Understanding AI Evaluation Metrics: Accuracy, Precision, & Recall Explained
A comprehensive guide on the core AI evaluation metrics—accuracy, precision, recall—complete with definitions, formulas, real‑world examples, and practical tips.
An Overview of Intelligence: Symbolic, Statistical, Embodied, and Hybrid Approaches
Explore symbolic reasoning, data‑driven statistics, embodied interactions, and hybrid models in a single, in‑depth guide. Compare their strengths, weaknesses, and how they converge to advance artificial and natural intelligence.
From Checkers to World‑Conquest: The Evolution of AI in Games
From Checkers to World‑Conquest: The Evolution of AI in Games#
1. Introduction#
Games have long served as a crucible for artificial intelligence (AI). Beginning with the deterministic puzzle of tic‑tac‑toe, the field has advanced from simple board calculations to real‑time, large‑state strategy engines that learn to play games without pre‑programmed rules. The progression from early chess engines to AI that can adapt to the unpredictable nuances of modern strategy games highlights key algorithmic breakthroughs and sets the stage for future research.
Logic Theorist: The First AI Program
Logic Theorist: The First AI Program#
The First Machine That Could Deduce Theorems#
1. Setting the Stage: Computing in the 1940s#
- Analog to digital transition: Early mechanical calculators were limited in speed and scope.
- ENIAC and early computers: The Electronic Numerical Integrator and Computer (ENIAC) performed calculations but lacked logical reasoning capabilities.
- Post‑war research: Scientists from IBM, the University of Manchester, and the Massachusetts Institute of Technology (MIT) began exploring programmable machines.
Contextual Insight: By the mid‑1940s, researchers recognized that programs could be stored in memory, giving a machine the ability to perform different tasks—an essential concept for modern AI.