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How to migrate from Neo4j to Memgraph

#artificialintelligence

Through this tutorial, you'll learn how to migrate the movies dataset from Neo4j to Memgraph. If you used Neo4j, you are probably familiar with their example graph that helps you learn the basics of the Cypher query language. Neo4j is an ACID-compliant transactional native graph database, while Memgraph is a platform designed for graph computations on streaming data. You can read more about their differences in the Neo4j vs Memgraph article. If you're having trouble running this on the new Apple M1 chip, try adding --platform linux/arm64/v8 after the run command.


Easy Filmmaking: Artificial Intelligence in Films & Videos

#artificialintelligence

This course is designed to teach you the ins and outs of easy film and video making by showing you the art and craft of making films and videos by showing you ... Want to make films and videos that really catch attention and praise? This online Easy Filmmaking Course will teach you how to make great films and videos using proven techniques and approaches. This course is designed to teach you the ins and outs of easy film and video making by showing you the art and craft of making films and videos by showing you how to plan, design and put them together. While there are plenty of courses about making films and videos, it's hard to find a course that gives you a step-by-step insight into making films easily that really punch through the noise. This is the course for you, taught by a professional filmmaker who has personally make more than 40 productions.


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.


Deep Reinforcement Learning

arXiv.org Artificial Intelligence

Deep reinforcement learning has gathered much attention recently. Impressive results were achieved in activities as diverse as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to solve difficult problems. They have learned to fly model helicopters and perform aerobatic manoeuvers such as loops and rolls. In some applications they have even become better than the best humans, such as in Atari, Go, poker and StarCraft. The way in which deep reinforcement learning explores complex environments reminds us of how children learn, by playfully trying out things, getting feedback, and trying again. The computer seems to truly possess aspects of human learning; this goes to the heart of the dream of artificial intelligence. The successes in research have not gone unnoticed by educators, and universities have started to offer courses on the subject. The aim of this book is to provide a comprehensive overview of the field of deep reinforcement learning. The book is written for graduate students of artificial intelligence, and for researchers and practitioners who wish to better understand deep reinforcement learning methods and their challenges. We assume an undergraduate-level of understanding of computer science and artificial intelligence; the programming language of this book is Python. We describe the foundations, the algorithms and the applications of deep reinforcement learning. We cover the established model-free and model-based methods that form the basis of the field. Developments go quickly, and we also cover advanced topics: deep multi-agent reinforcement learning, deep hierarchical reinforcement learning, and deep meta learning.


Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods

Journal of Artificial Intelligence Research

Interest in Artificial Intelligence (AI) and its applications has seen unprecedented growth in the last few years. This success can be partly attributed to the advancements made in the sub-fields of AI such as machine learning, computer vision, and natural language processing. Much of the growth in these fields has been made possible with deep learning, a sub-area of machine learning that uses artificial neural networks. This has created significant interest in the integration of vision and language. In this survey, we focus on ten prominent tasks that integrate language and vision by discussing their problem formulation, methods, existing datasets, evaluation measures, and compare the results obtained with corresponding state-of-the-art methods. Our efforts go beyond earlier surveys which are either task-specific or concentrate only on one type of visual content, i.e., image or video. Furthermore, we also provide some potential future directions in this field of research with an anticipation that this survey stimulates innovative thoughts and ideas to address the existing challenges and build new applications.


Snakes AI Competition 2020 and 2021 Report

arXiv.org Artificial Intelligence

The Snakes AI Competition was held by the Innopolis University and was part of the IEEE Conference on Games2020 and 2021 editions. It aimed to create a sandbox for learning and implementing artificial intelligence algorithms in agents in a ludic manner. Competitors of several countries participated in both editions of the competition, which was streamed to create asynergy between organizers and the community. The high-quality submissions and the enthusiasm around the developed framework create an exciting scenario for future extensions.


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.


Dive into Deep Learning

arXiv.org Artificial Intelligence

Just a few years ago, there were no legions of deep learning scientists developing intelligent products and services at major companies and startups. When the youngest among us (the authors) entered the field, machine learning did not command headlines in daily newspapers. Our parents had no idea what machine learning was, let alone why we might prefer it to a career in medicine or law. Machine learning was a forward-looking academic discipline with a narrow set of real-world applications. And those applications, e.g., speech recognition and computer vision, required so much domain knowledge that they were often regarded as separate areas entirely for which machine learning was one small component. Neural networks then, the antecedents of the deep learning models that we focus on in this book, were regarded as outmoded tools. In just the past five years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, reinforcement learning, and statistical modeling. With these advances in hand, we can now build cars that drive themselves with more autonomy than ever before (and less autonomy than some companies might have you believe), smart reply systems that automatically draft the most mundane emails, helping people dig out from oppressively large inboxes, and software agents that dominate the worldʼs best humans at board games like Go, a feat once thought to be decades away. Already, these tools exert ever-wider impacts on industry and society, changing the way movies are made, diseases are diagnosed, and playing a growing role in basic sciences--from astrophysics to biology.


Microsoft Partners With Netflix To Create New Data Science Learning Modules

#artificialintelligence

With the increasing requirement for more data scientists, ML experts, and AI engineers in every industry, Microsoft, in partnership with Netflix, has launched three new learning modules to guide learners through beginning concepts in data science, machine learning and artificial intelligence. Inspired by the new Netflix original film -- 'Over the Moon' these learning modules include three paths -- planning a Moon mission using the Python Pandas Library; predicting meteor showers using Python and VC Code; and using AI to recognise objects in images using Azure Custom Vision. The growing requirement of data scientists comes with criteria of having a broad set of skills from data analysis with no-code and low-code solutions which will help them with designing and writing intricate ML models and solve some of the planet's most difficult problems. Keeping this in mind, Microsoft, partnering with Netflix, has launched these modules for providing high quality, free content to help learners develop their skills depending based on their professional goals and personal interests. According to Microsoft's blog post, "One such endeavour in creating an opportunity for you to learn and upskill is through unique partnerships. In the summer of 2020, we launched a set of Microsoft Learn modules inspired by real NASA scientists and engineers at https://aka.ms/LearnInSpace. And this Fall we are excited to bring you three more Microsoft Learn modules inspired by the new Netflix Original Over the Moon."


Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

arXiv.org Artificial Intelligence

Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.