Reinforcement learningReinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.
Self-driving carA self-driving car, also known as an autonomous car, driverless car, or robotic car (robo-car), is a car that is capable of traveling without human input. Self-driving cars use sensors to perceive their surroundings, such as optical and thermographic cameras, radar, lidar, ultrasound/sonar, GPS, odometry and inertial measurement units. Control systems interpret sensory information to create a three-dimensional model of the vehicle's surroundings.
PerceptionPerception () is the organization, identification, and interpretation of sensory information in order to represent and understand the presented information or environment. All perception involves signals that go through the nervous system, which in turn result from physical or chemical stimulation of the sensory system. Vision involves light striking the retina of the eye; smell is mediated by odor molecules; and hearing involves pressure waves.
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.
Vehicular automationVehicular automation involves the use of mechatronics, artificial intelligence, and multi-agent systems to assist the operator of a vehicle (car, aircraft, watercraft, or otherwise). These features and the vehicles employing them may be labeled as intelligent or smart. A vehicle using automation for difficult tasks, especially navigation, to ease but not entirely replace human input, may be referred to as semi-autonomous, whereas a vehicle relying solely on automation is called robotic or autonomous.
VotingVoting is a method by which a group, such as a meeting or an electorate, convenes together for the purpose of making a collective decision or expressing an opinion usually following discussions, debates or election campaigns. Democracies elect holders of high office by voting. Residents of a jurisdiction represented by an elected official are called "constituents", and the constituents who choose to cast a ballot for their chosen candidate are called "voters.
Wasted voteIn electoral systems, a wasted vote is any vote that does not receive representation in the final election outcome. This includes lost votes for a losing candidate or party, and excess votes for winning candidates in excess of the minimum needed to win. When applied to ranked-vote systems, it includes exhausted votes, votes where none of the candidates the voter ranked are elected. Wasted vote share changes from one election to another depending on voter behavior. Wasted votes can lead to political apathy.
Spoilt voteIn voting, a ballot is considered spoilt, spoiled, void, null, informal, invalid or stray if a law declares or an election authority determines that it is invalid and thus not included in the vote count. This may occur accidentally or deliberately. The total number of spoilt votes in a United States election has been called the residual vote. In Australia, such votes are generally referred to as informal votes, and in Canada they are referred to as rejected votes. In some jurisdictions spoilt votes are counted and reported.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Contingent voteThe contingent vote is an electoral system used to elect a single representative in which a candidate requires a majority of votes to win. It is a variation of instant-runoff voting (IRV). Under the contingent vote, the voter ranks the candidates in order of preference, and the first preference votes are counted. If no candidate has a majority (more than half the votes cast), then all but the two leading candidates are eliminated and the votes received by the eliminated candidates are distributed among the two remaining candidates according to voters' preferences.
AwarenessIn philosophy and psychology, awareness is a concept about knowing, perceiving and being cognizant of events. Another definition describes it as a state wherein a subject is aware of some information when that information is directly available to bring to bear in the direction of a wide range of behavioral actions. The concept is often synonymous to consciousness and is also understood as being consciousness itself. The states of awareness are also associated with the states of experience so that the structure represented in awareness is mirrored in the structure of experience.
Generative adversarial networkA generative adversarial network (GAN) is a class of machine learning framework and a prominent framework for approaching generative AI. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss. Given a training set, this technique learns to generate new data with the same statistics as the training set.
