A Brief History of Generative AI

Generative AI will be the most disruptive technological innovation since the advent of the personal computer and the inception of the Internet with the potential to create 10s of millions of new jobs, permanently alter the way we work, fuel the creator economy, and displace or augment 100s of millions of workers in roles from computer programmers to computer graphics artists, photographers, video editors, digital marketers and yes, even journalists. Even with all the hype around generative AI this year, it’s true power has not yet been seen or felt, in 2023 there will be significant innovations that will begin a revolution that will leave no industry or job function un-impacted in one way or another. Generative AI research can trace its history back to the 1960s. However generative AI began to develop into something similar to its current form in 2006, with the first significant paper in the field, Geoffrey Hinton and his co-author’s “A Fast Learning Algorithm for Deep Belief Nets” which re-introduced Restricted Boltzmann Machines in the context of deep learning (he originally introduced the RBM concept in 1983.) However few innovations took place in the field, until in 2014, with the introduction of GANs by Ian Goodfellow and his colleagues. Generative AI developments in research were made in the following years, most significantly the introduction of the transformer architecture for natural language processing applications, presented in the paper “Attention is all you Need” by Vaswani and colleagues from Google. <a href="https://matthewdwhite.medium.com/a-brief-history-of-generative-ai-cb1837e67106">Learn More</a>