Season 2, Episode 1. A new season answering the AI questions people are tired of pretending they understand. Part one of two.

Season one was for executives and data leaders. This season is for everyone else. It started when Sarah's mother — a retired schoolteacher, nine episodes in — rang and asked, "That was lovely, darling. What is AI?" Sarah had a technically correct answer. Not a good one.

So James holds the audience's scepticism and refuses to let Sarah drift into jargon, while they take the questions listeners sent in: what AI is, what the letters stand for, whether ChatGPT is the same thing as AI, and what the machine is really doing when it answers you.

What we cover:

• What the letters stand for, and why there is genuinely nothing hidden underneath them
• Why the term is seventy years old, not three — coined at a summer workshop in 1956, which kills the feeling that you missed the beginning
• Why there is no single agreed definition, even among the people building it, and the one word that does most of the work: infers
• The one sentence that explains everything else: traditional software is told what to do, AI is shown what to do
• Russian dolls, with the analogy's flaw fixed on air by James: each doll opens onto a shelf of siblings, not a single successor — which is where most of the real value sits
• Memorising "walked" versus working out "jumped" — the difference between a rule and a pattern, in one example
• The spam filter you already trust, and the AI effect: the moment something works, we stop calling it intelligent
• The Turing test has arguably been passed — which proves imitation and understanding are separable, not identical
• Why ChatGPT is to AI what Hoover is to vacuum cleaners, and why confusing the two makes you miss where the value is
• Car maker, engine, car: how OpenAI, GPT and ChatGPT actually relate
• What a language model is really doing, demonstrated on James in four words: "Mary had a little..."
• Guess, check, update — training explained without a single equation

Key references:

OECD, the AI definition governments have aligned to:
https://oecd.ai/en/ai-principles

NASA, on there being no single simple definition:
https://www.nasa.gov/what-is-artificial-intelligence/

IBM, how AI, machine learning, deep learning and generative AI nest together:
https://www.ibm.com/think/topics/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks

Dartmouth, the 1956 summer research project where the term was coined:
https://home.dartmouth.edu/about/artificial-intelligence-ai-coined-dartmouth

Alan Turing (1950), Computing Machinery and Intelligence:
https://doi.org/10.1093/mind/LIX.236.433

Wikipedia, the Turing test and the study in which GPT-4.5 was judged human 73% of the time:
https://en.wikipedia.org/wiki/Turing_test

Wikipedia, the AI effect and Tesler's Theorem:
https://en.wikipedia.org/wiki/AI_effect

CSET Georgetown, a plain-language explanation of next-word prediction:
https://cset.georgetown.edu/article/the-surprising-power-of-next-word-prediction-large-language-models-explained-part-1/

Britannica, artificial intelligence and the memorisation-versus-generalisation example:

https://www.britannica.com/technology/artificial-intelligence

Better AI still starts with better foundations.

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