Pithy Cyborg | AI News Made Simple
Pithy Cyborg | AI News Made Simple
Artificial intelligence is moving fast, but the core ideas behind modern AI are often poorly explained. In this category, I break down how AI models work, why large language models behave the way they do, and what concepts like hallucinations, embeddings, context windows, reasoning limits, and model collapse actually mean.
If you want clear explanations without hype, this is a strong place to start. I focus on the technical reality behind AI behavior so you can better understand what these systems are doing, where they fail, and why their limitations matter.
Browse the articles below to explore AI fundamentals in plain English.
Yes, and this happens constantly. A language model can correctly state that antibiotics do not work on viruses without having any functional understanding of bacterial cell walls, viral replication, …
Continue Reading about Can an AI Model Know a Fact Without Knowing Why It Is True? →
A large language model can summarize a legal brief, write a sonnet, and explain quantum entanglement in plain English. Ask it to count the number of times the letter "r" appears in "strawberry" and it …
Continue Reading about Why Does an LLM Understand a Sentence but Fail at a Simple Character Count? →
AI models do not have a confidence dial they forget to turn down. The same mechanism that generates fluent, authoritative-sounding text also generates fluent, authoritative-sounding errors. Certainty …
Continue Reading about Why Do AI Models Sound Certain Even When They Are Just Guessing? →
