The Learning Machine A plain-language guide to artificial intelligence

Where AI is already working

Health, farming, money, language and transport — with the honest limits of each.

Deployed, not theoretical

Everything on this page is in routine use somewhere today. Each entry carries its limits with it, because a capability without its failure modes is a sales pitch rather than a description.

Health

Reading scans, sorting queues, spotting outbreaks

Image models flag diabetic retinopathy from a retina photograph, tuberculosis from a chest X-ray, and suspicious tissue on a pathology slide. In places with a handful of radiologists per million people, screening that a nurse can run with a phone camera changes who gets diagnosed.

  • Retinal screening in clinics
  • TB triage from chest X-rays
  • Sepsis early-warning scores

Where it breaks: A model trained on scans from one hospital often degrades on another's machines. It reports findings, not diagnoses, and it does not know what it has never seen.

Farming

Pests, rain and yield

A photograph of a leaf can be classified for fall armyworm or cassava mosaic disease on a phone with no signal. Satellite imagery plus weather data estimates yields district by district before harvest, which is what crop insurance and food planning need.

  • Leaf-photo pest diagnosis
  • Satellite yield estimates
  • Index-based crop insurance

Where it breaks: Advice is only as good as the local training photographs. Models built on North American maize misread East African varieties, and a wrong pest call costs a season.

Money

Fraud, credit and collections

Card networks score every transaction in milliseconds against your own history. Mobile-money lenders score borrowers with no formal credit file using airtime top-ups, transaction rhythm and repayment behaviour, which is how millions got a first loan.

  • Real-time card fraud scoring
  • Mobile-money credit scoring
  • Anti-money-laundering triage

Where it breaks: Alternative scoring is opaque. If the model quietly learns that a neighbourhood repays badly, it penalises everyone there, and the applicant is rarely told why.

Language

Translation, transcription and drafting

Speech recognition turns a clinic consultation into notes. Translation now covers Swahili, Amharic, Yoruba and hundreds of other languages that commercial systems ignored for decades. Drafting tools take a first pass at reports, emails and code.

  • Clinical note dictation
  • Swahili and Sheng translation
  • Code completion

Where it breaks: Low-resource languages get thinner training data and worse output, and models state wrong facts in fluent sentences. Fluency is not accuracy.

Transport

Routing, maintenance and driving

Journey estimates come from live traffic models rather than distance. Fleet operators predict which bus will break down from vibration and engine data. Driver-assist systems hold a lane and brake for obstacles.

  • Live traffic routing
  • Predictive fleet maintenance
  • Lane keeping and automatic braking

Where it breaks: Full self-driving has been eighteen months away for over a decade. Rain, hand signals, potholes and unmapped roads remain genuinely hard.

Learning

Practice, marking and tutoring

Adaptive practice systems track which sub-skill a student is missing and serve the next question accordingly. Tutoring assistants explain a step again, differently, without getting bored — which matters in a class of eighty.

  • Adaptive maths practice
  • Essay feedback drafts
  • Language conversation partners

Where it breaks: A confident wrong explanation is worse than no explanation. These tools work when a teacher reviews the output, not when they replace one.

The pattern across all of them

AI is strongest where a task is repetitive, the data is abundant, and a wrong answer can be caught by someone downstream. It is weakest where examples are rare, the situation is novel, or the cost of an error lands on a person who cannot appeal.

Which is why the successful deployments are nearly all assistive. The model reads the first pass and a person decides. Systems designed to remove the person have a much poorer record, and usually get quietly re-staffed.

A question worth asking of any AI product

"What happens when it is wrong, and who finds out?" If the answer is that nobody finds out, the accuracy figure does not matter.