Look up any term in AI.

Gradient Descent

The rule that turns a gradient into a weight update. Worked on f(x)=x²: why the step size alone decides between converging, oscillating and blowing up.

Bias in AI (Algorithmic Bias)

Why AI systems produce unfair outcomes: how bias enters through data and proxy labels, and why no model can satisfy every definition of fairness at once.

Social AI

Social AI is artificial intelligence built to perceive, interpret, and take part in human social interaction — reading emotion, tone, intent, and context.

Underfitting

Underfitting is when a model is too simple to capture the real pattern, so it does poorly on both training and new data — the mirror of overfitting.

Regularization

Regularization deliberately constrains a model so it fits training data less perfectly, trading a little training accuracy for much better generalization.

Reinforcement Learning (RL)

How an agent learns to act by trial and error: it takes actions in an environment, earns rewards or penalties, and adjusts to maximize long-term reward.