Question 1

What's the difference between Artificial Intelligence and Machine Learning?

Accepted Answer

Artificial Intelligence (AI) is the broad goal: getting computers to perform tasks that seem to require human intelligence (like understanding language or recognizing images). Machine Learning (ML) is one way to achieve AI: instead of hand-coding rules, you train models on data so they learn patterns and make predictions or decisions.

Question 2

When should I use Artificial Intelligence?

Accepted Answer

Use AI when rules-based logic is too brittle or complex and you have a clear outcome to optimize. Common triggers include: you need to classify or predict (fraud detection, churn prediction), understand unstructured data (text, images, audio), personalize experiences (recommendations), automate repetitive knowledge work (document processing, customer support), or detect anomalies (monitoring and security). Start with a small, measurable use case and confirm you have enough quality data (or a suitable prebuilt AI API) before scaling.

Question 3

How much does Artificial Intelligence cost?

Accepted Answer

AI costs vary widely based on what you build and how you run it. Key factors include: (1) data costs (storage, labeling, governance), (2) training costs (compute time, often GPU/TPU, and experiment iterations), (3) inference costs (how many predictions or API calls you make and latency requirements), (4) model choice (small vs large models; proprietary API vs self-hosted), and (5) operational costs (monitoring, retraining, security, compliance). Many teams reduce cost by using prebuilt AI services for common tasks, batching requests, choosing smaller models when possible, and autoscaling infrastructure.

Artificial Intelligence

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