According to Gartner, AI applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decision-making, and take action. In essence, the concept of AI centres on enabling computer systems to think and act in a more'human' way, by learning from and responding to the vast amounts of information they're able to use. AI is already transforming our everyday lives. From the AI features on our smartphones such as built-in smart assistants, to the AI-curated content and recommendations on our social media feeds and streaming services. As the name suggests, machine learning is based on the idea that systems can learn from data to automate and improve how things are done – by using advanced algorithms (a set of rules or instructions) to analyse data, identify patterns and make decisions and recommendations based on what they find.
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.
AI - Artificial Intelligence AGI - Artificial General Intelligence ANN - Artificial Neural Network ANOVA - Analysis of Variance ANT - Actor Network Theory API - Application Programming Interface APX - Amsterdam Power Exchange AVE - Average Variance Extracted BU - Business Unit CART - Classification and Regression Tree CBMV - Crowd-based Business Model Validation CR - Composite Reliability CT - Computed Tomography CVC - Corporate Venture Capital DR - Design Requirement DP - Design Principle DSR - Design Science Research DSS - Decision Support System EEX - European Energy Exchange FsQCA - Fuzzy-Set Qualitative Comparative Analysis GUI - Graphical User Interface HI-DSS - Hybrid Intelligence Decision Support System HIT - Human Intelligence Task IoT - Internet of Things IS - Information System IT - Information Technology MCC - Matthews Correlation Coefficient ML - Machine Learning OCT - Opportunity Creation Theory OGEMA 2.0 - Open Gateway Energy Management 2.0 OS - Operating System R&D - Research & Development RE - Renewable Energies RQ - Research Question SVM - Support Vector Machine SSD - Solid-State Drive SDK - Software Development Kit TCP/IP - Transmission Control Protocol/Internet Protocol TCT - Transaction Cost Theory UI - User Interface VaR - Value at Risk VC - Venture Capital VPP - Virtual Power Plant Chapter I
Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.