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A Non-Mathematical Introduction to Using Neural Networks

A Non-Mathematical Introduction to Using Neural Networks
The goal of this article is to help you understand what a neural network is, and how it is used. Most people, even non-programmers, have heard of neural networks. There are many science fiction overtones associated with them. Most laypeople think of neural networks as a sort of artificial brain. Neural networks are one small part of AI. The human brain really should be called a biological neural network (BNN). There are some basic similarities between biological neural networks and artificial neural networks. Like I said, neural networks are designed to accomplish one small task. The task that neural networks accomplish very well is pattern recognition. Figure 1: A Typical Neural Network As you can see, the neural network above is accepting a pattern and returning a pattern. Neural Network Structure Neural networks are made of layers of similar neurons. The input and output patterns are both arrays of floating point numbers. Programming hash tables use keys and values. A Simple Example Related:  Neural NetworkVeille technologiqueA bit about Neuro-computing (science)

Collective Intelligence in Neural Networks and Social Networks « 100 Trillion Connections Context for this post: I’m currently working on a social network application that demonstrates the value of connection strength and context for making networks more useful and intelligent. Connection strength and context are currently only rudimentarily and mushily implemented in social network apps. This post describes some of the underlying theory for why connection strength and context are key to next generation social network applications. A recent study of how behavioral decisions are made in the brain makes it clear how important strengths of connections are to the intelligence of networks. “Scientists at the University of Rochester, Washington University in St. “The answer lies in a simple computation performed by single nerve cells: a weighted average. (The fact that neurons in the brain make a weighted average of thousands of inputs has long been understood in theory. Obviously individual humans are enormously more complex than individual neurons. 1. 2. 3. 4. Like this:

OpenAi - - about us The OpenAI site is centered around an Open Source project and community involving artificial intelligence. The term "Open Source" means that the source code for the project is available for free and can be used by others free of charge. Artificial Intelligence refers to the general aim of intelligent computing, making machines think and learn. The project itself is the creation of a set of tools that are considered to be models of human intelligence. These tools are intended to be integrated into programs or used stand alone for research. We're looking for this site to be: a home for the OpenAI project a place for the community to connect an information repository for AI in general a showcase for the tools The project itself is geared toward developing a specification for AI related tools. OpenAI will also provide the details of the specification online as it develops so that the community can help in it's creation by giving insight and criticism.

Neuro Evolving Robotic Operatives Neuro-Evolving Robotic Operatives, or NERO for short, is a unique computer game that lets you play with adapting intelligent agents hands-on. Evolve your own robot army by tuning their artificial brains for challenging tasks, then pit them against your friends' teams in online competitions! New features in NERO 2.0 include an interactive game mode called territory capture, as well as a new user interface and more extensive training tools. NERO is a result of an academic research project in artificial intelligence, based on the rtNEAT algorithm. Currently, we are developing an open source successor to NERO , OpenNERO , a game platform for AI research and education. IBM simulates 530 billon neurons, 100 trillion synapses on supercomputer A network of neurosynaptic cores derived from long-distance wiring in the monkey brain: Neuro-synaptic cores are locally clustered into brain-inspired regions, and each core is represented as an individual point along the ring. Arcs are drawn from a source core to a destination core with an edge color defined by the color assigned to the source core. (Credit: IBM) Announced in 2008, DARPA’s SyNAPSE program calls for developing electronic neuromorphic (brain-simulation) machine technology that scales to biological levels, using a cognitive computing architecture with 1010 neurons (10 billion) and 1014 synapses (100 trillion, based on estimates of the number of synapses in the human brain) to develop electronic neuromorphic machine technology that scales to biological levels.” Simulating 10 billion neurons and 100 trillion synapses on most powerful supercomputer Neurosynaptic core (credit: IBM) Two billion neurosynaptic cores DARPA SyNAPSE Phase 0DARPA SyNAPSE Phase 1DARPA SyNAPSE Phase 2

Khan Academy Crowd Computing and The Synaptic Web A couple of days ago David Gelernter – a known Computer Science Visionary who famously survived an attack by the Unabomber – wrote a piece on Wired called ‘The End of the Web, Search, and Computer as We Know It’. In it, he summarized one of his predictions around the web moving from a static document oriented web to a network of streams. Nova Spivack, my Co-founder and CEO at Bottlenose, also wrote about this in more depth in his blog series about The Stream. I’ve been interested in the work of David Gelernter for quite some time and thought this might be a good time to revisit some of his previous predictions. Crowd Computing 18. In order to make software and apps more intelligent, there is a vast amount of computation that needs to be done. At my company we send raw social media messages down to the browser, the browser then does natural language processing, semantic analysis and finally our StreamSense algorithms to discover trends in the stream. Bottlenose Crowd Computing Layer 28.

Lotus Artificial Life - Hardware Artificial Life This applet displays a cellular automata substrate capable of supporting evolving, self-reproducing which are capable of universal computation. The applet is fully interactive, allowing you to apply selection based on organisms visual characteristics using a variety of implements. Selection may also applied automatically. Currently the built in selection methods are for size and shape only. The cellular automata uses a strict von-Neumann neighbourhood and is based on an innovative, multi-layered design. The whole architecture is designed to be implemented on massively parallel hardware. Note: if you're playing with wiping out organisms manually you'll probably want to have the 'No selection at all' checkbox ticked - this causes all cells to be born pregnant and removes some constraints which abort malformed offspring.

OVERVIEW OF NEURAL NETWORKS This installment addresses the subject of computer-models of neural networks and the relevance of those models to the functioning brain. The computer field of Artificial Intelligence is a vast bottomless pit which would lead this series too far from biological reality -- and too far into speculation -- to be included. Neural network theory will be the singular exception because the model is so persuasive and so important that it cannot be ignored. Neurobiology provides a great deal of information about the physiology of individual neurons as well as about the function of nuclei and other gross neuroanatomical structures. The building-block of computer-model neural networks is a processing unit called a neurode, which captures many essential features of biological neurons. In the diagram, three neurodes are shown, which can perform the logical operation "AND", ie, the output neurode will fire only if the two input neurodes are both firing. Neural networks are "black boxes" of memory.

Introduction aux Réseaux de Neurones Artificiels Feed Forward Plongeons-nous dans l'univers de la reconnaissance de formes. Plus particulièrement, nous allons nous intéresser à la reconnaissance des chiffres (0, 1, ..., 9). Imaginons un programme qui devrait reconnaître, depuis une image, un chiffre. De façon plus générale, un réseau de neurone permet l'approximation d'une fonction. Dans la suite de l'article, on notera un vecteur dont les composantes sont les n informations concernant un exemple donné. Voyons maintenant d'où vient la théorie des réseaux de neurones artificiels. Comment l'homme fait-il pour raisonner, parler, calculer, apprendre... ? Approches adoptée en recherche en Intelligence Artificielle procéder d'abord à l'analyse logique des tâches relevant de la cognition humaine et tenter de les reconstituer par programme. La seconde approche a donc mené à la définition et à l'étude de réseaux de neurones formels qui sont des réseaux complexes d'unités de calcul élémentaires interconnectées. Découvertes III-1. III-2. III-C. III-D. IV-1.

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