Season 2, Episode 2. Part two of two. Last episode ended mid-argument: James asked where a chatbot stores the date of the Battle of Hastings, and Sarah said it doesn't store it anywhere. This is the answer.

Still no jargon and no assumed knowledge. James holds the audience's scepticism, Sarah explains, and James drags her back out of the machine room whenever it gets too clever.

What we cover:

• Where the "knowledge" actually lives when there is no database inside: a map of meaning built from billions of numbers, where related ideas sit near each other
• Why relationships are directions rather than stored facts — the step from "man" to "woman" is the same step from "king" to "queen," and nobody ever wrote a rule about gender
• Why the map isn't flat: hundreds of directions, each one a quiet question about the word, so a word is really a dot with hundreds of scores attached
• The three things worth keeping, and permission to forget the rest
• Does it understand? The honest answer is that serious people are split roughly fifty-fifty, and we don't pretend to settle it
• Why it invents things: plausibility is the target, not truth — plus the second cause, which is that we scored these systems in a way that rewards a confident guess over "I don't know"
• The lawyer who filed fabricated case law and called ChatGPT "a super search engine" — four words that contain the whole misconception
• Four differences from normal software you will actually feel: same question different answer, nobody wrote the rules, it fails politely instead of crashing, and it goes stale
• Why that makes AI a running cost rather than a purchase — less like buying a dishwasher, more like hiring someone, and only one of those two gets budgeted for
• The rule of thumb worth keeping: if you can write the rule down, write the rule down. Nobody should train a model to calculate GST
• James pushes back twice: if there's no database, why does his chatbot give him real working links — and how does a frozen model know about last week? Sarah on retrieval, live search, and the model-versus-system distinction
• Why cited answers are safer but not safe: the check stops being "is this true" and becomes "does the thing it linked me to actually say this"

The takeaway: the useful question is never "does it understand?" It's "can I rely on this without checking?" Even when it hands you a link. A chatbot is not a search engine.

Key references:

• CSET Georgetown, how language models predict the next word, in plain language: https://cset.georgetown.edu/article/the-surprising-power-of-next-word-prediction-large-language-models-explained-part-1
• MIT Technology Review, why hallucination is what these models are designed to do, and the lawyer's "super search engine": https://www.technologyreview.com/2024/06/18/1093440/what-causes-ai-hallucinate-chatbots/
• OpenAI, why training and evaluation reward guessing over admitting uncertainty: https://openai.com/index/why-language-models-hallucinate/
• Bender, Gebru et al. (2021), On the Dangers of Stochastic Parrots: https://doi.org/10.1145/3442188.3445922
• Lewis et al. (2020), the original paper on retrieval-augmented generation: https://arxiv.org/abs/2005.11401
• IBM, what retrieval-augmented generation is: https://www.ibm.com/think/topics/retrieval-augmented-generation
• IBM, narrow AI is the only kind that exists today: https://www.ibm.com/think/topics/artificial-general-intelligence

Better AI still starts with better foundations.

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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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Season two of AI - Beyond the Hype is a different show, and we need your help to make it.

Season one was nine episodes for executives and data leaders on the foundations underneath enterprise AI. Season two answers questions about AI from people who don't work in this field — and who are quietly tired of pretending they follow the conversation.

How does it actually know things? Is it thinking? Why does it make things up? Should I let my kids use it? Will it take my job?

Not dumbed down. Deep enough to give a real answer, then stopping before the jargon starts.

No question is too basic — the more basic, the better. One sentence is plenty. We'll do the work.

Send yours to: asksarahandjames@gmail.com

Better AI still starts with better foundations.

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