Interpretable AI

TL;DR for operators The useful idea in this paper is not “chain-of-thought, but more formal.” That would be too easy, and therefore probably wrong. The paper introduces Theorem-Grounded Execution Ontologies, or TGEO: a framework that turns a reasoning problem into an executable graph of theorem assignments, ontologies, objects, states, operators, predicates, contracts, and validation records.1 In plain operational language, it tries to convert a model’s reasoning from a persuasive memo into a governed work order. ...

Factory dashboards are full of curves. Temperature curves, vibration curves, pressure curves, yield curves, defect curves. Most AI systems are happy to predict the next point on the curve and call it intelligence. Useful, yes. Scientific, not quite. Engineers often want something more stubbornly old-fashioned: an equation. Not because equations look elegant in a slide deck, although they do help meetings feel temporarily civilized. They want equations because equations can be inspected, simulated, challenged, simplified, embedded into control systems, and argued over by humans who still prefer causes to vibes. ...

A pricing team gives an LLM several hundred property listings and asks a sensible question: Which characteristics help predict the selling price? The model returns an equally sensible list. Swimming pools. Granite countertops. Scenic views. Green lawns. Kitchen islands. Everything sounds plausible. That is the problem. The list describes what generally makes a house attractive. It does not necessarily describe what separated expensive from inexpensive houses in this particular collection, sold in particular locations, during a particular year. The LLM has supplied real-estate conventional wisdom when the business needed dataset-specific evidence. ...

Sleep looks simple until someone has to label it. A patient lies still. Sensors record electrical activity. The night becomes a long strip of waveforms. Then a sleep technologist, following clinical scoring rules, breaks the record into 30-second epochs and assigns stages: Wake, N1, N2, N3, REM. That sounds mechanical. It is not. N1 can look annoyingly close to REM. Wake can share alpha activity with early sleep. Signals are noisy. Humans disagree. Machines, when handed the wrong representation, fail with impressive confidence. Very on brand. ...

TL;DR for operators Trading platforms have spent decades giving users fixed technical indicators and then, more recently, neural models that treat those indicators as just another column in a feature table. Longfei Lu’s paper on Technical Indicator Networks, or TINs, proposes a different wiring job: make the indicator itself into the neural architecture.1 ...