Outline of ethicsEthics or moral philosophy is a branch of philosophy that involves systematizing, defending, and recommending concepts of right and wrong conduct. The field of ethics, along with aesthetics, concern matters of value, and thus comprise the branch of philosophy called axiology. The following outline is provided as an overview of and topical guide to ethics.
Deep reinforcement learningDeep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs (e.g.
Virtue ethicsVirtue ethics (also aretaic ethics, from Greek ἀρετή [aretḗ]) is an approach to ethics that treats virtue as central. Virtue ethics is usually contrasted with two other major approaches in ethics, consequentialism and deontology, which make the goodness of outcomes of an action (consequentialism) and the concept of moral duty (deontology) central. While virtue ethics does not necessarily deny the importance to ethics of goodness of states of affairs or of moral duties, it emphasizes virtue, and sometimes other concepts, like eudaimonia, to an extent that other ethics theories do not.
Role-playing video gameA role-playing video game, commonly referred to as a role-playing game (RPG) or computer role-playing game (CRPG), is a video game genre where the player controls the actions of a character (or several party members) immersed in some well-defined world, usually involving some form of character development by way of recording statistics. Many role-playing video games have origins in tabletop role-playing games and use much of the same , settings and game mechanics.
Q-learningQ-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations. For any finite Markov decision process (FMDP), Q-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state.
Ethical consumerismEthical consumerism (alternatively called ethical consumption, ethical purchasing, moral purchasing, ethical sourcing, or ethical shopping and also associated with sustainable and green consumerism) is a type of consumer activism based on the concept of dollar voting. People practice it by buying ethically made products that support small-scale manufacturers or local artisans and protect animals and the environment, while boycotting products that exploit children as workers, are tested on animals, or damage the environment.
Business ethicsBusiness ethics (also known as corporate ethics) is a form of applied ethics or professional ethics, that examines ethical principles and moral or ethical problems that can arise in a business environment. It applies to all aspects of business conduct and is relevant to the conduct of individuals and entire organizations. These ethics originate from individuals, organizational statements or the legal system. These norms, values, ethical, and unethical practices are the principles that guide a business.
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.
Automatic image annotationAutomatic image annotation (also known as automatic image tagging or linguistic indexing) is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a . This application of computer vision techniques is used in systems to organize and locate images of interest from a database. This method can be regarded as a type of multi-class with a very large number of classes - as large as the vocabulary size.
Rule-based machine learningRule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learners that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.
General game playingGeneral game playing (GGP) is the design of artificial intelligence programs to be able to play more than one game successfully. For many games like chess, computers are programmed to play these games using a specially designed algorithm, which cannot be transferred to another context. For instance, a chess-playing computer program cannot play checkers. General game playing is considered as a necessary milestone on the way to artificial general intelligence.
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.
