Abstraction & Reasoning Corpus for Artificial General Intelligence

As explained in Deep Learning with R, Third Edition

ARC-AGI-1 is the original version of the ARC-AGI benchmark, consisting of abstract reasoning tasks intended to test fluid intelligence.

ARC-AGI-1 is a benchmark for fluid intelligence and abstract reasoning. Each task provides three or four input–output grid examples, followed by a new input whose output must be constructed. The task-specific rule is intended to be discovered on the fly rather than recalled from training. section 19.3.4

A typical ARC-AGI task

Study several paired grids, infer the transformation connecting each input to its output, then apply that transformation to an unseen grid. You receive three attempts before moving to the next task, so success depends on identifying the underlying logic rather than memorizing a familiar strategy. section 19.3.4

ARC-AGI controls for prior knowledge through core knowledge priors and tests only tasks that were not previously seen. Its resistance to memorization makes it a demanding test of generalization and on-the-fly recombination; scaling language models alone produced only limited progress on the 2019 benchmark. section 19.3.4 section 19.3.5

Figure 19.9 An ARC-AGI task: the nature of the task is demonstrated by a couple of input–output pair examples. Provided with a new input, you must construct the corresponding output.

Use the task figure to inspect how paired grids demonstrate a rule before a new grid must be solved. section 19.3.4

How Deep Learning with R, Third Edition covers it

How deep it goes

Defines the task format and rationale, but stops short of specifying individual grid transformations or solving algorithms.

Examples

Uses paired input–output grids, an unseen input grid, three attempts, and Figure 19.9 as its concrete task illustration.

What it assumes

Assumes core knowledge priors as a common starting point while demonstrating why solving requires more than prior task exposure.

What you can do after

Evaluate reasoning systems by comparing their ability to infer novel grid transformations rather than memorize known strategies.

How it connects

Connects abstract reasoning with fluid intelligence, generalization, core knowledge priors, IQ tests, and memorization-resistant evaluation.

liveBookmanning.com

Read Deep Learning with R, Third Edition on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Sutskever's ListARC-AGI is a benchmark of small grid-based puzzles that evaluates abstract rule induction from a few examples.
• 

  liveBookmanning.com
  Deep Learning with Python, Third EditionA benchmark dataset designed to measure generalization and abstract reasoning on tasks that are easy for humans but challenging for standard AI systems.

Compare how 3 books explain Abstraction & Reasoning Corpus for Artificial General Intelligence →

Related concepts

• generalizationmeasures · Generalization is the extension of a pattern, model, solution, or conclusion beyond the specific examples or cases from which it was derived.
• ARC-AGI-2contrasts with · ARC-AGI-2 is a benchmark dataset of abstract reasoning tasks designed to evaluate fluid reasoning and generalization to novel problems.
• fluid intelligencemeasures · Fluid intelligence is the ability to solve novel problems and adapt reasoning to unfamiliar situations without relying solely on previously learned knowledge.
• memorizationcontrasts with · Memorization is the retention of specific information or examples in a form that enables their later recall or reproduction.
• language modelcontrasts with · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.