8 results ·
● Live web index
C
cs.stanford.edu
research
https://cs.stanford.edu/people/eroberts/courses/soco/projects/neural-networks…
| The Artificial Neuron History Comparison Architecture Applications Future Sources | Neural Network Header **History: The 1940's to the 1970's** In 1943, neurophysiologist Warren McCulloch and mathematician Walter Pitts wrote a paper on how neurons might work. In order to describe how neurons in the brain might work, they modeled a simple neural network using electrical circuits. MADALINE was the first neural network applied to a real world problem, using an adaptive filter that eliminates echoes on phone lines. It is based on the idea that while one active perceptron may have a big error, one can adjust the weight values to distribute it across the network, or at least to adjacent perceptrons. Despite the later success of the neural network, traditional von Neumann architecture took over the computing scene, and neural research was left behind. In the same time period, a paper was written that suggested there could not be an extension from the single layered neural network to a multiple layered neural network.
F
faculty.washington.edu
research
https://faculty.washington.edu/seattle/Neural-Nets/History.html
| Neural Nets - History and People |. | Pioneers of artificial neural networks Warren McCulloch and Walter Pitts, Marvin Minsky, Paul Werbos and John Hopfield |. Pioneers of artificial neural networks. | Should it be surprising that Alan Turing foresaw the development of artificial neural networks? In 1943 Warren McCulloch and Walter Pitts published the pioneering article "A Logical Calculus of Ideas Immanent in Nervous Activity" in which they proposed the first artificial neural network based solely on algorithms and mathematics. In 1951 Marvin Minsky built the SNARC, the first neural network simulator. In 1969, Minsky and Seymour Papert (the inventor of LOGO) published the book Perceptrons, which laid the foundations for modern artificial neural nets. He pioneered the concept of training a neural net by changing the strength of its synaptic signals. In 1974, Paul Werbos introduced the back propagation algorithm, which allows errors in a neural network to propagate back to previous neuron layers. In 1982, John Hopfield invented the associative neural network--called the Hopfield net.
P
pub.towardsai.net
article
https://pub.towardsai.net/a-brief-history-of-neural-nets-472107bc2c9c
They developed a simple neural network using electrical circuits to show how neurons in the brain might work. * **1958:**_Frank Rosenblatt_ develops the _perceptron_ (single-layer neural network) inspired by the way neurons work in the brain. * **1982:**_John_ _Hopfield_ develops the Hopfield Network, a recurrent Neural Net, which describes relationships between binary (firing or not-firing) neurons. * **1998:**_LeNet_-5 — a Convolutional Neural Network was developed by _Yann_ _LeCun et al.._ Convolutional Neural Nets are especially suited for image data. * **2006:**_Geoffrey Hinton_ creates the _Deep Belief Network_, a generative model. * **2009:**_Ruslan Salakhutdinov_ and _Geoffrey Hinton_ present _Deep Boltzmann Machine_, a generative model similar to a Deep Belief Network, but allowing bidirectional in the bottom layer. The U-Net consists of a encoder convolutional network connected with a decoder network to upsample the image. * **2020**: _OpenAI_ publishes Generative Pre-trained Transformer 3 (GPT-3), a deep learning model to produce human-like text. Image 24: AI Agents: Complete Course.
E
en.wikipedia.org
article
https://en.wikipedia.org/wiki/History_of_artificial_neural_networks
* [(Top)](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#). * [3.1 LSTM](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#LSTM). * [5 Deep learning](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#Deep_learning). * [7.2 Transformer](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#Transformer). * [8.3 Deep learning](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#Deep_learning_2). * [11 Notes](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#Notes). * [Read](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks). * [Read](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks). popularized backpropagation.[[31]](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_note-32). They reported up to 70 times faster training.[[85]](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_note-86). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-fukuneoscholar_61-0)**Fukushima, K. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-wz1988_68-0)**Zhang, Wei (1988). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-wz1990_69-0)**Zhang, Wei (1990). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-70)**Fukushima, Kunihiko; Miyake, Sei (1982-01-01). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-LECUN1989_71-0)**LeCun _et al._, "Backpropagation Applied to Handwritten Zip Code Recognition," _Neural Computation_, 1, pp. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-73)**Zhang, Wei (1991). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-74)**Zhang, Wei (1994). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-Weng1992_75-0)**J. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-Weng19932_76-0)**J. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-Weng1997_77-0)**J. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-81)**Sven Behnke (2003). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-:62_88-0)**Ciresan, D. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-:9_91-0)**Ciresan, D.; Meier, U.; Schmidhuber, J. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-szegedy_94-0)**Szegedy, Christian (2015). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-101)**Linn, Allison (2015-12-10). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-olli2010_106-0)**Niemitalo, Olli (February 24, 2010). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-108)**Gutmann, Michael; Hyvärinen, Aapo. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-Cherry_1953_115-0)**Cherry EC (1953). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-118)**Fukushima, Kunihiko (1987-12-01). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-:12_121-0)**Soydaner, Derya (August 2022). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-122)**Giles, C. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-123)**Feldman, J. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-125)**Schmidhuber, Jürgen (January 1992). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-135)**Levy, Steven. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-138)**Kohonen, Teuvo (1982). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-139)**Von der Malsburg, C (1973). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-141)**Smolensky, Paul (1986). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-144)**Sejnowski, Terrence J. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-hinton2006_146-0)**[Hinton, G. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-hinton2009_147-0)**Hinton, Geoffrey (2009-05-31). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-149)**Watkin, Timothy L. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-150)**Schwarze, H; Hertz, J (1992-10-15). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-151)**Mato, G; Parga, N (1992-10-07). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-schmidhuber19922_153-0)**Schmidhuber, Jürgen (1992). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-154)**Hanson, Stephen; Pratt, Lorien (1988). **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-157)**Yang, J. **[^](https://en.wikipedia.org/wiki/History_of_artificial_neural_networks#cite_ref-158)**Strukov, D.
Y
youtube.com
video
https://www.youtube.com/watch?v=AA2ettRM6_Q
Neural Networks Explained: From 1943 Origins to Deep Learning Revolution 🚀 | AI History & Evolution
The AI Guy
1400 subscribers
258 likes
10587 views
10 Jun 2024
Discover the fascinating history of neural networks, from their origins in 1943 to the groundbreaking deep learning advancements of today. Learn how pioneering scientists like Warren McCulloch, Walter Pitts, Frank Rosenblatt, John Hopfield, Geoffrey Hinton, and others contributed to this revolutionary field. Understand key developments like the perceptron, backpropagation, and the role of GPUs in transforming AI. Join us on this journey through time to see how neural networks have evolved to shape modern machine learning and artificial intelligence. 🚀 #NeuralNetworks #DeepLearning #AIHistory #MachineLearning #ArtificialIntelligence
9 comments
D
dataversity.net
article
https://www.dataversity.net/articles/a-brief-history-of-neural-networks/
Deep learning uses neural networks, a data structure design loosely inspired by the layout of biological neurons. (It should be noted, Rosenblatt’s primary goal was not to build a computer that could recognize and classify images, but to gain insights about how the human brain worked.) The Perceptron neural network was originally programmed with two layers, the input layer and the output layer. This was the first design of a deep learning model using a convolutional neural network. The early designs of neural networks (such as the Perceptron) did not include hidden layers, but two obvious ones (input/output). In 1989, deep learning became an actuality when Yann LeCun, et al., experimented with the standard backpropagation algorithm (created in 1970), applying it to a neural network. In 2009, Nvidia supported the “big bang of deep learning.” At this time, many successful deep learning neural networks received training using Nvidia GPUs. GPUs have become remarkably important in machine learning. Deep learning algorithms are supported by neural networks.
M
medium.com
article
https://medium.com/@wsmaisys/from-biology-to-brilliance-a-brief-history-of-ar…
# 🧠 From Biology to Brilliance: A Brief History of Artificial Neural Networks | by Waseem M Ansari | Medium. Image 2: Waseem M Ansari. > **_Reference_**_:__“The Neuron Doctrine, 1891–1951” — Shepherd, G.M. This model marked the beginning of **connectionism** in AI — the idea that simple units (neurons) could be connected to simulate brain-like learning. This led to what is known as the **AI Winter**, a period of declining interest and funding in neural network research. Image 8: Waseem M Ansari. Image 9: Waseem M Ansari. Image 11: The Hidden Geometry of Language That Powers AI: The NLP Guide🚀. Image 12: Waseem M Ansari. Image 13: The Breakthrough That Taught Machines to See: A Non-Technical Guide to AI Vision 👀⚡. Image 14: Waseem M Ansari. ## The Breakthrough That Taught Machines to See: A Non-Technical Guide to AI Vision 👀⚡ ### How computers see and understand images with unprecedented accuracy. Image 16: Waseem M Ansari. Image 17: If You Understand These 5 AI Terms, You’re Ahead of 90% of People.
N
news.mit.edu
research
https://news.mit.edu/2017/explained-neural-networks-deep-learning-0414
Deep learning is in fact a new name for an approach to artificial intelligence called neural networks, which have been going in and out of fashion for more than 70 years. Neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952 as founding members of what’s sometimes called the first cognitive science department. Neural nets were a major area of research in both neuroscience and computer science until 1969, when, according to computer science lore, they were killed off by the MIT mathematicians Marvin Minsky and Seymour Papert, who a year later would become co-directors of the new MIT Artificial Intelligence Laboratory. By the 1980s, however, researchers had developed algorithms for modifying neural nets’ weights and thresholds that were efficient enough for networks with more than one layer, removing many of the limitations identified by Minsky and Papert.