Term tag: Interpretability

AI Safety

AI safety is three fields, not one: present-day reliability, alignment, and catastrophic risk. What each actually claims, and why they get confused.

Causal Reasoning

Why a model trained on observational data cannot tell cause from correlation, what that costs when you act on it, and what actually identifies one.

Chain-of-Thought (CoT)

A model writing out its reasoning before answering. Once a prompting trick, now trained in by RL, billed as output tokens, and frequently unfaithful.

Consciousness

Could an AI have subjective experience, and how would we tell? The hard problem, the four leading theories, and why a model's self-report proves nothing.

Decision Trees (DT)

How a decision tree actually chooses each split: Gini impurity worked by hand, why depth drives overfitting, and what a single tree cannot represent.

Explainable AI (XAI)

What being explainable actually requires: a model you can read versus a post-hoc guess about one, and the evidence that the guess is often wrong.

Feature Selection (FS)

Which columns to keep and how to decide: filter vs wrapper vs embedded, why 2^n subsets rules out brute force, and the CV mistake that fakes accuracy.

Logistic Regression (LR)

Logistic regression turns weighted evidence into a probability with the sigmoid: why the name says regression, how to read coefficients, and when to use it.

Neurons

One neuron multiplies its inputs by weights, adds a bias and applies a function. The arithmetic worked by hand, and where the brain analogy breaks.

Transparency

Transparency in AI is the degree to which a system's data, workings, and decisions are visible and disclosed to users, auditors, and regulators.