Plain-language explainers on the ideas behind modern AI. Each piece below is a self-contained read — no hype, just the concepts.
How a Neural Network Learns: Gradient Descent in Plain English
Imagine standing on a foggy hillside trying to reach the valley floor. You can’t see far, but you can feel the slope under your feet — so you take a small step downhill, then another. That is gradient descent. A neural network’s “position” is the value of its millions of weights; the “altitude” is its error on training data. Each training step measures the slope (the gradient) and nudges every weight slightly downhill. Repeat millions of times, and the network settles into a configuration where its predictions are usefully accurate. Learning rate, batching, and optimizers like Adam are all refinements of this one walk downhill.
Tokens: The Atoms of Language Models
Language models don’t read letters or whole words — they read tokens, statistically common chunks of text. “Unbelievable” might split into “un”, “believ”, “able”. Tokenization explains many quirks of LLMs: why they sometimes struggle to count letters in a word, why context windows are measured in tokens rather than words, and why API pricing is per token. As a rule of thumb for English, one token is roughly four characters or three-quarters of a word — but the exact split depends on each model’s tokenizer.
Why Your Next Laptop Has an NPU
Neural networks are mostly enormous grids of multiply-and-add operations. CPUs do these fine but one batch at a time; GPUs do thousands in parallel but draw serious power. An NPU is purpose-built silicon for exactly this math at very low energy cost, which is why it fits in a phone or thin laptop. The payoff is on-device AI: live captioning, image search, transcription, and assistant features that work offline and keep your data local instead of sending it to a server.
RAG: Giving Language Models an Open-Book Exam
A plain LLM answers from memory — whatever it absorbed during training. Retrieval-Augmented Generation changes the exam rules: before answering, the system searches a document store for relevant passages (using embeddings to match meaning, not just keywords) and pastes the best ones into the model’s context. The model then writes its answer grounded in those sources. The result: fresher information, fewer fabrications, and answers that can cite where they came from.
Hallucinations: Why Fluent Isn’t the Same as True
An LLM’s training objective is to produce plausible text, and most of the time plausible text is also true. But when the model lacks knowledge, the objective doesn’t change — it still produces something plausible, now untethered from fact. That’s a hallucination. Mitigations include retrieval grounding, asking models to cite sources, calibrated refusals (“I don’t know”), and — above all — human verification for anything that matters.
From Winter to Spring: AI’s Cycles of Hype and Progress
AI research has weathered repeated “winters” — periods when funding and enthusiasm collapsed after expectations outran results, notably in the 1970s and late 1980s. Each thaw came from a real technical shift: faster hardware, more data, better algorithms. The deep-learning era that began around 2012 combined all three. The lesson of the winters endures: durable progress comes from measurable capability gains, not promises — a useful lens for evaluating today’s claims, too.
