Prior to the acquisition of NeuroDimension (in 2016), it was a software development company headquartered in Gainesville, Florida and founded in 1991 by Steven Reid, MD, Jose Principe, PhD (Director of the Computational Neural Engineering Lab at the University of Florida) and Curt Lefebvre, PhD (CEO of nDimensional). Dr. Reid provided the initial capital to get the company off the ground. Dr. Principe provided the engineering staff with technical direction and had helped secure research grant funding for the company. Dr. Lefebvre was the principal author of the company’s core neural network technology.
The company was formed around a software tool, NeuroSolutions, which enables engineers and researchers to model their data using neural networks.
Financial Analysis and TradingSolutions
In 1997, it became apparent that one of the most common uses of NeuroSolutions was to create neural network models to time the financial markets.[citation needed]
Released in 2008, Trader68 handles the trading and distribution of trading signals from TradingSolutions, proprietary research, and other sources.
In late 2015, Trader68 was discontinued and is no longer supported or actively developed. TradingSolutions was discontinued in 2016.
nDimensional, Inc. Acquires NeuroDimension, Inc.
In August 2016, nDimensional, Inc. announced the acquisition of NeuroDimension, Inc. to help accelerate its new web-based Platform-as-a-Service product called nD to market.[citation needed]
Artificial neural networks (ANNs), usually simply called neural networks (NNs), are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.
In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.
NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks developed by Ken Stanley in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity. It is based on applying three key techniques: tracking genes with history markers to allow crossover among topologies, applying speciation to preserve innovations, and developing topologies incrementally from simple initial structures ("complexifying").
Neuroevolution, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, topology and rules. It is most commonly applied in artificial life, general game playing and evolutionary robotics. The main benefit is that neuroevolution can be applied more widely than supervised learning algorithms, which require a syllabus of correct input-output pairs. In contrast, neuroevolution requires only a measure of a network's performance at a task. For example, the outcome of a game can be easily measured without providing labeled examples of desired strategies. Neuroevolution is commonly used as part of the reinforcement learning paradigm, and it can be contrasted with conventional deep learning techniques that use gradient descent on a neural network with a fixed topology.
Evolutionary robotics (ER) is a methodology that uses evolutionary computation to develop controllers and/or hardware for autonomous robots. Algorithms in ER frequently operate on populations of candidate controllers, initially selected from some distribution. This population is then repeatedly modified according to a fitness function. In the case of genetic algorithms, a common method in evolutionary computation, the population of candidate controllers is repeatedly grown according to crossover, mutation and other GA operators and then culled according to the fitness function. The candidate controllers used in ER applications may be drawn from some subset of the set of artificial neural networks, although some applications use collections of "IF THEN ELSE" rules as the constituent parts of an individual controller. It is theoretically possible to use any set of symbolic formulations of a control law as the space of possible candidate controllers. Artificial neural networks can also be used for robot learning outside the context of evolutionary robotics. In particular, other forms of reinforcement learning can be used for learning robot controllers.
In the field of artificial intelligence, neuro-fuzzy refers to combinations of artificial neural networks and fuzzy logic.
Neural network software is used to simulate, research, develop, and apply artificial neural networks, software concepts adapted from biological neural networks, and in some cases, a wider array of adaptive systems such as artificial intelligence and machine learning.
NeuroSolutions is a neural network development environment developed by NeuroDimension. It combines a modular, icon-based (component-based) network design interface with an implementation of advanced learning procedures, such as conjugate gradients, Levenberg-Marquardt and backpropagation through time. The software is used to design, train and deploy neural network models to perform a wide variety of tasks such as data mining, classification, function approximation, multivariate regression and time-series prediction.
Prosoniq Products Software was a German software developer of audio and music tools, mostly known for their sonicWORX, OrangeVocoder, TimeFactory and Hartmann Neuron synthesizer products. It also licensed proprietary technologies in the audio/music DSP sector to software manufacturers including Emagic, Steinberg, Digidesign, TwelveTone Systems, Merging, DAVID, AutoDesk/Discreet and others. Headquartered in Karlsruhe, Germany, Prosoniq pioneered the use of artificial neural networks for commercial audio processing.
Altium Limited is an American, Australian-domiciled owned public software company that provides PC-based electronics design software for engineers who design printed circuit boards. Founded as Protel Systems Pty Ltd in Tasmania, Australia in 1985, Altium now has regional headquarters in the United States, Australia, China, Europe, and Japan, with resellers in all other major markets.
Evolutionary acquisition of neural topologies (EANT/EANT2) is an evolutionary reinforcement learning method that evolves both the topology and weights of artificial neural networks. It is closely related to the works of Angeline et al. and Stanley and Miikkulainen. Like the work of Angeline et al., the method uses a type of parametric mutation that comes from evolution strategies and evolutionary programming, in which adaptive step sizes are used for optimizing the weights of the neural networks. Similar to the work of Stanley (NEAT), the method starts with minimal structures which gain complexity along the evolution path.
IBM SPSS Modeler is a data mining and text analytics software application from IBM. It is used to build predictive models and conduct other analytic tasks. It has a visual interface which allows users to leverage statistical and data mining algorithms without programming.
In computer science, an evolving intelligent system is a fuzzy logic system which improves the own performance by evolving rules. The technique is known from machine learning, in which external patterns are learned by an algorithm. Fuzzy logic based machine learning works with neuro-fuzzy systems.
Jeffrey Owen Katz is an American scientist best known for his pivotal contribution to the field of factor analysis and his development of innovative AI tools. Born April 6, 1950, he is the only child of Nathan Katz (accountant) and Rosalyn Anker. He grew up in Queens, New York, but moved with his family to Merrick, N.Y. in 1962. He was a recognized child prodigy in electronic engineering, able to read and draw schematic diagrams before he could read and write English. Rather than send their only child away to a boarding school for gifted children, his parents arranged for home-schooling, which was continued until Katz' mid-teens when he began auditing college level courses at local universities.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
The following outline is provided as an overview of and topical guide to machine learning. Machine learning is a subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.
Andrzej Cichocki from the RIKEN Brain Science Institute, Wako, Saitama, Japan was named Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2013 for contributions to applications of blind signal processing and artificial neural networks.
Babak Hodjat was the co-founder and CEO of Sentient Technologies and now holds the position of Vice President of Evolutionary AI at Cognizant. He is a specialist in the field of artificial intelligence and machine learning.
Aidoc is an Israeli company that develops computer-aided simple triage systems.
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