Machine learning is the part of artificial intelligence that learns patterns from data, statistics and trial and error, then uses those patterns to recognize, rank, predict or control something. The 44 examples below show where that process appears in everyday products and behind-the-scenes operations. A use case is not proof that a system is accurate, safe or fair: Built In’s company examples are reported illustrations, while independent performance evidence must be checked separately.
What machine learning does
Traditional software follows rules written in advance. A machine-learning system is trained on examples or feedback and adjusts a model so it can produce an output for new data. The output might be a label (“this image contains a face”), a score (the likelihood of fraud), a ranking (which post should appear first), a generated response or a control signal for a robot.
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Machine learning is a subfield of AI, not a synonym for every AI feature. Generative AI belongs within the broader machine-learning family, but many machine-learning systems never generate text or images. A recommendation engine, speech recognizer, fraud detector and autonomous-vehicle controller solve different problems and may require different data, model types, evaluation methods and safeguards.
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The examples are organized by the task being performed. The named company examples reflect how Built In described applications in its August 26, 2026 update, rather than an independent audit of the products.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
Perception: turning images, sound and language into usable signals
Perception systems classify or extract information from unstructured inputs such as photos, video, speech and documents. Their output is often consumed by another model or reviewed by a person.
| # | Application | Typical data and output | How it is used and what to check |
|---|---|---|---|
| 1 | Face authentication | Camera frames become a face representation and a match score. | Used to unlock a device or approve an action. Check liveness tests, false matches and a non-biometric fallback. Built In cites Apple Face ID as an illustration. |
| 2 | Photo search and tagging | Pixels are classified into people, places, objects or scenes. | Creates searchable albums. Review errors involving similar faces, unusual lighting and sensitive attributes. |
| 3 | Object detection in images | Images or video produce labels and bounding boxes for detected objects. | Can support moderation, inventory or security workflows. Built In lists AWS Rekognition as an example; its mention does not establish accuracy for a particular deployment. |
| 4 | Document OCR and extraction | Scanned pages are converted into text, fields and confidence scores. | Speeds invoice, form and identity-document handling. Human review is needed when confidence is low or a field changes a financial or legal outcome. |
| 5 | Medical-image assistance | Radiology, pathology or dermatology images produce a classification or highlighted region. | Can prioritize a queue or flag a finding for a clinician. Misclassification and misdiagnosis are material risks; a flag is not a diagnosis. |
| 6 | Speech-to-text dictation | Audio is mapped to words, punctuation and speaker turns. | Used in captions, notes and call transcripts. Measure word-error rates across accents, languages, noise levels and specialist vocabulary. |
| 7 | Voice-assistant intent recognition | Spoken utterances are classified into an intent and extracted parameters. | Triggers a search, reminder or device command. Confirm the intent before costly, destructive or privacy-sensitive actions. |
| 8 | Speaker identification | Voice features are compared with enrolled speaker profiles. | Can personalize a service or investigate an audio file. Check consent, spoof resistance and the consequences of a false match. |
| 9 | Machine translation | Text or speech in one language is converted into another. | Built In names Google Translate as an application example. Have a qualified person check safety, legal, medical and culturally sensitive translations. |
| 10 | Sentiment and intent analysis | Messages, reviews or calls receive topic, sentiment or intent labels. | Summarizes large feedback volumes. Sarcasm, code-switching and minority dialects can make a confident label wrong. |
| 11 | Visual quality inspection | Factory-camera images are classified as acceptable, defective or uncertain. | Supports production-line inspection. Keep a manual path for borderline items and retrain when materials, lighting or camera positions change. |
Ranking and recommendation: deciding what appears first
Ranking systems estimate which item is most relevant to a person or business objective. They do not merely “know what is best”; they optimize a chosen signal, such as predicted engagement, purchase likelihood or task relevance.
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| # | Application | Signals and output | Decision and caution |
|---|---|---|---|
| 12 | Online-shop recommendations | Views, searches, purchases and item attributes produce a ranked list. | Built In describes Etsy recommendations as an example. Check whether popularity feedback narrows discovery or disadvantages new sellers. |
| 13 | Streaming recommendations | Viewing, listening, skips and content features produce suggested titles or tracks. | Built In cites Netflix as an illustration. A recommendation is a ranking objective, not proof that the item is objectively superior. |
| 14 | Social-feed ranking | Posts, follows, interactions and freshness signals determine feed order. | Built In includes social-feed ranking. Audit what engagement incentives amplify and provide controls for chronology, muting and corrections. |
| 15 | Search-result and answer ranking | Queries, document features, prior interactions and feedback produce an ordered result set. | Built In lists answer ranking among applications. Test relevance and coverage for uncommon queries; a high-ranked answer can still be wrong. |
| 16 | News and article personalization | Reading behavior and story topics produce a personalized selection. | Useful for discovery but can reduce viewpoint diversity. Make the basis of personalization understandable and offer an unpersonalized option. |
| 17 | Music-playlist generation | Listening sequences and audio characteristics produce a playlist or next-track prediction. | Evaluate novelty, repetition and representation of less-played artists, not only immediate clicks. |
| 18 | Advertising selection | Context, audience features and predicted response rank candidate adverts. | Review privacy, consent and disparate delivery. A higher predicted click rate is not evidence of a beneficial outcome. |
| 19 | Job and project matching | Skills, experience and role requirements produce ranked candidates or assignments. | Use as decision support with structured human review. Historical hiring data can reproduce past exclusion. |
| 20 | Travel and route-option ranking | Map, traffic, schedule and preference signals rank routes or itineraries. | Show assumptions such as tolls, accessibility or arrival-time reliability; let users override a recommendation. |
| 21 | Email spam filtering | Message text, sender behavior, links and attachments produce a spam probability. | Quarantines likely abuse. Provide recovery for false positives and avoid treating a score as a definitive security verdict. |
| 22 | Autocomplete and next-word prediction | Typing context and language patterns rank likely words, phrases or code completions. | Saves time but can insert inaccurate or sensitive text. Require review before sending messages or executing generated code. |
Prediction and anomaly detection: estimating what may happen
These systems output probabilities, forecasts or alerts. They are especially useful when a person needs to focus attention, but the threshold for an alert changes the balance between missed events and false alarms.
| # | Application | Signals and output | Decision and caution |
|---|---|---|---|
| 23 | Credit decisioning | Application, repayment and account data produce a risk score or decision recommendation. | Built In identifies credit decisioning as a financial use. Explain adverse decisions, test for disparate effects and retain a human appeal route. |
| 24 | Fraud detection | Transaction amount, timing, device and account relationships produce a fraud probability. | Can block or hold a payment. Tune thresholds to limit customer lockouts and provide a rapid review process. |
| 25 | Financial anomaly detection | Time-series and network behavior are compared with a learned normal pattern. | Built In describes anomaly detection in finance. An unusual event is not necessarily misconduct; investigate alerts with context. |
| 26 | Insurance-claim triage | Claim text, images, policy data and history prioritize cases or estimate severity. | Routes straightforward cases and flags complexity. Audit outcomes by claimant group and preserve manual handling for disputes. |
| 27 | Healthcare coding assistance | Clinical notes are mapped to possible diagnosis or procedure codes. | Built In includes healthcare coding. A coder must verify the suggestion because an incorrect code can affect treatment records and payment. |
| 28 | Healthcare-claims processing | Claim fields and documentation predict missing information, eligibility or routing. | Reduces administrative work but should not silently deny care. Log reasons and make correction straightforward. |
| 29 | Retail demand forecasting | Sales history, seasonality, promotions and local factors produce a future-demand estimate. | Supports purchasing and staffing. Monitor forecast drift when prices, supply or customer behavior changes. |
| 30 | Inventory replenishment | Stock levels, lead times and demand predictions produce an order quantity or alert. | Balances shortages and carrying costs. Include supplier disruption scenarios that historical data may not represent. |
| 31 | Predictive maintenance | Sensor readings, operating cycles and repair history estimate failure risk or remaining useful life. | Schedules inspection before breakdown. Validate alerts against real failures and do not skip safety checks because a model predicts low risk. |
| 32 | Energy-load forecasting | Weather, calendar and consumption measurements predict future demand. | Helps plan generation and storage. Recheck performance during extreme weather or changing equipment use. |
| 33 | Customer churn or propensity scoring | Usage, service contacts and account history estimate the likelihood of leaving or taking an action. | Guides outreach. Test whether interventions help customers rather than merely identifying them as targets. |
Automation and control: choosing or executing an action
Control systems connect predictions to physical or operational actions. Errors can have immediate safety or financial consequences, so the appropriate level of human supervision matters as much as model quality.
| # | Application | Signals and output | Decision and caution |
|---|---|---|---|
| 34 | Autonomous driving | Cameras, radar, maps and vehicle state produce perception, prediction and control commands. | Built In cites Waymo autonomous vehicles as an example. Define the operating conditions, fallback behavior and human responsibility; a demonstration is not a safety certification. |
| 35 | Driver-assistance functions | Lane, speed and obstacle signals produce warnings or steering and braking assistance. | Keep the driver engaged and verify limitations in poor weather, unusual road layouts and sensor obstruction. |
| 36 | Warehouse robot navigation | Maps, location sensors and nearby-object data produce collision-free paths. | Coordinates mobile robots around people and stock. Use physical safeguards and a stop control independent of the learned policy. |
| 37 | Robotic picking and sorting | Camera and gripper feedback identify an item and choose a grasp or destination. | Improves throughput for variable goods. Test fragile, occluded and out-of-distribution items before unattended operation. |
| 38 | Industrial process control | Temperature, pressure, flow and quality readings produce set-point or actuator recommendations. | Can stabilize a process, but hard safety limits and operator override must remain outside the model. |
| 39 | Precision agriculture | Soil, weather, satellite and crop imagery guide irrigation, fertilizer or treatment zones. | Reduces blanket application when conditions support it. Validate recommendations across fields, seasons and crop varieties. |
| 40 | Delivery-route optimization | Orders, vehicle capacity, traffic and time windows produce route assignments. | Reduces mileage or late deliveries. Include driver hours, accessibility and disruption handling in the objective. |
| 41 | Traffic-signal optimization | Intersection counts, queues and timing history produce signal adjustments. | May improve flow while protecting pedestrian phases. Measure impacts across neighborhoods, not only average vehicle delay. |
| 42 | Cybersecurity threat detection | Network, endpoint and authentication events produce an anomaly or threat score. | Prioritizes analyst investigation. Keep evidence, test against novel attacks and avoid automatic isolation without a safe recovery path. |
| 43 | Game-playing and simulation agents | Game states and reward feedback produce the next move or strategy. | Demonstrates learning through trial and error. Success in a defined game environment does not transfer automatically to real-world decisions. |
| 44 | Generative assistants for text, images or code | Large training datasets and a prompt produce a probabilistic completion or artifact. | Generative AI is part of machine learning. Review for factual errors, unwanted memorization, security flaws and inappropriate automation before publication or deployment. |
How to judge an application instead of trusting its label
“Uses machine learning” says little about whether a system is appropriate. Apply the same questions to a phone feature, a hospital workflow or an autonomous machine:
- Define the task. Is the system recognizing, ranking, forecasting, detecting an anomaly or controlling an action?
- Identify the input. List the data and signals, their age, their missing values and the populations or environments represented.
- Separate score from decision. A probability or ranking is not the final decision. Identify who or what acts on it and what threshold is used.
- Specify human oversight. Look for review, appeal, override, fallback and a safe shutdown path. High-impact decisions need more than a nominal human in the loop.
- Ask for evidence that matches the claim. A vendor description supports the fact that an application is claimed or reported; it does not establish accuracy, safety, fairness, savings or current availability. Look for independently described test conditions, error rates and update dates.
- Plan for change. Monitor drift, false positives, false negatives and subgroup performance. Reassess after a new data source, sensor, product policy or operating environment is introduced.
Benefits and risks that cut across all 44 examples
Machine learning can process more records than a person, surface patterns that are difficult to spot manually, personalize a service, forecast demand and automate repetitive work. Those benefits depend on a clearly defined objective and data that represent the situation in which the system will be used.
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The same mechanism can produce harm. A misclassification can reject a legitimate payment, hide a relevant post or label a person incorrectly. In healthcare, an erroneous prediction can contribute to misdiagnosis. In embodied systems, a bad perception or control decision can cause physical injury. The Royal Society’s report Machine learning: the power and promise of computers that learn by example discusses these categories of harm and should be read as a 2017 issue framework, not a current regulatory survey.
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Public familiarity is also easy to overstate. In a 2017 survey conducted with Ipsos MORI and reported by the Royal Society, 89% of participants had heard of at least one of eight example applications, while 9% had heard the term “machine learning.” Those are historical findings and should not be presented as a 2026 estimate. The Royal Society summarized the everyday reality this way: “Many people already interact with machine learning systems on a daily basis, for example through virtual personal assistants on smartphones.”
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A practical mental model
When you encounter a new claim, translate it into one sentence: Given these inputs, the system produces this output, which this person or machine uses for this decision, under these conditions, with this level of review. That sentence exposes whether the claim concerns perception, ranking, prediction or control; what evidence is still missing; and where an error would matter. It also prevents a familiar product name or an impressive demonstration from being mistaken for a universal measure of machine-learning performance.
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