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

arXiv.org 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.


Seven Courses In 2021 On Deep Learning For Art

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Advancements in artificial intelligence have paved the way for researchers and scientists to train machines to interpret images, sounds, and patterns in videos. Deep learning algorithms have given a new meaning to artistic explorations, which have stunned the world. AI is gradually advancing to become the next medium for art, and several artists worldwide relate to this seamless development. In a world that is evolving amid codes and hi-tech machines, Generative Adversarial Networks (GANs) are a specific class of algorithms being used to generate AI artworks. From pitch recognition to natural image synthesis and generating human faces that never existed-- machines are likely to match human senses.


The State of AI Ethics Report (Volume 5)

arXiv.org Artificial Intelligence

This report from the Montreal AI Ethics Institute covers the most salient progress in research and reporting over the second quarter of 2021 in the field of AI ethics with a special emphasis on "Environment and AI", "Creativity and AI", and "Geopolitics and AI." The report also features an exclusive piece titled "Critical Race Quantum Computer" that applies ideas from quantum physics to explain the complexities of human characteristics and how they can and should shape our interactions with each other. The report also features special contributions on the subject of pedagogy in AI ethics, sociology and AI ethics, and organizational challenges to implementing AI ethics in practice. Given MAIEI's mission to highlight scholars from around the world working on AI ethics issues, the report also features two spotlights sharing the work of scholars operating in Singapore and Mexico helping to shape policy measures as they relate to the responsible use of technology. The report also has an extensive section covering the gamut of issues when it comes to the societal impacts of AI covering areas of bias, privacy, transparency, accountability, fairness, interpretability, disinformation, policymaking, law, regulations, and moral philosophy.


The Top 100 Software Companies of 2021

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The Software Report is pleased to announce The Top 100 Software Companies of 2021. This year's awardee list is comprised of a wide range of companies from the most well-known such as Microsoft, Adobe, and Salesforce to the relatively newer but rapidly growing - Qualtrics, Atlassian, and Asana. A good number of awardees may be new names to some but that should be no surprise given software has always been an industry of startups that seemingly came out of nowhere to create and dominate a new space. Software has become the backbone of our economy. From large enterprises to small businesses, most all rely on software whether for accounting, marketing, sales, supply chain, or a myriad of other functions. Software has become the dominant industry of our time and as such, we place a significance on highlighting the best companies leading the industry forward. The following awardees were nominated and selected based on a thorough evaluation process. Among the key criteria considered were ...


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.


Machine learning enhances non-verbal communication in online classrooms

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June 21, 2021--Researchers in the Center for Research on Entertainment and Learning (CREL) at the University of California San Diego have developed a system to analyze and track eye movements to enhance teaching in tomorrow's virtual classrooms – and perhaps future virtual concert halls. UC San Diego music and computer science professor Shlomo Dubnov, an expert in computer music who directs the Qualcomm Institute-based CREL, began developing the new tool to deal with a downside of teaching music over Zoom during the COVID-19 pandemic. "In a music classroom, non-verbal communication such as facial affect and body gestures is critical to keep students on task, coordinate musical flow and communicate improvisational ideas," said Dubnov. "Unfortunately, this non-verbal aspect of teaching and learning is dramatically hampered in the virtual classroom where you don't inhabit the same physical space." To overcome the problem, Dubnov and Ph.D. student Ross Greer recently published a conference paper on a system that uses eye tracking and machine learning to allow an educator to make'eye contact' with individual students or performers in disparate locations – and lets each student know when he or she is the focus of the teacher's attention.


When Newsrooms Collaborate With AI - Liwaiwai

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Two years ago, the Google News Initiative partnered with the London School of Economics and Political Science to launch JournalismAI, a global effort to foster media literacy in newsrooms through research, training and experimentation. Since then, more than 62 thousand journalists have taken Introduction to Machine Learning, an online course provided in 17 languages in partnership with Belgian broadcaster VRT. More than 4,000 people have downloaded the JournalismAI report, which argued that "robots are not going to take over journalism" and that media organizations are keen to collaborate with one another and with technology companies. And over 20 media organizations including La Nación, Reuters, the South China Morning Post and The Washington Post have joined Collab, a global partnership to experiment with AI. To mark this anniversary, together with the London School of Economics, we are hosting a week-long online event to bring together international academics, publishers and practitioners.


[P] Call for paper for workshop on AI education at AAAI 2021

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Researchers at Riiid are pleased to announce the Call for Papers for a virtual Workshop on AI Education to be held at AAAI 2021 in early February 2021. COVID-19 has brought upon us the inevitable transformation towards virtual education. The ensuing need for scalable, personalized learning systems has led to an unprecedented demand for understanding large-scale educational data. In this workshop, we will call for papers related to important Artificial Intelligence in Education (AIEd) topics that can help us imagine what new education will look like post COVID-19. Submissions of papers including Kaggle competition technical papers, shared task technical papers and general submissions should follow the AAAI format and can be up to 8 pages excluding references and appendices.


Applications of Deep Neural Networks

arXiv.org Artificial Intelligence

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Readers will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this book; however, familiarity with at least one programming language is assumed.


[R] Maximizing Computer Vision's Field of View (CVPR 2020) - Free live online lecture by the researcher

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Following the amazing turn in of redditors for previous lectures, we are organizing another free zoom lecture for the reddit community. In this next lecture Dr. Marc Eder will talk about his research - Maximizing Computer Visions's Field. This talk will introduce the emerging field of 360 computer vision, and provide an overview of the spherical distortion problem, highlighting how this distortion affects many of the highest profile problems in computer vision, from deep learning to structure-from-motion and SLAM. It will survey some of the existing work on the topic, and identify 3 guiding principles that drive a general solution to the problem. Finally, we will conclude with some opportunities for further research and some big picture takeaways from work thus far.