AGI on English AI Terms Dictionary

The Master Algorithm

Sat, 18 Jul 2026 10:18:07 +0000

Definition

Coined by Pedro Domingos in his book of the same name, the ‘Master Algorithm’ describes a theoretical unified framework for machine learning that could replicate all human learning processes. It envisions a single algorithm that can learn any concept given sufficient data, bridging different paradigms such as connectionism, symbolism, evolutionism, behaviorism, and analogizers. While currently speculative, it serves as a conceptual goal for researchers aiming to achieve general artificial intelligence through a comprehensive learning theory.

Recursive self-improvement

Sat, 18 Jul 2026 10:13:50 +0000

Definition

Recursive self-improvement refers to the theoretical capability of an artificial intelligence system to rewrite its own source code or architecture to become smarter, more efficient, or more capable. This concept is central to discussions on the technological singularity, where such improvements could lead to an intelligence explosion. The process involves the AI analyzing its current performance bottlenecks, generating improved versions of itself, and testing them in a loop, potentially leading to exponential growth in cognitive abilities beyond human comprehension.

Gödel machine

Sat, 18 Jul 2026 10:00:43 +0000

Definition

The Gödel machine is a hypothetical universal problem solver proposed by Jürgen Schmidhuber, based on formal logic and computability theory. It operates by continuously analyzing its own source code and environment to find proofs that a modification would improve performance according to its utility function. If such a proof is found, it safely rewrites its own code to implement the improvement. This concept represents the pinnacle of self-modifying intelligence, though it faces significant practical challenges regarding computational complexity and the undecidability of finding optimal self-improvement proofs within finite time.

AIXI

Sat, 18 Jul 2026 09:44:40 +0000

Definition

AIXI is a theoretical framework proposed by Marcus Hutter that defines an idealized intelligent agent. It combines Solomonoff induction for predicting the environment with reinforcement learning for decision-making. The agent seeks to maximize expected cumulative reward over time. Although computationally uncomputable due to the complexity of calculating Kolmogorov complexity, AIXI serves as a foundational benchmark for understanding the limits and principles of general intelligence and optimal decision-making in unknown environments.

AI-complete

Sat, 18 Jul 2026 09:44:24 +0000

Definition

AI-complete problems are tasks that, if solved, would imply the existence of Artificial General Intelligence (AGI). These problems require deep understanding, reasoning, and adaptability similar to humans, such as natural language translation, visual perception, or common sense reasoning. Unlike narrow AI tasks, AI-complete problems cannot be easily broken down into sub-problems solvable by specialized algorithms, representing the ultimate challenge in computer science and cognitive modeling.

Summary

A problem so complex that solving it requires human-like general intelligence, making it equivalent to achieving Artificial General Intelligence.