Contents of /works/2019-reframing-superintelligence/edition/

NameFormSizeTitle
README.mdplain text651 wordsREADME — Reframing Superintelligence: Comprehensive AI Services as General Intelligence — K. Eric Drexler, FHI Technical Report #2019-1 (Markdown edition)
00-abstract.mdplain text401 wordsAbstract
01-preface.mdplain text373 wordsPreface
02-secI-intro1.mdplain text2,062 words§I Introduction: From R&D automation to comprehensive AI Services
03-secII-intro2.mdplain text3,170 words§II Overview: Questions, propositions, and topics
04-sec01-most-direct-path.mdplain text816 words§1 R&D automation provides the most direct path to an intelligence explosion
05-sec02-standard-definitions.mdplain text831 words§2 Standard definitions of “superintelligence” conflate learning with competence
06-sec03-services-not-systems.mdplain text1,377 words§3 To understand AI prospects, focus on services, not implementations
07-sec04-prescriptive-descriptive.mdplain text691 words§4 The AI-services model includes both descriptive and prescriptive aspects
08-sec05-implicitly-anthropomorphic.mdplain text1,104 words§5 Rational-agent models place intelligence in an implicitly anthropomorphic frame
09-sec06-not-maximizers.mdplain text1,432 words§6 A system of AI services is not equivalent to a utility maximizing agent
10-sec07-human-like.mdplain text1,317 words§7 Training agents in human-like environments can provide useful, bounded services
11-sec08-strong-optimization.mdplain text1,344 words§8 Strong optimization can strongly constrain AI capabilities, behavior, and effects
12-sec09-opaque-algorithms.mdplain text1,193 words§9 Opaque algorithms are compatible with functional transparency and control
13-sec10-automation-not-agents.mdplain text1,190 words§10 R&D automation dissociates recursive improvement from AI agency
14-sec11-agent-is-service-tech.mdplain text972 words§11 Potential AGI-enabling technologies also enable comprehensive AI services
15-sec12-agi-not-required.mdplain text1,601 words§12 AGI agents offer no compelling value
16-sec13-agi-complexity.mdplain text908 words§13 AGI-agent models entail greater complexity than CAIS
17-sec14-services-risks.mdplain text1,033 words§14 The AI-services model brings ample risks
18-sec15-development-oriented.mdplain text810 words§15 Development-oriented models align with deeply-structured AI systems
19-sec16-aggregated-experience.mdplain text1,920 words§16 Aggregated experience and centralized learning support AI-agent applications
20-sec17-e2e-reinforcement.mdplain text1,183 words§17 End-to-end reinforcement learning is compatible with the AI-services model
21-sec18-reinforcement-not-reward.mdplain text1,081 words§18 Reinforcement learning systems are not equivalent to reward-seeking agents
22-sec19-orthogonality-undercuts.mdplain text1,376 words§19 The orthogonality thesis undercuts the generality of instrumental convergence
23-sec20-oracle-collusion.mdplain text1,109 words§20 Collusion among superintelligent oracles can readily be avoided
24-sec21-broad-world.mdplain text1,371 words§21 Broad world knowledge can support safe task performance
25-sec22-approval-models.mdplain text824 words§22 Machine learning can develop predictive models of human approval
26-sec23-human-guidance.mdplain text1,388 words§23 AI development systems can support effective human guidance
27-sec24-human-oversight.mdplain text844 words§24 Human oversight need not impede fast, recursive AI technology improvement
28-sec25-optimized-advice.mdplain text976 words§25 Optimized advice need not be optimized to induce its acceptance
29-sec26-design-planning.mdplain text2,100 words§26 Superintelligent-level systems can safely provide design and planning services
30-sec27-competitive-pressures.mdplain text785 words§27 Competitive pressures provide little incentive to transfer strategic decisions to AI systems
31-sec28-biomedical-agi.mdplain text1,189 words§28 Automating biomedical R&D does not require defining human welfare
32-sec29-services-reframe-agi-role.mdplain text1,184 words§29 The AI-services model reframes the potential roles of AGI agents
33-sec30-risky-to-safe.mdplain text1,972 words§30 Risky AI can help develop safe AI
34-sec31-abilities-not-powers.mdplain text436 words§31 Supercapabilities do not entail “superpowers”
35-sec32-world-stability.mdplain text1,263 words§32 Unaligned superintelligent agents need not threaten world stability
36-sec33-ai-not-boxed.mdplain text677 words§33 Competitive AI capabilities will not be boxed
37-sec34-rd-centralization.mdplain text423 words§34 R&D automation is compatible with both strong and weak centralization
38-sec35-future-knowledge.mdplain text911 words§35 Predictable aspects of future knowledge can inform AI safety strategies
39-sec36-interim-desiderata.mdplain text950 words§36 Desiderata and directions for interim AI safety guidelines
40-sec37-neural-symbolic.mdplain text3,291 words§37 How do neural and symbolic technologies mesh?
41-sec38-broadly-competent.mdplain text847 words§38 Broadly-capable systems coordinate narrower systems
42-sec39-tiling-space.mdplain text2,875 words§39 Tiling task-space with AI services can provide general AI capabilities
43-sec40-pflop-capacity.mdplain text2,995 words§40 Could 1 PFLOP/s systems exceed the basic functional capacity of the human brain?
44-afterword.mdplain text217 wordsAfterword
45-acknowledgements.mdplain text3,798 wordsAcknowledgements
VERIFICATION.mdplain text319 wordsVerification record — Phase 1 (mechanical conversion)
full.mdplain text64,617 wordsReframing Superintelligence: Comprehensive AI Services as General Intelligence — K. Eric Drexler, FHI Technical Report #2019-1 (Markdown edition)
index.mdplain text2,483 wordsIndex — Reframing Superintelligence: Comprehensive AI Services as General Intelligence — K. Eric Drexler, FHI Technical Report #2019-1 (Markdown edition)
references.mdplain text3,477 wordsReferences

README.md · plain text: README.md

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).

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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.