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Why neural net pioneer Geoffrey Hinton is sounding the alarm on AI

https://mitsloan.mit.edu/ideas-made-to-matter/why-neural-net-pioneer-geoffrey…

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linkedin.com article

Pioneers of Neural Networks: John Hopfield and Geoffrey Hinton

https://www.linkedin.com/pulse/pioneers-neural-networks-john-hopfield-geoffre…

The 2024 Nobel Prize in Physics has been awarded to John Hopfield and Geoffrey Hinton for their groundbreaking contributions to the field of machine learning.

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theconversation.com article

Physics Nobel awarded to neural network pioneers who laid foundations for AI

https://theconversation.com/physics-nobel-awarded-to-neural-network-pioneers-…

# **Physics Nobel awarded to neural network pioneers who laid foundations for AI**. The 2024 Nobel Prize in Physics has been awarded to scientists John Hopfield and Geoffrey Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks”. Inspired by ideas from physics and biology, Hopfield and Hinton developed computer systems that can memorise and learn from patterns in data. However, the Nobel Physics prize was for his work developing Hopfield networks in 1982. These are an extension of the Hopfield network that demonstrated the idea of machine learning – a system that lets a computer learn not from a programmer, but from examples of data. More important was the conceptual breakthrough that an artificial neural network could learn from data. He later published influential papers on backpropagation (the learning process used in modern machine learning systems) and convolutional neural networks (the main type of neural network used today for AI systems that work with image and video data).

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cs.stanford.edu research

Neural Networks - History - CS Stanford

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.

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mckinsey.com article

Deep learning's origins and pioneers - McKinsey

https://www.mckinsey.com/featured-insights/artificial-intelligence/deep-learn…

Bernard Widrow and Ted Hoff at Stanford University, developed a neural-network application by reducing noise in phone lines in the late 1950s.

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wired.com article

An AI Pioneer Explains the Evolution of Neural Networks - WIRED

https://www.wired.com/story/ai-pioneer-explains-evolution-neural-networks/

I'm looking at the brain and saying, “This thing works, and if we want to make something else that works, we should sort of look to it for inspiration.” So this is neuro-inspired, not a neural model. **GH:** So I think what we've learned in the last 10 years is that if you take a system with billions of parameters, and an objective function—like to fill in the gap in a string of words—it works much better than it has any right to. **GH:** If we really understand what's going on, we should be able to make things like education work better. **NT:** A couple of years from now, how do you think we will be using what we've learned about the brain and about how deep learning works to change how education functions?

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