Koomey's law This law posits that the energy efficiency of computation doubles roughly every one-and-a-half years (see Figure 1–7). In other words, the energy necessary for the same amount of computation halves in that time span. To visualize the exponential impact this has, consider the face that a fully charged MacBook Air, when applying the energy efficiency of computation of 1992, would completely drain its battery in a mere 1.5 seconds. According to Koomey's law, the energy requirements for computation in embedded devices is shrinking to the point that harvesting the required energy from ambient sources like solar power and thermal energy should suffice to power the computation necessary in many applications. Metcalfe's law This law has nothing to do with chips, but all to do with connectivity. Formulated by Robert Metcalfe as he invented Ethernet, the law essentially states that the value of a network increases exponentially with regard to the number of its nodes (see Figure 1–8).
Computer vision (sometimes called machine vision) is one of the most exciting applications of artificial intelligence. Algorithms that are able to understand images – both pictures and moving video – are a key technological foundation behind many innovations, from autonomous, self-driving vehicles to smart industrial machinery and even the filters on your phone that make the pictures you upload to Instagram look more pretty. Along with language processing abilities (natural language processing, or "NLP") its fundamental to our efforts to build machines that are capable of understanding and learning about the world around them, just like we do. Generally, it involves applications powered by deep learning – neural networks trained on thousands, millions or billions of images until they become experts at classifying what they can "see." The value of the market in computer vision technology is predicted to hit $48 billion by the end of 2022 and is likely to be a source of ongoing innovation and breakthroughs throughout the year. So let's take a look at some of the key trends we'll be following involving this fascinating technology: Data-centric artificial intelligence is based on the idea that equal, if not more, focus should be put into optimizing the quality of data used to train algorithms, as is put into developing the models and algorithms themselves.
The start of the Democratizing AI Newsletter which focuses in the first edition on "Artificial Intelligence a Key Exponential Technology in the Smart Technology Era" coincides with the launch of BiCstreet's "AI World Series" Live event, which kicks off both virtually and in-person (limited) from 10 March 2022, where this theme, amongst others, will be discussed in more detail over a 10-week AI World Series programme. The event is an excellent opportunity for companies, startups, governments, organisations and white collar professionals all over the world, to understand why Artificial Intelligence is critical towards strategic growth for any department or genre. See the 10 Weekly Program here: https://www.BiCstreet.com)). We live in tremendously exciting times where we already experience the disruptive and far-reaching impact of a smart technology revolution that seems to be on track to comprehensively change how we live, work, play, interact, and relate to one another.
AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner How intellect is artificial intelligence today? It is as smart as its dump, dull and deficient (3d). Today's quasi-AI is biased, black box, oblique, weak and narrow. It could blindly and unknowingly perform strictly what it was designed for, videogame/chess/strategic games playing, self-driving, language translation, face recognition, fraud detection, speech communication, product recommendation, pattern matching, generating poetry or music or images or faces or new molecules, etc. It is all relying on statistical relationships in raw input data sets to generate some patterns that humans find useful.
This special issue interrogates the meaning and impacts of "tech ethics": the embedding of ethics into digital technology research, development, use, and governance. In response to concerns about the social harms associated with digital technologies, many individuals and institutions have articulated the need for a greater emphasis on ethics in digital technology. Yet as more groups embrace the concept of ethics, critical discourses have emerged questioning whose ethics are being centered, whether "ethics" is the appropriate frame for improving technology, and what it means to develop "ethical" technology in practice. This interdisciplinary issue takes up these questions, interrogating the relationships among ethics, technology, and society in action. This special issue engages with the normative and contested notions of ethics itself, how ethics has been integrated with technology across domains, and potential paths forward to support more just and egalitarian technology. Rather than starting from philosophical theories, the authors in this issue orient their articles around the real-world discourses and impacts of tech ethics--i.e., tech ethics in action.
Andrew Yan-Tak Ng, a computer scientist and technology entrepreneur who focuses on machine learning and Artificial Intelligence (AI), said'AI is the new electricity.' In a world where we are all becoming increasingly dependent on technology, it would be hard to think of any industry that is untouched by AI. Just as electricity transformed almost every single aspect of our lives a 100 years ago with its incredible capabilities and convenience, in a similar manner AI is now being embedded in our daily experiences. From unlocking your phone using facial recognition software to making recommendations for restaurants that will be close by based off what you like to eat! Even when you chat with a customer care chatbot, you are literally talking with AI.
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.
Most artificial intelligence models are trained through supervised learning, meaning that humans must label raw data. Data labeling is a critical part of automating artificial intelligence and machine learning model, but at the same time, it can be time-consuming and tedious work. A Korean startup called AIMMO, which uses software and humans to label and categorize image, video, sound, text and sensor fusion data, built an AI data annotation platform, enabling the data labeling faster for enterprises. AIMMO announced today it has raised $12 million in a Series A round to advance its data labeling technology and spur global expansions. Seven venture capital firms participated in the latest round: DS Asset Management, Industrial Bank of Korea, Hanwha Investment & Securities, S&S Investment, Toss Investment, Korea Asset Investment & Securities and Venture Field.
Artificial intelligence (AI) is a broad field of computer science that focuses on creating intelligent machines that can accomplish activities that would normally need human intelligence. Machines may learn from their experiences, adapt to new inputs, and execute human-like jobs thanks to artificial intelligence (AI). Most AI examples you hear about today rely largely on deep learning and natural language processing, from chess-playing computers to self-driving cars. Computers can be trained to perform certain jobs by processing massive volumes of data and recognizing patterns in the data using these methods. Artificial Intelligence refers to the intelligence displayed by machines. In today's world, Artificial Intelligence has become highly popular. It is the simulation of human intelligence in computers that have been programmed to learn and mimic human actions.
Artificial intelligence is a target for every existing industry Or is it just another hyped innovation? It comes with no surprise how AI today becomes a catchall term that is said out loud in the job market. The US and China are in nip and tuck in the AI race for supremacy. Although China aims to be the technology leader by 2030, the economy is still at a struggle phase with a slowdown and trade war with the US. Emerging trends in artificial intelligence (AI) significantly points toward having a geopolitical disruption in the foreseeable future. As much as the