The history of AI, from the perceptron to LLMs

70 years, five leaps

You do not need any maths to follow this. Each leap did one thing: it fixed the problem the leap before it could not solve. Read it in order and the whole of modern AI clicks into place.

1. 🧠 1957, the perceptron. Frank Rosenblatt built the first machine that could learn from examples instead of being hand-programmed. Show it a picture, ask "is this a cat", and it drew a single straight line to decide yes or no. One artificial neuron, one yes-or-no answer. Powerful for its time, but a straight line can only separate the simplest things.
2. 🕸️ 1980s, neural networks. One neuron was not enough, so researchers wired thousands together in layers. The breakthrough was backpropagation: when the network got something wrong, this algorithm nudged every connection a little so it did better next time. Rumelhart, Hinton and Williams popularised it in a 1986 Nature paper, and suddenly networks could read handwriting and tell faces apart.
3. 🏔️ 2000s to 2012, deep learning. Stack many layers and each one learns something richer than the last: edges, then shapes, then whole objects. In 2012 a deep network called AlexNet crushed the ImageNet image contest and kicked off the modern AI boom. LeCun, Bengio and Hinton later laid out the whole idea in a 2015 Nature review.
4. ⚡ 2017, the transformer. A paper called "Attention Is All You Need" let models read a whole sentence at once and weigh how every word relates to every other, instead of crawling left to right word by word. This is the "T" in GPT and the single idea that made today's fluent AI possible.
5. 🤖 Today, generative AI. Train a giant transformer on huge amounts of text and you get a large language model like Claude that can write, reason and code. Same family tree as that 1957 perceptron, just vastly bigger, better trained, and now able to generate instead of only classify.

Learn the whole thing with Claude

The fastest way to actually keep this is to have Claude teach it back at your level, then quiz you until it sticks. Two prompts do it.

Teach me the history of AI from the perceptron to today's large language models, one era at a time: the perceptron, neural networks, deep learning, the transformer, and modern generative AI.

For each era, tell me what problem the previous one could not solve, the key idea that fixed it in plain English, one real person or paper behind it, and a one-line analogy I will remember. Keep it beginner-friendly and do not assume I know any maths.

Now quiz me on that timeline. Ask me one question at a time, wait for my answer, then tell me if I am right and fill in what I missed before the next question.

Start easy (dates and names) and get harder (why the transformer mattered more than what came before). At the end, give me a two-sentence summary I could say out loud to sound like I actually understand where AI came from.

The 4 words that unlock every AI conversation