content.raw: 🇺🇸 🇨🇦 🇬🇧 Nobel Prize winners against USSR & Japanese AI pioneers  ☭🇯🇵 
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content.value: 🇺🇸 🇨🇦 🇬🇧 Nobel Prize winners against USSR & Japanese AI pioneers  ☭🇯🇵 
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content.raw: 🇩🇪 Prof. Jürgen Schmidhuber:  “The #NobelPrize in Physics 2024 for Hopfield & Hinton turns out to be a Nobel Prize for plagiarism. They republished methodologies developed in #Ukraine and #Japan by Ivakhnenko and Amari in the 1960s & 1970s, as well as other techniques, without citing the original inventors.”
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content.value: 🇩🇪 Prof. Jürgen Schmidhuber:  “The #NobelPrize in Physics 2024 for Hopfield & Hinton turns out to be a Nobel Prize for plagiarism. They republished methodologies developed in #Ukraine and #Japan by Ivakhnenko and Amari in the 1960s & 1970s, as well as other techniques, without citing the original inventors.”
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content.raw: 1965 - First Deep Learning -  USSR ☭ (Ukraine 🇺🇦 now)
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content.value: 1965 - First Deep Learning -  USSR ☭ (Ukraine 🇺🇦 now)
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content.raw: Ivakhnenko and Lapa introduced the first deep learning in deep MLPs that learn internal representations of input data.
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content.value: Ivakhnenko and Lapa introduced the first deep learning in deep MLPs that learn internal representations of input data.
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content.raw: 1967/68 - Deep Learning by Stochastic Gradient Descent - Japan 🇯🇵
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content.value: 1967/68 - Deep Learning by Stochastic Gradient Descent - Japan 🇯🇵
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content.raw: Shun-Ichi Amari trained MLPs with many layers in non-incremental end-to-end fashion from scratch by stochastic gradient descent (SGD).
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content.value: Shun-Ichi Amari trained MLPs with many layers in non-incremental end-to-end fashion from scratch by stochastic gradient descent (SGD).
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content.raw: 1969 - Rectified linear unit - Japan 🇯🇵
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content.value: 1969 - Rectified linear unit - Japan 🇯🇵
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content.raw: In 1969, Kunihiko Fukushima introduced ReLU in the context of visual feature extraction in hierarchical neural networks.
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content.value: In 1969, Kunihiko Fukushima introduced ReLU in the context of visual feature extraction in hierarchical neural networks.
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content.raw: 1970 - Backpropagation - Finland 🇫🇮 😃
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content.value: 1970 - Backpropagation - Finland 🇫🇮 😃
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content.raw: In 1970, Seppo Linnainmaa was the first the reverse mode of automatic differentiation, now known as backpropagation.
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content.value: In 1970, Seppo Linnainmaa was the first the reverse mode of automatic differentiation, now known as backpropagation.
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content.raw: 1972 - Recurrent Neural Network - Japan 🇯🇵
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content.value: 1972 - Recurrent Neural Network - Japan 🇯🇵
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content.raw: In 1972, Shun-Ichi Amari published a learning recurrent neural network based on Lenz-Ising model (Amari's net was later called the "Hopfield network". Hopfield republished in 1982, without citing Amari papers.)
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content.value: In 1972, Shun-Ichi Amari published a learning recurrent neural network based on Lenz-Ising model (Amari's net was later called the "Hopfield network". Hopfield republished in 1982, without citing Amari papers.)
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content.raw: 1979 - First Convolutional neural network - Japan 🇯🇵
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content.value: 1979 - First Convolutional neural network - Japan 🇯🇵
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content.raw: CNN architecture was introduced in 1979 by Kunihiko Fukushima, also known as Neocognitron.
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content.value: CNN architecture was introduced in 1979 by Kunihiko Fukushima, also known as Neocognitron.
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content.href: https://people.idsia.ch/~juergen/deep-learning-history.html#AMH2
content.raw: https://people.idsia.ch/~juergen/deep-learning-history.html#AMH2
content.type: link
content.type: text
rawContent: 🇺🇸 🇨🇦 🇬🇧 Nobel Prize winners against USSR & Japanese AI pioneers  ☭🇯🇵 
  
  🇩🇪 Prof. Jürgen Schmidhuber:  “The #NobelPrize in Physics 2024 for Hopfield & Hinton turns out to be a Nobel Prize for plagiarism. They republished methodologies developed in #Ukraine and #Japan by Ivakhnenko and Amari in the 1960s & 1970s, as well as other techniques, without citing the original inventors.”
  
  1965 - First Deep Learning -  USSR ☭ (Ukraine 🇺🇦 now)
  Ivakhnenko and Lapa introduced the first deep learning in deep MLPs that learn internal representations of input data.
  
  1967/68 - Deep Learning by Stochastic Gradient Descent - Japan 🇯🇵
  Shun-Ichi Amari trained MLPs with many layers in non-incremental end-to-end fashion from scratch by stochastic gradient descent (SGD).
  
  1969 - Rectified linear unit - Japan 🇯🇵
  In 1969, Kunihiko Fukushima introduced ReLU in the context of visual feature extraction in hierarchical neural networks.
  
  1970 - Backpropagation - Finland 🇫🇮 😃
  In 1970, Seppo Linnainmaa was the first the reverse mode of automatic differentiation, now known as backpropagation.
  
  1972 - Recurrent Neural Network - Japan 🇯🇵
  In 1972, Shun-Ichi Amari published a learning recurrent neural network based on Lenz-Ising model (Amari's net was later called the "Hopfield network". Hopfield republished in 1982, without citing Amari papers.)
  
  1979 - First Convolutional neural network - Japan 🇯🇵
  CNN architecture was introduced in 1979 by Kunihiko Fukushima, also known as Neocognitron.
  
  https://people.idsia.ch/~juergen/deep-learning-history.html#AMH2 
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