What is AI?

AI refers to computational systems that learn, reason, and adapt. It encompasses machine learning, deep learning, robotics, and more.

Computer vision lets machines interpret visual data from cameras and sensors. Applications include facial recognition, autonomous driving, and medical imaging.

RL agents learn by interacting with environments, receiving rewards for good actions. This trained AlphaGo to beat the world champion in Go.

Training involves collecting data, preprocessing it, choosing a model architecture, optimizing with a loss function, and evaluating performance.

Pre-trained models can be fine-tuned for specific tasks with smaller datasets. This makes AI development faster and more accessible.

Biased training data produces discriminatory AI. Mitigation strategies include diverse datasets, bias audits, and fairness constraints in model training.

AI accelerates discoveries in protein folding (AlphaFold), drug design, climate modeling, and particle physics by finding patterns humans miss.

Self-driving cars use computer vision, sensor fusion, and planning algorithms. Drones and robots also use AI for navigation and decision-making.

Governments worldwide are creating AI regulations. The EU AI Act classifies AI by risk level. Responsible AI requires transparency and accountability.

Prompt engineering is the skill of crafting effective instructions for AI. Clear context, specific constraints, and examples improve AI outputs significantly.

AI careers include ML engineer, data scientist, AI researcher, ethics specialist, and prompt engineer. The field needs diverse perspectives.

Alan Turing asked 'Can machines think?' in 1950. Since then, AI evolved from expert systems to neural networks to today's transformer models.

AI encompasses deep learning, NLP, computer vision, and more. Understanding its capabilities, limitations, ethics, and societal impact is essential.

Supervised learning uses labeled data, unsupervised learning finds hidden patterns, and reinforcement learning uses rewards to guide behavior.

Neural networks are layers of artificial neurons inspired by the brain. Each neuron processes inputs, applies weights, and passes results forward.

Deep learning uses neural networks with many layers to solve complex problems like image recognition, language translation, and game playing.

CNNs are specialized for image processing. They use filters that slide across images to detect edges, shapes, and objects layer by layer.

NLP enables AI to parse grammar, understand context, generate text, and translate languages. Modern NLP uses transformer architectures.

The transformer architecture uses an attention mechanism that lets the model focus on relevant parts of input. This powers GPT, BERT, and similar models.

Generative AI creates new content — text, images, music, and code. Models like GPT generate text, while diffusion models create images.