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Neural Engineering Object : Nengo is built upon two theoretic underpinnings, the Neural Engineering Framework (NEF) and the Semantic Pointer Architecture (SPA). |
Neural Engineering Object : Notable developments accomplished using the Nengo software have occurred in many fields, and Nengo has been used and cited in over 100 publications. An important development to note is Spaun, a network of 6.6 million artificial spiking neurons (a small number compared to the number in the hu... |
Neural Engineering Object : Eliasmith, Chris (2013). How To Build A Brain. Oxford University Press. ISBN 978-0199794546. |
Neuroph : Neuroph is an object-oriented artificial neural network framework written in Java. It can be used to create and train neural networks in Java programs. Neuroph provides Java class library as well as GUI tool easyNeurons for creating and training neural networks. It is an open-source project hosted at SourceFo... |
Neuroph : Neuroph's core classes correspond to basic neural network concepts like artificial neuron, neuron layer, neuron connections, weight, transfer function, input function, learning rule etc. Neuroph supports common neural network architectures such as Multilayer perceptron with Backpropagation, Kohonen and Hopfie... |
Neuroph : Comparison of deep learning software Neural network SOM or Kohonen Retropropagation |
Neuroph : Neuroph Homepage |
NeuroSolutions : 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, the Levenberg-Marquardt algorithm, and back-propagati... |
NeuroSolutions : NeuroSolutions provides three separate wizards for automatically building neural network models: |
NeuroSolutions : NeuroDimension, Inc. provides three ways for NeuroSolutions to deploy a custom neural network solution for applications: code generation, DLL generation, and OLE generation. |
NeuroSolutions : Machine learning == References == |
OpenNN : OpenNN (Open Neural Networks Library) is a software library written in the C++ programming language which implements neural networks, a main area of deep learning research. The library is open-source, licensed under the GNU Lesser General Public License. |
OpenNN : The software implements any number of layers of non-linear processing units for supervised learning. This deep architecture allows the design of neural networks with universal approximation properties. Additionally, it allows multiprocessing programming by means of OpenMP, in order to increase computer perform... |
OpenNN : The development started in 2003 at the International Center for Numerical Methods in Engineering, within the research project funded by the European Union called RAMFLOOD (Risk Assessment and Management of FLOODs). Then it continued as part of similar projects. OpenNN is being developed by the startup company ... |
OpenNN : OpenNN is a general purpose artificial intelligence software package. It uses machine learning techniques for solving predictive analytics tasks in different fields. For instance, the library has been applied in the engineering, energy, or chemistry sectors. |
OpenNN : Comparison of deep learning software Neural Designer, also developed by Artelnics Artificial intelligence Machine learning Deep learning Artificial neural network == References == |
Peltarion Synapse : Synapse is a component-based development environment for neural networks and adaptive systems. Created by Peltarion, Synapse allows data mining, statistical analysis, visualization, preprocessing, design and training of neural networks and adaptive systems and the deployment of them. It utilizes a p... |
Peltarion Synapse : Due to its plug in-based design, the usage of Synapse can be very general. Synapse is based on the Microsoft .NET framework and all Synapse components are also .NET components. Although Peltarion has yet to release an official API for the Synapse platform, user made components are emerging, some of ... |
Peltarion Synapse : The development cycle in Synapse is based on the canonical data mining cycle. A notable difference however is that in Synapse that cycle is not linear, but supports an iterative approach where the user can freely move between the steps. Synapse features four different operating modes that make up th... |
Peltarion Synapse : Artificial neural network Neural network software Peltarion |
Peltarion Synapse : Levity, Peltarion shut down on September 30, 2022 purchased by King. |
PSIPRED : PSI-blast based secondary structure PREDiction (PSIPRED) is a method used to investigate protein structure. It uses artificial neural network machine learning methods in its algorithm. It is a server-side program, featuring a website serving as a front-end interface, which can predict a protein's secondary st... |
PSIPRED : Secondary structure is the general three-dimensional form of local segments of biopolymers such as proteins and nucleic acids (DNA, RNA). It does not, however, describe specific atomic positions in three-dimensional space, which are considered to be the tertiary structure. Secondary structure can be formally ... |
PSIPRED : The idea of this method is to use the information of the evolutionarily related proteins to predict the secondary structure of a new amino acid sequence. PSIBLAST is used to find related sequences and to build a position-specific scoring matrix. This matrix is processed by an artificial neural network, which ... |
PSIPRED : The prediction method or algorithm is split into three stages: generating a sequence profile, predicting initial secondary structure, and filtering the predicted structure. PSIPRED works to normalize the sequence profile generated by PSIBLAST. Then, by using neural networking, initial secondary structure is p... |
PSIPRED : Jpred Protein design Protein function prediction De novo protein structure prediction Molecular design software List of protein structure prediction software Comparison of software for molecular mechanics modeling Modelling biological systems Protein fragment library Lattice proteins Statistical potential == ... |
SNNS : SNNS (Stuttgart Neural Network Simulator) is a neural network simulator originally developed at the University of Stuttgart. While it was originally built for X11 under Unix, there are Windows ports. Its successor JavaNNS never reached the same popularity. |
SNNS : SNNS is written around a simulation kernel to which user written activation functions, learning procedures and output functions can be added. It has support for arbitrary network topologies and the standard release contains support for a number of standard neural network architectures and training algorithms. |
SNNS : There is currently no ongoing active development of SNNS. In July 2008 the license was changed to the GNU LGPL. |
SNNS : Artificial neural network Neural network software |
SNNS : SNNS homepage Patches with bugfixes and a Python interface to the SNNS kernel |
Synthetic nervous system : Synthetic Nervous System (SNS) is a computational neuroscience model that may be developed with the Functional Subnetwork Approach (FSA) to create biologically plausible models of circuits in a nervous system. The FSA enables the direct analytical tuning of dynamical networks that perform spe... |
Synthetic nervous system : SNSs share some features with machine learning networks like Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). All of these networks are composed of neurons and synapses inspired in some way by biological nervous systems. These compone... |
Synthetic nervous system : Functional Subnetworks are the building blocks of SNSs. They are composed of neurons and synapses modeled from the equations described above as well as other neuroscience models. When tuned properly, as shown in the following section, they are capable of performing mathematical calculations a... |
Synthetic nervous system : The leaky-integrator model above can be converted into a tuning-friendly equation by normalizing the membrane potential to read 0 when at rest ( U = V − E r ) and by introducing the R parameter. R is the potential operating range of the graded chemical synapse and is equal to E h i − E l o... |
Neural Network Exchange Format : Neural Network Exchange Format (NNEF) is an artificial neural network data exchange format developed by the Khronos Group. It is intended to reduce machine learning deployment fragmentation by enabling a rich mix of neural network training tools and inference engines to be used by appli... |
Neural Network Exchange Format : NNEF was proposed in 2015 by member companies of the Khronos Group as a device and implementation independent transfer format capable of describing any artificial neural net in terms of its structure, operations and data. The first version of the standard was launched in provisional for... |
Neural Network Exchange Format : The goal of NNEF is to enable data scientists and engineers to easily transfer trained networks from their chosen training framework into a wide variety of inference engines. NNEF encapsulates a complete description of the structure, operations and parameters of a trained neural network... |
Neural Network Exchange Format : NNEF is maintained by the Khronos Group under its Open Governance Principles as follows: Any company is invited and able to join Khronos to contribute to and influence the development of its specifications; Finalized specifications are publicly and freely distributed at zero cost from t... |
Neural Network Exchange Format : NNEF 1.0 Provisional, Released 20 December 2017. NNEF 1.0, Released 13 August 2018 NNEF 1.0.1, Released 10 May 2019 NNEF 1.0.2, Released 13 July 2019 |
Neural Network Exchange Format : The following Khronos members have participated in the NNEF working group: |
Neural Network Exchange Format : The NNEF tools project on GitHub contains the following open source tools: File format Parser Bidirectional converters between NNEF and ONNX, Caffe, Caffe2, TensorFlow (python), TensorFlow (protobuf) Model zoo: reference collection of models converted to NNEF |
Neural Network Exchange Format : Open Neural Network Exchange == References == |
Open Neural Network Exchange : The Open Neural Network Exchange (ONNX) [ˈɒnɪks] is an open-source artificial intelligence ecosystem of technology companies and research organizations that establish open standards for representing machine learning algorithms and software tools to promote innovation and collaboration in ... |
Open Neural Network Exchange : ONNX was originally named Toffee and was developed by the PyTorch team at Facebook. In September 2017 it was renamed to ONNX and announced by Facebook and Microsoft. Later, IBM, Huawei, Intel, AMD, Arm and Qualcomm announced support for the initiative. In October 2017, Microsoft announced... |
Open Neural Network Exchange : ONNX provides definitions of an extensible computation graph model, built-in operators and standard data types, focused on inferencing (evaluation). Each computation dataflow graph is a list of nodes that form an acyclic graph. Nodes have inputs and outputs. Each node is a call to an oper... |
Open Neural Network Exchange : Neural Network Exchange Format Comparison of deep learning software Predictive Model Markup Language—an XML-based predictive model interchange format PicklingTools—an open-source collection of tools for allowing C++ and Python systems to share information quickly and easily. |
Open Neural Network Exchange : Boyd, Eric (2017-09-07). "Microsoft and Facebook create open ecosystem for AI model interoperability – Microsoft Cognitive Toolkit". Microsoft Cognitive Toolkit. Retrieved 2017-10-11. onnx: Open Neural Network Exchange, Open Neural Network Exchange, 2017-10-11, retrieved 2017-10-11 |
Evolutionary acquisition of neural topologies : 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 t... |
Evolutionary acquisition of neural topologies : Despite sharing these two properties, the method has the following important features which distinguish it from previous works in neuroevolution. It introduces a genetic encoding called common genetic encoding (CGE) that handles both direct and indirect encoding of neural... |
Evolutionary acquisition of neural topologies : EANT has been tested on some benchmark problems such as the double-pole balancing problem, and the RoboCup keepaway benchmark. In all the tests, EANT was found to perform very well. Moreover, a newer version of EANT, called EANT2, was tested on a visual servoing task and ... |
HyperNEAT : Hypercube-based NEAT, or HyperNEAT, is a generative encoding that evolves artificial neural networks (ANNs) with the principles of the widely used NeuroEvolution of Augmented Topologies (NEAT) algorithm developed by Kenneth Stanley. It is a novel technique for evolving large-scale neural networks using the ... |
HyperNEAT : Multi-agent learning Checkers board evaluation Controlling Legged Robotsvideo Comparing Generative vs. Direct Encodings Investigating the Evolution of Modular Neural Networks Evolving Objects that can be 3D-printed Evolving the Neural Geometry and Plasticity of an ANN |
HyperNEAT : HyperNEAT Users Page Ken Stanley's website "Evolutionary Complexity Research Group at UCF" NEAT Project Homepage PicBreeder.org Archived 2021-04-17 at the Wayback Machine EndlessForms.com Archived 2018-11-14 at the Wayback Machine BEACON Blog: What is neuroevolution? |
Neuroevolution : Neuroevolution, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, and rules. It is most commonly applied in artificial life, general game playing and evolutionary robotics. The main benefit is that neuroe... |
Neuroevolution : Many neuroevolution algorithms have been defined. One common distinction is between algorithms that evolve only the strength of the connection weights for a fixed network topology (sometimes called conventional neuroevolution), and algorithms that evolve both the topology of the network and its weights... |
Neuroevolution : Most neural networks use gradient descent rather than neuroevolution. However, around 2017 researchers at Uber stated they had found that simple structural neuroevolution algorithms were competitive with sophisticated modern industry-standard gradient-descent deep learning algorithms, in part because n... |
Neuroevolution : Evolutionary algorithms operate on a population of genotypes (also referred to as genomes). In neuroevolution, a genotype is mapped to a neural network phenotype that is evaluated on some task to derive its fitness. In direct encoding schemes the genotype directly maps to the phenotype. That is, every ... |
Neuroevolution : Examples of neuroevolution methods (those with direct encodings are necessarily non-embryogenic): |
Neuroevolution : Automated machine learning (AutoML) Evolutionary computation NeuroEvolution of Augmenting Topologies (NEAT) HyperNEAT (A Generative version of NEAT) Evolutionary Acquisition of Neural Topologies (EANT/EANT2) |
Neuroevolution : "Evolution 101: Neuroevolution | BEACON". beacon-center.org. Retrieved 2018-01-14. "NNRG Areas - Neuroevolution". nn.cs.utexas.edu. University of Texas. Retrieved 2018-01-14. (has downloadable papers on NEAT and applications) "SharpNEAT Neuroevolution Framework". sharpneat.sourceforge.net. Retrieved 20... |
Neuroevolution of augmenting topologies : NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Kenneth Stanley and Risto Miikkulainen in 2002 while at The University of Texas at Austin. It alters bo... |
Neuroevolution of augmenting topologies : On simple control tasks, the NEAT algorithm often arrives at effective networks more quickly than other contemporary neuro-evolutionary techniques and reinforcement learning methods, as of 2006. |
Neuroevolution of augmenting topologies : Traditionally, a neural network topology is chosen by a human experimenter, and effective connection weight values are learned through a training procedure. This yields a situation whereby a trial and error process may be necessary in order to determine an appropriate topology.... |
Neuroevolution of augmenting topologies : The original implementation by Ken Stanley is published under the GPL. It integrates with Guile, a GNU scheme interpreter. This implementation of NEAT is considered the conventional basic starting point for implementations of the NEAT algorithm. |
Neuroevolution of augmenting topologies : Evolutionary acquisition of neural topologies |
Neuroevolution of augmenting topologies : Stanley's original, mtNEAT and rtNEAT for C++ ECJ, JNEAT, NEAT 4J, ANJI for Java SharpNEAT for C# MultiNEAT (MultiNEAT at the Wayback Machine (archived 2021-05-15)) and mtNEAT for C++ and Python neat-python for Python NeuralFit (not an exact implementation) and neat-python for ... |
Neuroevolution of augmenting topologies : NEAT Homepage (NEAT Homepage at the Wayback Machine (archived 2023-12-05)) "Evolutionary Complexity Research Group at UCF" - Ken Stanley's current research group NERO: Neuro-Evolving Robotic Operatives - an example application of rtNEAT GAR: Galactic Arms Race - an example appl... |
Deep Learning (South Park) : "Deep Learning" is the fourth episode of the twenty-sixth season of the American animated television series South Park, and the 323rd episode of the series overall. Written and directed by Trey Parker, it premiered on March 8, 2023. The episode, which parodies the use of the artificial inte... |
Deep Learning (South Park) : When fourth-grader Bebe Stevens extols the romantic texts written to her by Clyde Donovan, classmate Wendy Testaburger complains to her boyfriend, Stan Marsh, that his replies to her messages consist of merely a thumbs up. Clyde tells Stan about ChatGPT, an AI-based app he uses to write the... |
Deep Learning (South Park) : Bubbleblabber contributor John Schwarz rated the episode a 7.5 out of 10, stating in his review, "One day we're going to look back on this episode like we do when we think of the many chimps that we've sent to outer space when testing space flight capabilities and marvel at how far we've co... |
Deep Learning (South Park) : "Deep Learning" Full Episode at South Park Studios "Deep Learning" at IMDb Vainilavičius, Justinas (March 8, 2023). "ChatGPT, dude: viral chatbot makes it to 'South Park'". Cybernews. Archived from the original on March 8, 2023. |
ChatGPT in education : The usage of ChatGPT in education has sparked considerable debate and exploration. ChatGPT is a chatbot based on large language models (LLMs) that was released by OpenAI in November 2022. Educators' opinions vary widely; while some are skeptical about the benefits, many see them as valuable tools... |
ChatGPT in education : ChatGPT is a virtual assistant developed by OpenAI and launched in November 2022. It uses advanced artificial intelligence (AI) models called generative pre-trained transformers (GPT), such as GPT-4o, to generate text. GPT models are large language models that are pre-trained to predict the next ... |
ChatGPT in education : In a January 2023 assessment, ChatGPT demonstrated performance comparable to graduate-level standards at institutions such as the University of Minnesota and Wharton School. A blind study conducted at the University of Wollongong Law School compared GPT-3.5 and GPT-4 with 225 students in an end-o... |
ChatGPT in education : The impact of ChatGPT on education, especially in English studies, is a topic of significant discussion. Daniel Herman's perspective reflects concerns about the potential devaluation of writing skills if AI can generate text as easily as humans. Similarly, Naomi S. Baron wrote that "If AI text ge... |
ChatGPT in education : Some companies have responded to the influx of ChatGPT and generative AI among students by developing detection software which flags down essays likely written by AI. Among the first companies to develop solutions like this was Turnitin, which developed a tool to detect AI-based academic dishones... |
ChatGPT in education : AI in education Educational technology |
ChatGPT in education : ChatGPT Education | OpenAI |
GPT-3 : Generative Pre-trained Transformer 3 (GPT-3) is a large language model released by OpenAI in 2020. Like its predecessor, GPT-2, it is a decoder-only transformer model of deep neural network, which supersedes recurrence and convolution-based architectures with a technique known as "attention". This attention mec... |
GPT-3 : According to The Economist, improved algorithms, more powerful computers, and a recent increase in the amount of digitized material have fueled a revolution in machine learning. New techniques in the 2010s resulted in "rapid improvements in tasks", including manipulating language. Software models are trained to... |
GPT-3 : On May 28, 2020, an arXiv preprint by a group of 31 engineers and researchers at OpenAI described the achievement and development of GPT-3, a third-generation "state-of-the-art language model". The team increased the capacity of GPT-3 by over two orders of magnitude from that of its predecessor, GPT-2, making G... |
GPT-3 : There are many models in the GPT-3 family, some serving different purposes than others. In the initial research paper published by OpenAI, they mentioned 8 different sizes of the main GPT-3 model: Half of the models are accessible through the API, namely GPT-3-medium, GPT-3-xl, GPT-3-6.7B and GPT-3-175b, which ... |
GPT-3 : Generative Pre-trained Transformer 3.5 (GPT-3.5) is a sub class of GPT-3 Models created by OpenAI in 2022. On March 15, 2022, OpenAI made available new versions of GPT-3 and Codex in its API with edit and insert capabilities under the names "text-davinci-002" and "code-davinci-002". These models were described ... |
GPT-3 : BERT (language model) Hallucination (artificial intelligence) LaMDA Gemini (language model) Wu Dao GPTZero == References == |
Rie Qudan : Rie Qudan or Rie Kudan (born September 27, 1990, in Saitama, Japan) is a Japanese novelist. In 2024, Qudan won the 170th Akutagawa Prize for her novel Tōkyō-to Dōjō Tō ("Tokyo Sympathy Tower"). She stated that about 5% of the novel was written by artificial intelligence. |
Rie Qudan : Qudan was born on September 27, 1990, in Urawa (now Saitama) in Saitama Prefecture, Japan. She won her first writing award for an essay she wrote during sixth grade in elementary school. |
Rie Qudan : Qudan worked as a laboratory assistant after graduation from university. In 2021, Qudan won the 126th Literary World Newcomer Award with her debut novel, Warui Ongaku ("Bad music"). Her 2024 novel Tōkyō-to Dōjō Tō ("Tokyo sympathy tower") is a science fiction story about an architect who designs a tower to ... |
Rie Qudan : Qudan lives in Chiba Prefecture. |
Rie Qudan : Rie Qudan on Twitter (in Japanese) |
2023 Writers Guild of America strike : From May 2 to September 27, 2023, the Writers Guild of America (WGA)—representing 11,500 screenwriters—went on strike over a labor dispute with the Alliance of Motion Picture and Television Producers (AMPTP). Lasting 148 days, the strike is tied with the 1960 strike as the second-... |
2023 Writers Guild of America strike : One of the main focus points in the labor dispute is the residuals from streaming media; the WGA claims that AMPTP's share of such residuals has cut much of the writers' average incomes compared to a decade ago. Writers also wanted artificial intelligence, such as ChatGPT, to be u... |
2023 Writers Guild of America strike : The WGA announced the members of its negotiating committee in November 2022, with David Young as chief negotiator. In February 2023, Ellen Stutzman took over as the chief negotiator of the WGA. |
2023 Writers Guild of America strike : Many films, television programs, and podcasts have been affected by the strike; some have continued production without writers, while others have been paused or completely shut down. The projects that have been unaffected either were already written before May 2, are largely unscr... |
2023 Writers Guild of America strike : 2023 SAG-AFTRA strike List of Hollywood strikes 1960 Writers Guild of America strike 1981 Writers Guild of America strike 1988 Writers Guild of America strike 2007–08 Writers Guild of America strike Impact of the COVID-19 pandemic on television in the United States |
2023 Writers Guild of America strike : WGA Official Strike Website Frank, Jason P. (May 1, 2023). "The 2023 WGA Strike for Dummies". Vulture. Vox Media. Archived from the original on May 2, 2023. Media coverage from Deadline, May 23, 2023 Media coverage from The Hollywood Reporter; Media coverage from Variety, April 24... |
Sam Altman : Samuel Harris Altman (born April 22, 1985) is an American technology entrepreneur and investor best known as the chief executive officer of OpenAI since 2019 (he was briefly dismissed and reinstated in November 2023). He is also the chairman of clean energy companies Oklo Inc. and Helion Energy. Altman is ... |
Sam Altman : Altman was born on April 22, 1985, in Chicago, Illinois, into a Jewish family, and grew up in St. Louis, Missouri. His mother is a dermatologist, and his father was a real estate broker. Altman is the eldest of four siblings. At the age of eight, he received his first computer, an Apple Macintosh, and bega... |
Sam Altman : In 2017, Altman received an honorary Doctor of Engineering degree from the University of Waterloo in Canada for supporting companies through its Velocity entrepreneurship program. The government of Indonesia issued the country's first "golden visa", a 10-year border pass, to Altman in September 2023. In 20... |
Sam Altman : Altman has been a vegetarian since childhood. Altman is openly gay. He disclosed his sexuality at the age of 17 in high school, where he spoke out after some students objected to a National Coming Out Day speaker. He dated Loopt co-founder Nick Sivo for nine years. They broke up shortly after the company w... |
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