What is Artificial Intelligence?

Artificial Intelligence (AI) is the field of computer science focused on building systems that perform tasks that normally require human intelligence.

The three layers

Artificial Intelligence  (broad: any technique that mimics human intelligence)
  └── Machine Learning   (learns patterns from data without explicit rules)
        └── Deep Learning (ML using neural networks with many layers)

Machine Learning in one sentence

Give a computer examples, and it figures out the rules itself.

Traditional programming: data + rules → output
Machine learning: data + output → rules

Why now?

Three things converged in the 2010s:

Data — the internet produced more labelled data than ever
Compute — GPUs made matrix operations cheap
Algorithms — backpropagation + deep networks finally worked at scale

Real-world AI applications

Application	Type
Spam filter	Supervised learning (classification)
Product recommendations	Collaborative filtering
ChatGPT / Claude	Large language model (transformer)
Self-driving car	Reinforcement learning + computer vision
Face unlock	Deep learning (CNN)
Fraud detection	Anomaly detection

Large Language Models (LLMs)

LLMs like GPT-4 and Claude are trained on internet-scale text data. They learn to predict the next token — and from this simple task, emergent capabilities arise (reasoning, coding, translation).

Key parameters to know:

Temperature — controls randomness (0 = deterministic, 1 = creative)
Context window — how much text the model can "see" at once (GPT-4: 128K tokens)
Fine-tuning — adapting a pre-trained model on domain-specific data

Interview Tip

When asked about AI in a backend interview, focus on practical integration: how to call OpenAI/Claude APIs, how to design prompts, how to handle rate limits and errors. Theoretical ML knowledge is less expected unless you're applying for an ML role.