A Short History of Artificial Intelligence

Editor's note: For many years, AI developments crept along at a snail’s pace. It sometimes felt like we’d never move beyond the era of the AOL SmarterChild chatbot. And then, everything changed. In a little over half a decade, we’ve undergone a century’s worth of innovation. 

In this post, Anna-Sofia Lesiv explores the major turning points that led us to this moment. Regardless of whether you’re an AI super fan watching ChatGPT’s every move, or a reluctant luddite wondering what the hell a “transformer” is, this essay is worth a read.

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With recent advances in machine learning, we could be entering a period of technological progression more impactful than the Scientific Revolution and the Industrial Revolution combined. The development of transformer architectures and massive deep learning models trained on thousands of GPUs has led to the emergence of smart, complex programs capable of understanding and producing language in a manner indistinguishable from humans.

The notion that text is a universal interface that can encode all human knowledge and be manipulated by machines has captivated mathematicians and computer scientists for decades. The massive language models that have emerged in the previous handful of years alone are now proof that these thinkers were onto something deep. Models like GPT-4 are already capable of not only writing creatively but coding, playing chess, and answering complex queries.

The success of these models, and their rapid improvement through incremental scaling and training, suggests that the learning architectures available today may soon be sufficient to bring about a general artificial intelligence. It could be that new models will be required to produce artificial general intelligence (AGI), but if existing models are on the right track, the path to artificial general intelligence could instead amount to an economics problem—what does it take to get the money and energy necessary to train a sufficiently large model?