Looking back at how far AI has actually come since it all began

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The field formally traces its name back to a 1956 summer workshop at Dartmouth, where a small group of researchers coined the term artificial intelligence and genuinely believed a machine capable of human level reasoning might be achievable within a generation. That optimism collided with reality repeatedly over the following decades, producing what historians now call AI winters, stretches where funding and interest collapsed after early promises failed to materialize.

Expert systems in the 1980s brought a real commercial wave, rule based programs that could genuinely diagnose diseases or configure computer orders within a narrow domain, before that approach also hit a wall once the sheer complexity of encoding real world knowledge by hand became apparent. Progress then stayed genuinely slow and academic for most of the 1990s and 2000s, punctuated mainly by narrow wins like Deep Blue beating Kasparov at chess in 1997.

The actual turning point most researchers point to is 2012, when a neural network called AlexNet dramatically outperformed every other approach at an image recognition competition, proving that deep learning, an idea that had existed in some form for decades, finally had enough data and computing power behind it to genuinely work at scale. The 2017 transformer architecture paper then set up the specific technical foundation that every major language model since has actually built on.

ChatGPT's public launch in November 2022 is the moment this went from a research community's excitement to genuinely mainstream household awareness almost overnight. And the pace since then has been dramatically faster than almost any earlier period in the field's entire history, multimodal models, coding agents, and now systems that can complete genuinely multi step tasks with real autonomy.

So the honest arc here is nearly seventy years of alternating hype and disappointment before the current stretch of genuine sustained progress. Which is worth remembering both when today's pace feels overwhelming and when it feels like it might be slowing down again