Automation bias is when a human decision maker favors recommendations made by an automated decision-making system over information made without automation, even when the automated decision-making system makes errors115.
Automation is a technology by which a process or procedure is performed with minimal human intervention116.
Autonomic computing is the ability of a system to adaptively self-manage its own resources for high-level computing functions without user input117.
Autonomous artificial intelligence is a biologically inspired system that tries to reproduce the structure of the brain, the principles of its operation with all the properties that follow from this118,119.
Autonomous car (also self-driving car, robot car, and driverless car) is a vehicle that is capable of sensing its environment and moving with little or no human input120.
Autonomous is a machine is described as autonomous if it can perform its task or tasks without needing human intervention121.
Autonomous robot is a robot that performs behaviors or tasks with a high degree of autonomy. Autonomous robotics is usually considered to be a subfield of artificial intelligence, robotics, and information engineering122.
Autonomous vehicle is a mode of transport based on an autonomous driving system. The control of an autonomous vehicle is fully automated and carried out without a driver using optical sensors, radar and computer algorithms123.
Autoregressive Model is an autoregressive model is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step. In statistics and signal processing, an autoregressive model is a representation of a type of random process. It is used to describe certain time-varying processes in nature, economics, etc.124.
Auxiliary intelligence systems based on artificial intelligence that complement human decisions and are able to learn in the process of interacting with people and the environment.
Average precision is a metric for summarizing the performance of a ranked sequence of results. Average precision is calculated by taking the average of the precision values for each relevant result (each result in the ranked list where the recall increases relative to the previous result)125.
Ayasdi is an enterprise scale machine intelligence platform that delivers the automation that is needed to gain competitive advantage from the companys big and complex data. Ayasdi supports large numbers of business analysts, data scientists, endusers, developers and operational systems across the organization, simultaneously creating, validating, using and deploying sophisticated analyses and mathematical models at scale126.
«B»
Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks. It can be used to train Elman networks. The algorithm was independently derived by numerous researchers127.
Backpropagation, also called «backward propagation of errors,» is an approach that is commonly used in the training process of the deep neural network to reduce errors128.
Backward Chaining, also called goal-driven inference technique, is an inference approach that reasons backward from the goal to the conditions used to get the goal. Backward chaining inference is applied in many different fields, including game theory, automated theorem proving, and artificial intelligence129.
Bag-of-words model in computer vision. In computer vision, the bag-of-words model (BoW model) can be applied to image classification, by treating image features as words. In document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary. In computer vision, a bag of visual words is a vector of occurrence counts of a vocabulary of local image features130.
Bag-of-words model is a simplifying representation used in natural language processing and information retrieval (IR). In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order but keeping multiplicity. The bag-of-words model has also been used for computer vision. The bag-of-words model is commonly used in methods of document classification where the (frequency of) occurrence of each word is used as a feature for training a classifier131.
Baldwin effect the skills acquired by organisms during their life as a result of learning, after a certain number of generations, are recorded in the genome132.
Baseline is a model used as a reference point for comparing how well another model (typically, a more complex one) is performing. For example, a logistic regression model might serve as a good baseline for a deep model. For a particular problem, the baseline helps model developers quantify the minimal expected performance that a new model must achieve for the new model to be useful133.
Batch the set of examples used in one gradient update of model training134.
Batch Normalization is a preprocessing step where the data are centered around zero, and often the standard deviation is set to unity135.
Batch size the number of examples in a batch. For example, the batch size of SGD is 1, while the batch size of a mini-batch is usually between 10 and 1000. Batch size is usually fixed during training and inference; however, TensorFlow does permit dynamic batch sizes136,137.
Bayess Theorem is a famous theorem used by statisticians to describe the probability of an event based on prior knowledge of conditions that might be related to an occurrence138.
Bayesian classifier in machine learning is a family of simple probabilistic classifiers based on the use of the Bayes theorem and the «naive» assumption of the independence of the features of the objects being classified139.
Bayesian Filter is a program using Bayesian logic. It is used to evaluate the header and content of email messages and determine whether or not it constitutes spam unsolicited email or the electronic equivalent of hard copy bulk mail or junk mail. A Bayesian filter works with probabilities of specific words appearing in the header or content of an email. Certain words indicate a high probability that the email is spam, such as Viagra and refinance140.
Bayesian Network, also called Bayes Network, belief network, or probabilistic directed acyclic graphical model, is a probabilistic graphical model (a statistical model) that represents a set of variables and their conditional dependencies via a directed acyclic graph141.
Bayesian optimization is a probabilistic regression model technique for optimizing computationally expensive objective functions by instead optimizing a surrogate that quantifies the uncertainty via a Bayesian learning technique. Since Bayesian optimization is itself very expensive, it is usually used to optimize expensive-to-evaluate tasks that have a small number of parameters, such as selecting hyperparameters142.
Bayesian programming is a formalism and a methodology for having a technique to specify probabilistic models and solve problems when less than the necessary information is available143,144.
Bees algorithm is a population-based search algorithm which was developed by Pham, Ghanbarzadeh and et al. in 2005. It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both combinatorial optimization and continuous optimization. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies145.