Emotional exhaustionEmotional exhaustion is symptom of burnout, a chronic state of physical and emotional depletion that results from excessive work or personal demands, or continuous stress. It describes a feeling of being emotionally overextended and exhausted by one's work. It is manifested by both physical fatigue and a sense of feeling psychologically and emotionally "drained". There are various ways in which burnout can occur such as: impacting an individual's physical, emotional, and behavioral aspects.
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Affective computingAffective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer science, psychology, and cognitive science. While some core ideas in the field may be traced as far back as to early philosophical inquiries into emotion, the more modern branch of computer science originated with Rosalind Picard's 1995 paper on affective computing and her book Affective Computing published by MIT Press.
DALL-EDALL-E (stylized as DALL·E) and DALL-E 2 are s developed by OpenAI using deep learning methodologies to generate s from natural language descriptions, called "prompts". DALL-E was revealed by OpenAI in a blog post in January 2021, and uses a version of GPT-3 modified to generate images. In April 2022, OpenAI announced DALL-E 2, a successor designed to generate more realistic images at higher resolutions that "can combine concepts, attributes, and styles". OpenAI has not released source code for either model.
DesireDesires are states of mind that are expressed by terms like "wanting", "wishing", "longing" or "craving". A great variety of features is commonly associated with desires. They are seen as propositional attitudes towards conceivable states of affairs. They aim to change the world by representing how the world should be, unlike beliefs, which aim to represent how the world actually is. Desires are closely related to agency: they motivate the agent to realize them.
Positive psychologyPositive psychology is a branch of psychology that studies the conditions that contribute to the optimal functioning of people, groups, and institutions. It studies "positive subjective experience, positive individual traits, and positive institutions... it aims to improve quality of life." It is a field of study that has grown as individuals and researchers look for common ground on better well-being. Positive psychology began as a new domain of psychology in 1998 when Martin Seligman chose it as the theme for his term as president of the American Psychological Association.
Online machine learningIn computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms.
Artificial intelligence artArtificial intelligence art is any visual artwork created through the use of artificial intelligence (AI) programs. Artists began to create AI art in the mid- to late-20th century, when the discipline was founded. In the early 21st century, the availability of AI art tools to the general public increased, providing opportunities for use outside of academia and professional artists. Throughout its history, artificial intelligence art has raised many philosophical concerns, including related to copyright, deception, and impact on more traditional artists.
Text-to-image modelA text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description. Such models began to be developed in the mid-2010s, as a result of advances in deep neural networks. In 2022, the output of state of the art text-to-image models, such as OpenAI's DALL-E 2, Google Brain's , StabilityAI's Stable Diffusion, and Midjourney began to approach the quality of real photographs and human-drawn art.
Unsupervised learningUnsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.
