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Mathematical intuition, deep learning, and Robbins' problem

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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F. Thomas Bruss

History and Overview · Submitted 14 Dec 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The second and major goal is to guide interested readers through the probabilistic intuition behind Robbins' problem and to show why A.I., and in particular Deep Learning, may contribute an essential part in its solution.

how it is stated · hedged possibility 0.92 read the record →

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arXiv ID2401.05368 (version 1)
Pith ID3KBUJQ2M, Pith's permanent record of this paper and its review
CitationF. Thomas Bruss. "Mathematical intuition, deep learning, and Robbins' problem." arXiv:2401.05368 (2023). https://pith.science/paper/3KBUJQ2M
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,303 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.97 · human participants 0.03 · controlled comparison 0.01 · randomized assignment 0.01 · states quantitative result 0.02 · compares with prior results 0.03 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

{\bf Abstract.} The present article is an essay about mathematical intuition and Artificial intelligence (A.I.), followed by a guided excursion to a well-known open problem. It has two objectives. The first is to reconcile the way of thinking of a computer program as a sequence of mathematically defined instructions with what we face nowadays with newer developments. The second and major goal is to guide interested readers through the probabilistic intuition behind Robbins' problem and to show why A.I., and in particular Deep Learning, may contribute an essential part in its solution. This article contains no new mathematical results, and no implementation of deep learning either. Nevertheless, we hope to find through its semi-historic narrative style, with well-known examples and an easily accessible terminology, the interest of mathematicians of different inclinations.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 {\bf Abstract.} The present article is an essay about mathematical intuition and Artificial intelligence (A.I.), followed by a guided excursion to a well-known open problem. It has two objectives. contribution 0.73 0.37 0.04 asserted 0.96
s1 The first is to reconcile the way of thinking of a computer program as a sequence of mathematically defined instructions with what we face nowadays with newer developments. contribution 0.85 0.20 0.04 asserted 0.96
s2 The second and major goal is to guide interested readers through the probabilistic intuition behind Robbins' problem and to show why A.I., and in particular Deep Learning, may contribute an essential part in its solution. contribution 0.89 0.21 0.30 hedged possibility 0.92
s3 This article contains no new mathematical results, and no implementation of deep learning either. limitation 1.00 0.75 0.07 asserted 1.00
s4 Nevertheless, we hope to find through its semi-historic narrative style, with well-known examples and an easily accessible terminology, the interest of mathematicians of different inclinations. contribution 0.36 0.13 0.05 hedged possibility 0.70

What kind of paper the classifier reports this is: position or commentary 1.00 · evidence from argument 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

{\bf Abstract.} The present article is an essay about mathematical intuition and Artificial intelligence (A.I.), followed by a guided excursion to a well-known open problem. It has two objectives. The first is to reconcile the way of thinking of a computer program as a sequence of mathematically defined instructions with what we face nowadays with newer developments. The second and major goal is to guide interested readers through the probabilistic intuition behind Robbins' problem and to show why A.I., and in particular Deep Learning, may contribute an essential part in its solution. This article contains no new mathematical results, and no implementation of deep learning either. Nevertheless, we hope to find through its semi-historic narrative style, with well-known examples and an easily accessible terminology, the interest of mathematicians of different inclinations.

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