Everyday Examples of AI

Your phone’s “magic”

Modern phones run compact models on-device for privacy and speed. Face unlock and photo search use vision networks to detect faces, objects, and even text (OCR), letting you search photos for “beach” or “receipts.” Dictation and voice commands use speech-to-text, intent recognition, and text-to-speech. Photo enhancement (night mode, portrait blur) uses learned priors to reconstruct detail and separate the subject from the background. All of this happens in milliseconds, often without leaving your phone.

Search, feeds, and recommendations

Search engines embed queries and documents into vectors (numeric representations that capture meaning), retrieve candidates, then rank them using signals like click-through, dwell time, and freshness. Social and streaming platforms apply similar ranking to feeds and “up next” lists. The goal isn’t just clicks; modern systems optimize for longer-term satisfaction (e.g., did you finish the video, or quickly bounce?).

• Personalization: Collaborative filtering learns from users with similar tastes; sequence models learn your next likely action.
• Controls: Mute topics, mark “not interested,” and follow the sources you want, these are training signals you control.

Maps & logistics

Routing blends graph search with predictive models of traffic and travel time. Delivery networks forecast demand, position drivers, and optimize pricing and batching. A small improvement in ETA prediction, multiplied by millions of trips saves huge amounts of time and fuel. For warehousing, vision models count inventory and spot damage; reinforcement learning finds efficient picking routes.

Money: fraud, credit, and budgeting

Fraud detection scores transactions using features like amount, merchant, device, velocity, and graph signals (shared devices, IPs, wallets). Suspicious payments trigger step-up authentication or declines. Credit risk models estimate default probability using account history and macro features; regulators expect fairness, explainability, and adverse-action reason codes. Personal finance apps categorize expenses automatically, detect outliers (“Your food delivery spend is up 24%”), and propose budgets, powered by classification and forecasting models.

Workflows: support, documentation, and coding

Language models triage emails, summarize conversations, and draft replies grounded in your policies via retrieval. Document tools rewrite for clarity, create executive summaries, and translate. For developers, code assistants autocomplete functions, explain errors, and generate tests, accelerating but not replacing engineering judgment. The common pattern is AI suggests, human decides, with logs for quality and learning.

Health & safety

AI helps transcribe and summarize clinical notes, flag anomalies in vitals, and assist radiologists with triage. Content safety systems detect phishing, malware patterns, and abusive content. These systems augment experts, not replace them; human oversight remains essential.

Crypto & Web3

• Wallet clustering: Graph algorithms and embeddings group addresses by behavior to spot communities and potential sybil patterns.
• Risk scoring: Models flag flows that touch mixers, sanctioned entities, or recent exploit addresses.
• Smart-contract copilots: Explain functions in plain English, warn about reentrancy or access-control risks, and suggest tests.
• Market digests: Natural-language summaries of token movements, DAO proposals, and on-chain events.

In all of these, analysts still set hypotheses and validate results. AI accelerates work and uncovers weak signals; it doesn’t guarantee truth.

Habits that improve results

• Be specific: State the audience, goal, and constraints (“Summarize for a CFO; 5 bullets; include 1 risk and 1 action”).
• Give examples: Few-shot prompts (showing what “good” looks like) dramatically improve output.
• Iterate: If the first answer isn’t right, refine your instructions rather than giving up.
• Use feedback: On platforms, mark “not interested,” correct labels, and report issues, these are training signals.

Mini-projects to try

• Personal recommender: Track your watch history for a week; cluster items and prompt for 5 recommendations with reasons.
• Email triage: Paste 20 subject lines; prompt for categories and urgency; draft 3 responses you could actually send.
• Budget insights: Paste anonymized transactions; prompt for category totals, anomalies, and a savings plan with concrete steps.
• On-chain digest: Collect three governance proposals; prompt for a comparative summary with pros/cons and notable addresses.