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.
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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.