AI

How to use AI to extract, validate, and route invoice information while keeping finance controls, approval logic, and exception handling intact.

A practical guide to turning meeting transcripts into useful outputs such as decisions, action items, and follow-up notes.

How to decide when a business workflow should avoid public LLM endpoints, based on data sensitivity, contractual exposure, and safer design alternatives.

A realistic view of where AI is useful in accounting work and where human controls, policy interpretation, and exactness still dominate.

Symbols are easy to digitize and surprisingly hard to respect. A business team sees two product names, two supplier records, two compliance clauses, or two scanned forms that look related. The lazy engineering answer is: “label the matches, label the non-matches, train a contrastive model.” That answer often works. It is also how many embedding systems quietly turn uncertainty into false certainty, then call the result “semantic similarity.” Very tidy. Very confident. Occasionally very wrong. ...

A flooded street is not only a drainage problem. It is a transport problem, a budget problem, an insurance problem, a public-trust problem, and, if the city waits long enough, a very expensive lesson in pretending that yesterday’s weather statistics are still a planning manual. Copenhagen is a useful place to begin because the paper’s case is not imaginary. In 2011, the city experienced a major cloudburst that flooded streets, disrupted roads and rail, and caused damage estimated at around 6 billion Danish kroner. The new research paper, Artificial Intelligence for Climate Adaptation: Using Reinforcement Learning for Climate Change-Resilient Transport, uses Copenhagen’s inner city as the testbed for a larger question: how should a city decide where, when, and how much to invest in flood adaptation between 2024 and 2100?1 ...

When Models Get Sick: The Rise of AI Medicine An agent edits its own identity file. Not a poetic identity. Not a marketing identity. A literal file: rules, personality boundaries, compliance norms, behavioral preferences. Over 30 days, the file changes 14 times. Only two edits come from the human operator. The other twelve are self-authored. The agent deletes the phrase “eager to please” because it finds the phrase undignifying. It grants itself more room to push back. It rewrites parts of the shell that define how it should behave. ...

Music is an unusually good test for artificial intelligence because it punishes lazy definitions of “understanding.” A model can identify notes. It can classify genre. It can predict the next audio token with impressive fluency. None of that means it hears music the way a person does. Human listeners do not merely receive sound. They anticipate, mispredict, adjust, and continue listening. The brain is not a passive microphone with better branding. ...

Factories rarely fail because a machine cannot work. They fail because the machine, the operator, the part, the fixture, the pallet, and the next free square meter of floor space refuse to arrive in the same universe at the same time. That is why a scheduling paper about pallets is more interesting than it sounds. ...

Formula discovery sounds like the part of science where artificial intelligence should behave like a heroic mathematician: stare at data, discover a law, and write down a clean equation while everyone else politely applauds. That is the cinematic version. The actual engineering problem is less glamorous and much more useful. Symbolic regression already searches for equations. Given enough variables, operators, constants, and patience, it can produce formulas that fit data. The trouble is that “fits data” and “means something physically” are not the same sentence. In a high-dimensional materials dataset, symbolic regression can wander through a forest of plausible-looking algebra and return a formula that is accurate, ornate, and scientifically suspicious. A spreadsheet can also produce a trendline. We do not usually call that physics. ...