Can AI read medical scans?
Sometimes, on a narrow measured task: pattern matching on pixels, not a radiologist in software: with the DeGrave shortcut paper as the honest door into what can go wrong.
For everyone
AI without the movie script. Short explainers in plain language, with links to papers and deeper History chapters if you want the technical side.
Sometimes, on a narrow measured task: pattern matching on pixels, not a radiologist in software: with the DeGrave shortcut paper as the honest door into what can go wrong.
A plain-language guide to code-generating models as next-token engines that can draft and explain: with human review: using Codex/HumanEval as the door into measured benchmarks and why fluency is not production-ready ownership.
Training explained like practice, not mysticism: examples, errors, and updates: with links into the History serial and classic papers.
A plain-language guide to what “open” usually means for AI models: weights, code, data, or API-only: vs closed products, with tradeoffs for privacy, audit, cost, and safety, and Bommasani et al. as the door into access and release.
A plain-language guide to the two-stage recipe: learn general patterns from cheap practice on raw data, then specialize for a product job: with BERT as the door and honest limits on what finetuning buys.
A plain-language tour of what people mean by AI today: pattern engines trained on data, not sci-fi minds: with doors into the technical sources if you want them.
A plain-language guide to attention as learned weighing of context: everyday glance-back vs the engineering trick that helped sequences, with Vaswani et al. as the door and honest limits on what weights explain.
A plain-language guide to ChatGPT as a product on a language model: fluent text, instruction tuning, and honest limits: not a person and not all of AI.
A plain-language guide to embeddings as lists of numbers that place words, sentences, or images in a space where nearby means similar for the trained task: with Mikolov et al. word2vec as the classic door, and honest limits on similarity vs truth.
A plain-language guide to AI “hallucination” as fluent, confident, unsupported output: a borrowed metaphor, not a mind seeing visions: with TruthfulQA and doors into related Easy posts.
A plain-language tour of neural networks as stacked layers of simple units with adjustable weights: loosely neuron-inspired, not a brain: with doors into Rumelhart et al. and the History serial.
A plain-language guide to multimodal AI as systems that handle more than one kind of input: images plus text, not human senses: with CLIP as the door into matching pictures and captions, and honest limits on chat-about-images.
A plain-language guide to parameters as adjustable knobs: why ‘billion-parameter’ headlines are about capacity, not a library of facts: with Kaplan et al. scaling laws as the door into size, data, and compute.
A plain-language guide to RLHF as training a model to prefer answers humans rate better: not installing morals or a mind: with InstructGPT as the door and honest limits on truth, safety, and whose feedback counts.
Training data explained as the designed pile of examples that nudge a model’s weights: not a library it reads like a person: with doors into datasheets, ImageNet-era scale, and related Easy posts.
A measured guide to why training and serving models use electricity: burst vs ongoing cost, size and scale, efficiency levers: with Luccioni et al. on BLOOM as the door into what was actually measured.
Training is practice on the homework; evaluation asks whether the model can do the test: and whether the test is the real job: with CheckList as the door into how one accuracy number can hide systematic failures.