for artists to blend every corner of our physical surroundings with digital creativity. This article conducts a comprehensive survey on computational arts, in which seven critical topics are relevant to the metaverse, describing novel artworks in blended virtual-physical realities. The topics first cover the building elements for the metaverse, e.g., virtual scenes and characters, auditory, textual elements. Next, several remarkable types of novel creations in the expanded horizons of metaverse cyberspace have been reflected, such as immersive arts, robotic arts, and other user-centric approaches fuelling contemporary creative outputs. Finally, we propose several research agendas: democratising computational arts, digital privacy, and safety for metaverse artists, ownership recognition for digital artworks, technological challenges, and so on. The survey also serves as introductory material for artists and metaverse technologists to begin creations in the realm of surrealistic cyberspace.

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


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

11 Examples Of AI Climate Change Solutions For Zero Carbon – Forbes


The company's solution combines deep learning and machine learning technologies with symbolic AI to mimic human intuition.

On the Opportunities and Risks of Foundation Models 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 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.

Acquisition of Purdue-affiliated startup propels computer intelligence to the next level


WEST LAFAYETTE, Ind. – Technology that combines machine learning with artificial intelligence from Purdue University has taken its next giant leap toward powering more Internet of Things and edge computing devices. FWDNXT, a software and hardware startup that spun out of Purdue, was acquired in October by Micron Technology Inc., an industry leader in innovative memory and storage solutions. Micron is integrating FWDNXT's artificial intelligence hardware and software technology with its advanced memory to explore deep learning solutions for data analytics, particularly in IoT and edge computing. "Purdue provided the entrepreneurial resources to help me achieve my vision of taking our work on machine learning and deep learning technology to a much wider audience where we can have a bigger impact," said Eugenio Culurciello, Micron fellow and chief machine learning architect. "Micron has the leadership in memory, long history of innovation and drive to deliver power and performance capabilities that address the most complex and demanding edge applications at scale."