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Ngo's report of August 2022 states that by default AGIs seem likely to end up pursuing goals undesirable to humans, and that the lack of formal frameworks for describing alignment problems is a major reason to expect them to be difficult to solve. Copy claim link
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Machine-checked, not reviewed by an editor Stated in source Supported
Supporting evidence: primary · 1 source; Context: primary · 1 source
Date 2022-08-30 · publication Records dated 2022 → Role Articulation Register (kind of text) Nonfiction Scope and limits The author states that the argument is uncertain and that problems of the earlier phases are more likely to be solved by default as machine learning progresses; the record is the report's argument, not evidence that alignment is unsolved.
The evidence 03 primary source Supports the claim
The Alignment Problem from a Deep Learning Perspective ↗ arXiv · 2022-08-30
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Source excerpt “By default, it seems likely to me that AGIs will end up pursuing goals which are undesirable to us, rather than consistently following our intentions.” Location in source Section 2, PDF p. 3
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-31
Source details and check notes Authors Richard Ngo Version arXiv:2209.00626v1, submitted 30 August 2022 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 9503e506974e64d75506c4c0177779f217f1662b905aaa603bf35cb26bbb9192). The author list is the byline as printed on the first page. Version date 2022-08-30 Canonical address Open the canonical source ↗ Accessed 2026-10-28 Language English (en) Attribution Richard Ngo, “The Alignment Problem from a Deep Learning Perspective”, 2022-08-30. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, arguments about catastrophic risk from advanced AI (research/risk-debate). The quotation (25 words) matched the text retrieved for the candidate pack on 2026-10-29. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. A cross-family machine check (2026-10-31, item 58) answered the record’s question blind and agreed with its stance. No human editorial review. primary source Supports the claim
The Alignment Problem from a Deep Learning Perspective ↗ arXiv · 2022-08-30
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Source excerpt “our lack of formal frameworks for describing alignment problems is a major reason to expect them to be difficult to solve” Location in source Section 1, PDF p. 2
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-31
Source details and check notes Authors Richard Ngo Version arXiv:2209.00626v1, submitted 30 August 2022 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 9503e506974e64d75506c4c0177779f217f1662b905aaa603bf35cb26bbb9192). The author list is the byline as printed on the first page. Version date 2022-08-30 Canonical address Open the canonical source ↗ Accessed 2026-10-28 Language English (en) Attribution Richard Ngo, “The Alignment Problem from a Deep Learning Perspective”, 2022-08-30. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, arguments about catastrophic risk from advanced AI (research/risk-debate). The quotation (21 words) matched the text retrieved for the candidate pack on 2026-10-29. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. A cross-family machine check (2026-10-31, item 58) answered the record’s question blind and agreed with its stance. No human editorial review. primary source Context only
The Alignment Problem from a Deep Learning Perspective ↗ arXiv · 2022-08-30
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Cite the original source Cite the original document, not Selekoa. Check these details against it.
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Source excerpt “I expect that the problems in the earlier phases are more likely to be solved by default as the field of ML progresses” Location in source Section 3, PDF p. 13
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-31
Source details and check notes Authors Richard Ngo Version arXiv:2209.00626v1, submitted 30 August 2022 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 9503e506974e64d75506c4c0177779f217f1662b905aaa603bf35cb26bbb9192). The author list is the byline as printed on the first page. Version date 2022-08-30 Canonical address Open the canonical source ↗ Accessed 2026-10-28 Language English (en) Attribution Richard Ngo, “The Alignment Problem from a Deep Learning Perspective”, 2022-08-30. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, arguments about catastrophic risk from advanced AI (research/risk-debate). The quotation (23 words) matched the text retrieved for the candidate pack on 2026-10-29. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. A cross-family machine check (2026-10-31, item 58) answered the record’s question blind and agreed with its stance. No human editorial review. Status and meaning Editorial status. Not reviewed by an editor . An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.
Relation. A passage of the document meets the proposition’s criterion. The record documents what the text states, in its register (fiction and satire are labelled); it is not evidence that the proposition is true and implies no influence on or from another text.
Date. Date of the arXiv v1 submission.
Record ID rd-r06-ngo-2022-alignment-problem-deep-learning