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2014 BCE
Skype has Real Time Translation
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2012 BCE
"Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared view of four research groups"
The research groups of Microsoft, Google, IBM and Hinton´s lab shows results of working on Neural Nets -
2012 BCE
"ImageNet Classification with deep convolutional neural networks"
By Hinton, Krizhevsky and Sutskever. They create an entry to the ILSVRC (Large Scale Visual Recognition Competition). It was the climax of deep learning ascent. -
2011 BCE
Advances on Google's and Android's speech recognition system
Navdeep Jaitly works on Google's speech recognition system. It pushed Android's speech recognition algorithm -
2011 BCE
Google Brain is created.
Andrew Ng and Jeff Dean create Google Brain to make experiments with neural nets using a great number of CPU cores. -
2009 BCE
"Large Scale Deep Unsupervised Learning using Graphic Processors"
By Raina, Madhavan, Ng. It suggest taht unsupervised learning on speech recognition is 70 times faster using GPUs. -
2007 BCE
"Greedy layer-wise Training of Deep Networks"
By Bengio et al. It has arguments to say that deep machine learning methods are more efficient for dificult problems than shallow methods -
2006 BCE
"A Fast Learning Algorithm for Deep Belief Nets"
By Hinton, Osindero, Whye. A breakthrough significant enough to rekindle interest in neural nets. -
Period: 2006 BCE to 2017 BCE
Deep Learning move have born.
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2002 BCE
"Training products of experts by minimizing contrastive divergence"
By Hinton. It showed that Restricted Boltzmann machine can be trained in an efficient manner. -
1997 BCE
New concept of "Long Short Term Memory" LSTM
Introduced by Schmidhuber and Hochreifer -
1995 BCE
The Helmholtz Machine By Hinton, Dayan, Frey and Neal
This type of machine was born in "The Wake-sleep algorithm unsupervised neural networks" -
1995 BCE
"Learning to play the game of chess"
By Sebatian Thrun. It was a demostration of problems of TD-Gammon (reinforcement learning) approach -
1995 BCE
"Convolutional Networks for Images, Speech, and Time-Series"
By the modern giant of deep learning Yoshua Bengio -
Period: 1995 BCE to 2002 BCE
Second AI Winter
AI Winter began when "Support Vector Machines" appear. -
1993 BCE
Reinforcement Learning
Was treated in the PhD thesis "" Reinforcement learning for robots using neural networks" -
1993 BCE
"A connectionist Approach to Speech Recognition"
By Bengio. It explains the general failure of Recurrent Neural Nets (RNN) -
1992 BCE
Belief Nets appears
Thank to Redford M. Neal in "Connectionist learning of belief networks". Theese nets are like Boltzman Machines but with layers -
1990 BCE
Boom of CNN in handwritten zip code recognition
LeCun's CNN system is used on 10 to 20% of all the checks in U.S -
1989 BCE
"Multilayer feedforward networks are universal approximators"
It Mathematically proved that multilayers allow neural nets to theoretically implement any function -
1989 BCE
"Backpropagation applied to handwritten zip code recognition"
Yann LeCun et al. at AT&T Bell Labs. -
1989 BCE
Early Neural Net applications on Robotics
In CMU Navlab was created "ALVINN: An autonomous land vehicle in a neural network" -
1989 BCE
"Phoneme Recognition using Time Delay Neural Networks"
By Waibel, Hanazawa, Hinton, Shikano, Lang. Speech Recognition close up begins with this article. -
1987 BCE
CIFAR funded Hinton's work
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1986 BCE
Backpropagation Neural Nets return to Popularity
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1986 BCE
"Learning Representations by backpropagation errors"
David Rumelhart, Geoffrey Hinton and Ronald Williams publish this paper that talks about the problems discussed about Perceptrons by Minsky -
1986 BCE
"Weight Sharing" or convolutional neural nets.
It was discussed in the analisys of backpropagation by Rumelhart, Hinton and Williams -
1986 BCE
Autoencoders by Hinton, Rumelhart and Williams
The idea of Autoencoders is discussed in the analysis of backpropagation -
1985 BCE
"A Learning Algorithm for Boltzmann Machine" by Ackley, Hinton, Zejnowski
Boltzmann Machines are networks just like neural nets and have units that are very similar to Perceptrons, theese units are stochastic, it means, they behave according to a probability distribution. -
1982 BCE
Werbos and backpropagation
Paul Werbos publish about using backpropagation in Neural Nets -
1982 BCE
CIFAR is created
Canadian Institute For Advanced Research -
1974 BCE
Backpropagation for Neural Nets
Paul werbos proposes to use backpropagation in neural networks -
1970 BCE
Backpropagation runs on a PC
Seppo Linnainmaa uses Backpropagation on a PC for first time -
1970 BCE
FIRST AI WINTER BEGINS from 70´s to early 80's
NO Funding on research -
Period: 1970 BCE to 1986 BCE
First AI Winter
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1969 BCE
The book titled "Perceptrons" is published
Marvin Minsky and Seymour Papert write "Perceptrons" explaining their nonconformism with Perceptrons in Neural Nets. -
1960 BCE
ADALINE Neuron appear
Bernard Widrow and Tedd Hoff demonstrate that Adaptive Linear Neurons can be implemented in electric circuits using chemical memistors. -
1958 BCE
Perceptron
Frank Rosenblatt's Perceptron Artificial Neuron Model is conceived as a simplified mathematical model that shows how neurons work -
1951 BCE
First Hardware Neural Net Implemented
Marvin Minsky implement the first Hardware NN with SNARC(Stochastic NeuraL Analog Reinforcement Calculator) -
Period: 1950 BCE to 1970 BCE
Beginning
