AI Concepts and Capabilities
What AI actually is
Artificial intelligence is software that performs tasks we associate with human cognition: perceiving, understanding language, reasoning, and generating content. Machine learning is the dominant approach: instead of hand-coding rules, you supply data and the system learns patterns. Deep learning uses layered neural networks and underpins most modern language and vision systems. Generative AI specifically produces new content (text, images, code, audio) rather than only classifying or predicting a label.
Workload categories to know
- Computer vision: image classification, object detection, OCR, facial detection.
- Natural language processing: text classification, sentiment, entity extraction, translation, summarization.
- Speech: speech-to-text (recognition), text-to-speech (synthesis), speaker recognition.
- Information extraction / document intelligence: pulling structured fields from forms and invoices.
- Anomaly detection: flagging unusual data points, common in fraud and equipment monitoring.
- Knowledge mining: indexing heterogeneous content so it becomes searchable and answerable.
- Generative AI and agents: producing content and orchestrating multi-step tasks with tools.
Traps in questions
- Confusing classification (assign one label from a fixed set) with regression (predict a number).
- Confusing object detection (locate and label multiple objects, returns bounding boxes) with image classification (one label for the whole image).
- Assuming AI is always accurate or unbiased. Responsible AI principles exist precisely because models inherit bias from data and can hallucinate.
- Mixing up speech recognition with natural language understanding - transcription is not comprehension.
- Treating generative AI as a database lookup; it predicts plausible output and may fabricate.
Rule of thumb
Ask: what is the output shape? One label from a list = classification. A continuous number = regression. New free-form content = generative. Coordinates plus labels = detection. A transcript = speech recognition.
Worked example
A hospital wants to flag X-ray images that contain any abnormality (yes/no) and also mark where on the image. The yes/no decision is binary classification; the marking requires object detection. If instead the goal were estimating patient length-of-stay in days, that is regression.