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Part ReviewPart 2 · How We Got HereDAY 11–20

Part 2 Review

All ten days of Part 2 at a glance · short, with analogies · DAY 11–20

For ten days, we traced where AI came from. Here we put the whole road from 1958 to today in one place and walk it again.

No quizzes, no experiments. Just read it through. It's fine to pick only the days that feel fuzzy.

DAY 11 · 1958: the neuron that learns

A neuron machine that learned on its own

In 1958, Frank Rosenblatt built the perceptron. It's one artificial neuron: multiply each signal by a weight, add them up, output 1 if the sum clears a threshold, else 0. The real breakthrough: when wrong, it fixed its own weights — the first machine to learn from examples, not written rules.

DAY 12 · Two AI winters

When hopes outrun skill, winter comes

In 1969, Minsky and Papert proved mathematically that one layer of neurons can't solve some problems. Money and people left — the first winter. In the 1980s, expert systems with hand-written rules boomed but failed just off-script; in 1987 their special-computer market crashed — the second winter. Both times, it wasn't that AI was bad — the promises ran ahead of its skill.

DAY 13 · 2012: deep learning comes back

The day three things came together

In 2012, a University of Toronto team's neural network won the ImageNet photo-guessing contest with a 15.3% error rate. Second place had 26.2%, so the gap was more than 10 percentage points. Hardly anything was new: millions of photos, gaming graphics cards and deep neural networks came together for the first time that year.

DAY 14 · 2017: the Transformer

Words started looking at each other

In 2017, a Google team's paper, 'Attention Is All You Need', set out the Transformer — the backbone of today's AI. Before, sentences were read front to back, one word at a time; the Transformer let every word look at all the others at once. Looking all at once means computing all at once, so it was a perfect match for graphics cards.

DAY 15 · 2020: GPT-3

Scaled up, it copied from examples alone

Same Transformer backbone, just made bigger: in 2020 that gave us GPT-3, with 175 billion numbers inside. The surprise: no retraining needed — put two or three examples in the question and it followed along right away. From then on, 'what you tell it to do' — the prompt — started to really matter.

DAY 16 · 2022: ChatGPT

A chat window is what changed the world

ChatGPT, opened to everyone on November 30, 2022, was a GPT-family model put in a back-and-forth chat window. A big part was polishing it with people's picks of the answers they liked better, so it actually got what you asked. So what changed that day was less the tech than who got to use it.

DAY 17 · Thinking before answering

Work it out before you answer

AI got much better at hard problems not by getting bigger, but by working through steps before answering. At first, people had to say 'think step by step'; later came reasoning models trained to do it on their own. The catch: longer thinking means slower answers and more text, so it's not worth it for easy questions.

DAY 18 · AI that uses tools

Grabbing a calculator when you don't know

AI's main job is adding the next word, so it's weak at exact things like big multiplications or today's weather. So it got tools — a calculator, search, running code — and from 2023, models chose by themselves when to grab one. A good AI isn't one that knows everything — it's one that notices it doesn't know and grabs a tool.

DAY 19 · AI that sees and hears

Chop anything into pieces, line them up

AI didn't learn to see photos and hear sounds by getting eyes and ears. Photos are cut into square grid pieces and sound into tiny time slices, then lined up in one row with the text pieces. Then no new backbone is needed — just feed them into the Transformer, where pieces look at each other.

DAY 20 · Everyone's model race

From using one to picking one

In late 2022 you could count good chat AIs on one hand; now dozens of groups in the US, Europe, China and Korea make their own. The biggest change: open models, whose whole bundle of numbers is released for anyone to download and run on their own gear. So now you don't just use one AI — you pick, weighing price, speed and what each one is good at.

Ten days, tied into one line

It started with one neuron (1958), froze twice, and woke up again in 2012, when data, computing power and depth came together.

In 2017, words started looking at each other and the backbone was set; then that backbone was scaled up (2020), put in a chat window (2022), and made to think, use tools, and see and hear.

And now there are dozens of models like that. In Part 3, we try out what you can actually do with them, one thing at a time — starting with summarizing and translation.

Learn more

  1. Frank Rosenblatt, The Perceptron (1958) — the first artificial neuron that learnsen.wikipedia.org/wiki/Perceptron
  2. McCulloch & Pitts, A logical calculus of the ideas immanent in nervous activity (1943)link.springer.com/article/10.1007/BF02478259
  3. Minsky & Papert, Perceptrons (1969) — the limits of a single layermitpress.mit.edu/9780262630221/perceptrons
  4. Learning representations by back-propagating errors (1986) — backpropagationnature.com/articles/323533a0
  5. ImageNet: A Large-Scale Hierarchical Image Database (2009)image-net.org/static_files/papers/imagenet_cvpr09.p…
  6. ILSVRC 2012 Results — top-5 error 15.3%image-net.org/challenges/LSVRC/2012/results.html
  7. ImageNet Classification with Deep Convolutional Neural Networks (NIPS 2012)papers.nips.cc/paper_files/paper/2012/hash/c399862d…
  8. Attention Is All You Need (2017) — the Transformerarxiv.org/abs/1706.03762
  9. Language Models are Few-Shot Learners (2020) — GPT-3, 175 billion parametersarxiv.org/abs/2005.14165
  10. Introducing ChatGPT (OpenAI, 2022)openai.com/index/chatgpt
  11. Training language models to follow instructions with human feedback (2022) — RLHFarxiv.org/abs/2203.02155
  12. Large Language Models are Zero-Shot Reasoners (2022) — 'Let's think step by step'arxiv.org/abs/2205.11916
  13. Learning to Reason with LLMs (OpenAI, 2024) — 12% and 74%openai.com/index/learning-to-reason-with-llms
  14. Toolformer (2023) — learning on its own when to call a toolarxiv.org/abs/2302.04761
  15. Function calling and other API updates (OpenAI, 2023)openai.com/index/function-calling-and-other-api-upd…
  16. GPT-4V(ision) System Card (OpenAI, 2023)openai.com/index/gpt-4v-system-card
  17. Hello GPT-4o (OpenAI, 2024) — voice replies in 0.32 seconds on averageopenai.com/index/hello-gpt-4o
  18. Introducing LLaMA (Meta AI, 2023) — 65 billion, openly releasedai.meta.com/blog/large-language-model-llama-meta-ai
  19. The Llama 3 Herd of Models (2024) — 405 billion, openly releasedarxiv.org/abs/2407.21783
  20. DeepSeek-R1 (2025) — an open reasoning modelarxiv.org/abs/2501.12948