302,437 research outputs found
Artificial Intelligence and Statistics
Artificial intelligence (AI) is intrinsically data-driven. It calls for the
application of statistical concepts through human-machine collaboration during
generation of data, development of algorithms, and evaluation of results. This
paper discusses how such human-machine collaboration can be approached through
the statistical concepts of population, question of interest,
representativeness of training data, and scrutiny of results (PQRS). The PQRS
workflow provides a conceptual framework for integrating statistical ideas with
human input into AI products and research. These ideas include experimental
design principles of randomization and local control as well as the principle
of stability to gain reproducibility and interpretability of algorithms and
data results. We discuss the use of these principles in the contexts of
self-driving cars, automated medical diagnoses, and examples from the authors'
collaborative research
Leo Breiman
Statistics is a uniquely difficult field to convey to the uninitiated. It
sits astride the abstract and the concrete, the theoretical and the applied. It
has a mathematical flavor and yet it is not simply a branch of mathematics. Its
core problems blend into those of the disciplines that probe into the nature of
intelligence and thought, in particular philosophy, psychology and artificial
intelligence. Debates over foundational issues have waxed and waned, but the
field has not yet arrived at a single foundational perspective.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS387 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Recommended from our members
A.I. approaches in statistics
The role of pattern recognition and knowledge representation methods from artificial Intelligence with in statistics is considered. Two areas of potential use are identified and one, data exploration, is used to illustrate the possibilities. A method is presented to identify and seperate overlapping groups within cluster analysis, using an A.I. approach. The potential of such 'intelligent' approaches is stressed
ARTIFICIAL NEURAL NETWORKS AND THEIR APPLICATIONS IN BUSINESS
In modern software implementations of artificial neural networks the approach inspired by biology has more or less been abandoned for a more practical approach based on statistics and signal processing. In some of these systems, neural networks, or parts of neural networks (such as artificial neurons), are used as components in larger systems that combine both adaptive and non-adaptive elements. There are many problems which are solved with neural networks, especially in business and economic domains.neuron, neural networks, artificial intelligence, feed-forward neural networks, classification
Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields
Gibbs random fields play an important role in statistics, however, the
resulting likelihood is typically unavailable due to an intractable normalizing
constant. Composite likelihoods offer a principled means to construct useful
approximations. This paper provides a mean to calibrate the posterior
distribution resulting from using a composite likelihood and illustrate its
performance in several examples.Comment: JMLR Workshop and Conference Proceedings, 18th International
Conference on Artificial Intelligence and Statistics (AISTATS), San Diego,
California, USA, 9-12 May 2015 (Vol. 38, pp. 921-929). arXiv admin note:
substantial text overlap with arXiv:1207.575
Recommended from our members
Herbert Simon (1916-2001). The scientist of the artificial
With the disappearance of Herbert A. Simon, we have lost one of the most original thinkers of the 20th century. Highly influential in a number of scientific fields—some of which he actually helped create, such as artificial intelligence or information-processing psychology—Simon was a true polymath. His research started in management science and political science, later encompassed operations research, statistics and economics, and finally included computer science, artificial intelligence, psychology, education, philosophy of science, biology, and the sciences of design. His often controversial ideas earned him wide scientific recognition and essentially all the top awards of the fields in which he researched, including the Turing award from the Association of Computing Machinery, with Allen Newell, in 1975, the Nobel prize in economics, in 1978, and the Gold Medal Award for Psychological Science from the American Psychological Foundation, in 1988
Role of artificial intelligence in operations environment : a review and bibliometric analysis
Abstract: Purpose - ‘Technological Intelligence’ is the capacity to appreciate and adapt technological advancements, and ‘Artificial Intelligence’ is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave, and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its societal and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have we reached with respect to artificial intelligence research. Present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals, and citation statistics..
A New Approach to Probabilistic Programming Inference
We introduce and demonstrate a new approach to inference in expressive
probabilistic programming languages based on particle Markov chain Monte Carlo.
Our approach is simple to implement and easy to parallelize. It applies to
Turing-complete probabilistic programming languages and supports accurate
inference in models that make use of complex control flow, including stochastic
recursion. It also includes primitives from Bayesian nonparametric statistics.
Our experiments show that this approach can be more efficient than previously
introduced single-site Metropolis-Hastings methods.Comment: Updated version of the 2014 AISTATS paper (to reflect changes in new
language syntax). 10 pages, 3 figures. Proceedings of the Seventeenth
International Conference on Artificial Intelligence and Statistics, JMLR
Workshop and Conference Proceedings, Vol 33, 201
- …