Contents of /works/2019-reframing-superintelligence/edition/
| Name | Form | Size | Title |
|---|---|---|---|
| README.md | plain text | 651 words | README — Reframing Superintelligence: Comprehensive AI Services as General Intelligence — K. Eric Drexler, FHI Technical Report #2019-1 (Markdown edition) |
| 00-abstract.md | plain text | 401 words | Abstract |
| 01-preface.md | plain text | 373 words | Preface |
| 02-secI-intro1.md | plain text | 2,062 words | §I Introduction: From R&D automation to comprehensive AI Services |
| 03-secII-intro2.md | plain text | 3,170 words | §II Overview: Questions, propositions, and topics |
| 04-sec01-most-direct-path.md | plain text | 816 words | §1 R&D automation provides the most direct path to an intelligence explosion |
| 05-sec02-standard-definitions.md | plain text | 831 words | §2 Standard definitions of “superintelligence” conflate learning with competence |
| 06-sec03-services-not-systems.md | plain text | 1,377 words | §3 To understand AI prospects, focus on services, not implementations |
| 07-sec04-prescriptive-descriptive.md | plain text | 691 words | §4 The AI-services model includes both descriptive and prescriptive aspects |
| 08-sec05-implicitly-anthropomorphic.md | plain text | 1,104 words | §5 Rational-agent models place intelligence in an implicitly anthropomorphic frame |
| 09-sec06-not-maximizers.md | plain text | 1,432 words | §6 A system of AI services is not equivalent to a utility maximizing agent |
| 10-sec07-human-like.md | plain text | 1,317 words | §7 Training agents in human-like environments can provide useful, bounded services |
| 11-sec08-strong-optimization.md | plain text | 1,344 words | §8 Strong optimization can strongly constrain AI capabilities, behavior, and effects |
| 12-sec09-opaque-algorithms.md | plain text | 1,193 words | §9 Opaque algorithms are compatible with functional transparency and control |
| 13-sec10-automation-not-agents.md | plain text | 1,190 words | §10 R&D automation dissociates recursive improvement from AI agency |
| 14-sec11-agent-is-service-tech.md | plain text | 972 words | §11 Potential AGI-enabling technologies also enable comprehensive AI services |
| 15-sec12-agi-not-required.md | plain text | 1,601 words | §12 AGI agents offer no compelling value |
| 16-sec13-agi-complexity.md | plain text | 908 words | §13 AGI-agent models entail greater complexity than CAIS |
| 17-sec14-services-risks.md | plain text | 1,033 words | §14 The AI-services model brings ample risks |
| 18-sec15-development-oriented.md | plain text | 810 words | §15 Development-oriented models align with deeply-structured AI systems |
| 19-sec16-aggregated-experience.md | plain text | 1,920 words | §16 Aggregated experience and centralized learning support AI-agent applications |
| 20-sec17-e2e-reinforcement.md | plain text | 1,183 words | §17 End-to-end reinforcement learning is compatible with the AI-services model |
| 21-sec18-reinforcement-not-reward.md | plain text | 1,081 words | §18 Reinforcement learning systems are not equivalent to reward-seeking agents |
| 22-sec19-orthogonality-undercuts.md | plain text | 1,376 words | §19 The orthogonality thesis undercuts the generality of instrumental convergence |
| 23-sec20-oracle-collusion.md | plain text | 1,109 words | §20 Collusion among superintelligent oracles can readily be avoided |
| 24-sec21-broad-world.md | plain text | 1,371 words | §21 Broad world knowledge can support safe task performance |
| 25-sec22-approval-models.md | plain text | 824 words | §22 Machine learning can develop predictive models of human approval |
| 26-sec23-human-guidance.md | plain text | 1,388 words | §23 AI development systems can support effective human guidance |
| 27-sec24-human-oversight.md | plain text | 844 words | §24 Human oversight need not impede fast, recursive AI technology improvement |
| 28-sec25-optimized-advice.md | plain text | 976 words | §25 Optimized advice need not be optimized to induce its acceptance |
| 29-sec26-design-planning.md | plain text | 2,100 words | §26 Superintelligent-level systems can safely provide design and planning services |
| 30-sec27-competitive-pressures.md | plain text | 785 words | §27 Competitive pressures provide little incentive to transfer strategic decisions to AI systems |
| 31-sec28-biomedical-agi.md | plain text | 1,189 words | §28 Automating biomedical R&D does not require defining human welfare |
| 32-sec29-services-reframe-agi-role.md | plain text | 1,184 words | §29 The AI-services model reframes the potential roles of AGI agents |
| 33-sec30-risky-to-safe.md | plain text | 1,972 words | §30 Risky AI can help develop safe AI |
| 34-sec31-abilities-not-powers.md | plain text | 436 words | §31 Supercapabilities do not entail “superpowers” |
| 35-sec32-world-stability.md | plain text | 1,263 words | §32 Unaligned superintelligent agents need not threaten world stability |
| 36-sec33-ai-not-boxed.md | plain text | 677 words | §33 Competitive AI capabilities will not be boxed |
| 37-sec34-rd-centralization.md | plain text | 423 words | §34 R&D automation is compatible with both strong and weak centralization |
| 38-sec35-future-knowledge.md | plain text | 911 words | §35 Predictable aspects of future knowledge can inform AI safety strategies |
| 39-sec36-interim-desiderata.md | plain text | 950 words | §36 Desiderata and directions for interim AI safety guidelines |
| 40-sec37-neural-symbolic.md | plain text | 3,291 words | §37 How do neural and symbolic technologies mesh? |
| 41-sec38-broadly-competent.md | plain text | 847 words | §38 Broadly-capable systems coordinate narrower systems |
| 42-sec39-tiling-space.md | plain text | 2,875 words | §39 Tiling task-space with AI services can provide general AI capabilities |
| 43-sec40-pflop-capacity.md | plain text | 2,995 words | §40 Could 1 PFLOP/s systems exceed the basic functional capacity of the human brain? |
| 44-afterword.md | plain text | 217 words | Afterword |
| 45-acknowledgements.md | plain text | 3,798 words | Acknowledgements |
| VERIFICATION.md | plain text | 319 words | Verification record — Phase 1 (mechanical conversion) |
| full.md | plain text | 64,617 words | Reframing Superintelligence: Comprehensive AI Services as General Intelligence — K. Eric Drexler, FHI Technical Report #2019-1 (Markdown edition) |
| index.md | plain text | 2,483 words | Index — Reframing Superintelligence: Comprehensive AI Services as General Intelligence — K. Eric Drexler, FHI Technical Report #2019-1 (Markdown edition) |
| references.md | plain text | 3,477 words | References |
README — Reframing Superintelligence: Comprehensive AI Services as General Intelligence — K. Eric Drexler, FHI Technical Report #2019-1 (Markdown edition)
This folder is a model-friendly Markdown edition of the FHI technical report, converted mechanically from the author's LaTeX source (main.tex, 2019; 42 sections, 192 footnotes, ~54,000 words). The rendered PDF is authoritative for typography and page references: https://ora.ox.ac.uk/objects/uuid:9c05427a-6390-4b42-9c55-ee45f73a26ad (Oxford Research Archive).
Files
README.md— this file.index.md— table of contents with each section's thesis, figure list, and a ranking of most-cited sections.NN-secMM-label.md— one file per section (NN = file sequence, MM = section number as printed in the PDF; sections I and II are the introduction and overview; 1–40 are the topical sections). Front and back matter (abstract, preface, afterword, acknowledgements) have their own files.references.md— the works cited, Chicago author-date, as in the PDF.full.md— all of the above concatenated in reading order, for single-file upload (~474 KB).
Conventions
- Headers. Each section file opens with an edition line, the section's Thesis (the author's one-sentence summary, set as an epigraph in the PDF), Cites sections (sections this one cross-references in its body), and Cited by sections (sections whose bodies cross-reference this one). Further Reading lists at section ends are the author's and are excluded from these counts.
- Cross-references. The PDF hyperlinks a phrase to another section and adds a footnote naming it. Here the phrase is written
[phrase](→§N); the footnote is dropped when it only repeats the target. A bare cross-reference footnote (no linked phrase) appears as[→ §N *Title*]. - Footnotes are inlined at their point of attachment as
[fn: …], so a section file is self-contained. - Citations are inline author–date, e.g. (Bostrom 2014, 107); full entries are in
references.md. - Figures. Images are not included. Each figure appears as a blockquote with its number (as in the PDF) and caption, followed by an italic line giving the source image filename and marking what follows as editorial: "(Image
figN.pngin the source; not reproduced here. The description below is editorial — a model-written first pass — not the author's text.)" The description paragraphs follow in the same blockquote. Image filenames do not always match figure numbers (for example, Figure 6 isfig8.png). Descriptions are interpretive aids, not the author's text. Figure 5 is text-only in the source; its content is reproduced verbatim above the caption, which is followed instead by "(Text-only figure; content reproduced above.)" - §36 lacks the thesis epigraph formatting in the source (the sentence is a plain first paragraph in the PDF); it is placed in the header here like the others.
- Section numbering follows the PDF (I, II, then 1–40); subsection headings are unnumbered, as in the source.
- Emphasis, quotation marks, tables, and math follow the source; a few LaTeX math expressions remain in
$…$notation.
Known source issues (present in the PDF too)
- unresolved \SecRef{tacitly-biological} (no such section in source; renders as 'Section ??' in PDF)
- unresolved \hyperref[tacitly-biological]
- Figure references in §16 use numbering that does not match the report's figures. The §16 text "along the lines suggested by Fig. 1" and the Figure 7 caption "In a naïve scale-up of the scheme outlined in Fig. 1" both refer to the individual-agent learning diagram that this edition numbers Figure 6, not to Figure 1 ("Classes of intelligent systems", §I). This is probably numbering left over from drafts. The author's text is left unchanged. On the same pattern, the §16 sentence "Naïve scaling of the individual-agent model to multiple agents does not fundamentally alter this picture (Fig 2)" appears to refer to Figure 7. (Checked against this edition's text; the rendered PDF was not re-checked for these items.)
Provenance
Converted with pandoc 3.1.3 plus custom pre/post-processing; verified by sentence-level comparison against the LaTeX source (see VERIFICATION.md). Conversion scripts are retained by the editor.