In 1958, the Perceptron, a room-sized computer with a new type of circuitry, was introduced. The U.S. Navy claimed that the Perceptron would lead to machines that could walk, talk, see, write, reproduce itself, and be conscious of its existence. More than six decades later, similar claims are being made about current artificial intelligence (AI).
The field of AI has been running through a boom-and-bust cycle since its early days. The Perceptron, invented by Frank Rosenblatt, arguably laid the foundations for AI. This revolutionary machine was filled with wires that physically connected different components together. Modern-day artificial neural networks that underpin familiar AI like ChatGPT and DALL-E are software versions of the Perceptron, except with substantially more layers, nodes, and connections.
Much like modern-day machine learning, if the Perceptron returned the wrong answer, it would alter its connections so that it could make a better prediction the next time around. Familiar modern AI systems work in much the same way. Using a prediction-based format, large language models (LLMs) are able to produce impressive long-form text-based responses and associate images with text to produce new images based on prompts. These systems get better as they interact more with users.
However, despite some success, humanlike intelligence was nowhere to be found. It quickly became apparent that the AI systems knew nothing about their subject matter. Without the appropriate background and contextual knowledge, it’s nearly impossible to accurately resolve ambiguities present in everyday language – a task humans perform effortlessly. The first AI “winter,” or period of disillusionment, hit in 1974 following the perceived failure of the Perceptron.
In conclusion, while optimism drives progress, it’s worth paying attention to the history of AI. Many proponents of the technology seem to have forgotten the failures of the past – and the reasons for them. As the field is in yet another boom, it’s important to remember that we’ve been here before.
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