Important People in AI History

Most think AI is a new field, but the concept of "thinking machines" is a notion that's centuries old.

Here are the people who have carried the torch, and their respective contributions to the field of cybernetics AI.

It's important to note that many of the magical elements of AI, particularly around LLMs, were invented in the 80s by people who are still contributing today.

Gottfried Wilhelm Leibniz (1676): Published the chain rule of differential calculus.

Adrien-Marie Legendre (around 1805): Work on linear neural networks.

Johann Carl Friedrich Gauss (around 1795-1805): Contributions to linear neural networks.

Augustin-Louis Cauchy (1847): Proposed the technique of gradient descent.

Leonardo Torres y Quevedo (1914): Built the first working chess end game player.

Ernst Ising and Wilhelm Lenz (1920s): Introduced the Ising model.

Kurt Gödel (1931-34): Fundamental work in computation-based AI.

Donald Hebb (1940s): Proposed Hebbian learning, a theory that has been influential in the development of neural networks and understanding how neurons in the brain adapt during learning.

Alan Turing (1950s): Wrote ideas related to artificial evolution and learning RNNs.

Frank Rosenblatt (1958): Work on multilayer perceptrons (MLPs).

John McCarthy (1956): Coined the term "AI".

Henry J. Kelley (1960): Had a precursor of backpropagation in control theory.

Ray Solomonoff (1964): Foundational work in algorithmic information theory.

Alexey Ivakhnenko & Valentin Lapa (1965): Introduced the first working deep learning algorithm for deep MLPs.

Paul Werbos (1982): Proposed to use the backpropagation method to train NNs.

Seppo Linnainmaa (1970): First published what's known as backpropagation.

Shun-Ichi Amari (1972): Made the Ising recurrent net adaptive.

Kunihiko Fukushima (1979): Introduced the convolutional neural network (CNN) architecture.

David E. Rumelhart, James L. McClelland and the PDP Research Group (1980s): Key figures in the development of the parallel distributed processing framework, which helped to advance the understanding and application of neural networks.

Geoffrey Hinton (1980s-present): Made significant contributions to the development of neural networks and deep learning. He was instrumental in the development of backpropagation for training multi-layer neural networks, and later, his work on deep belief networks in the mid-2000s helped to revive interest in neural networks.

Rodney Brooks (1980s-present): A key figure in robotics, Brooks' work on behavior-based robotics and the subsumption architecture has been influential in the development of autonomous robots.

Richard Sutton and Andrew Barto (1980s-present): Pioneers in the field of reinforcement learning, Sutton and Barto's work has been foundational in developing algorithms for decision-making and learning in machines, which is a key part of AI research.

Yann LeCun (1980s-present): A pioneer in the development of Convolutional Neural Networks (CNNs), especially for image recognition tasks. His work in the late 1980s and 1990s on CNNs laid the foundation for many modern computer vision technologies.

Thomas J. Sejnowski (1980s-present): A computational neuroscientist whose work in neural networks and the computational understanding of the brain has been highly influential.

Leslie Valiant (1980s-present): Proposed the Probably Approximately Correct (PAC) learning framework, which has been influential in the field of computational learning theory.

Yoshua Bengio (1990s-present): Known for his work on artificial neural networks and deep learning, Bengio, along with Hinton and LeCun, is considered one of the "Godfathers of AI." He has made significant contributions to the understanding and advancement of deep learning techniques.

Vladimir Vapnik (1990s-present): Known for his development of the Support Vector Machine (SVM), a popular machine learning algorithm.

Jürgen Schmidhuber (1990s-present): Made substantial contributions to the field of neural networks, particularly in the development of Long Short-Term Memory (LSTM) networks, which are a cornerstone of many modern AI applications, especially in sequence prediction tasks like language modeling and speech recognition.

Andrew Ng (2000s-present): Co-founder of Google Brain, Ng has been influential in applying deep learning techniques to various practical applications. He has played a significant role in popularizing AI through his online courses and as a co-founder of Coursera.

Sebastian Thrun (2000s-present): Known for his work in robotics and autonomous vehicles, including leading the development of Google's self-driving car.

Ian Goodfellow (2010s-present): Known for inventing Generative Adversarial Networks (GANs) in 2014, Goodfellow's work has been pivotal in the field of unsupervised learning and has significant implications for the generation of realistic images, art, and even video.

Demis Hassabis (2010s-present): Co-founder of DeepMind, Hassabis has been a key figure in applying deep learning to games such as Go, demonstrating the potential of AI in complex problem-solving tasks."},"id":"0539c8b9-f850-435f-a9b0-9c3e38722e36","isHidden":false,"type":"markdown"}]