“But for the kinds of AI techniques that are currently popular, and which rely more on big data than on reasoning, it’s a very serious issue.”
Our reading, the sentence that was checked:
The long-tail problem of rare events is a very serious issue for the currently popular AI techniques, which rely more on big data than on reasoning.
Assessment: Supported Evidence as of
Where and when Gary Marcus wrote it
Marcus on AI, Dear Elon Musk, here are five things you might want to consider about AGI, paragraph 10.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment marcus-dear-elon-musk-here-are-five-things-a53d89-a1
We recorded 21 claims from this post. See every claim from this post.
The wording above is the version this site observed on 2026-10-28. The source may have been edited after 2022-05-31. This page does not claim the wording was captured then.
What the claim commits to
- Kind
- Observation
- Deadline, as the speaker wrote it
- None stated. This site does not add one.
- Deadline state when assessed
- No horizon
- Deadline state on 2026-10-29
- No horizon (the release’s reference date)
Conditions
- for the kinds of AI techniques that are currently popular (scope, stated by the speaker)
From the speaker
- AI techniques that are currently popular
- rely more on big data than on reasoning
- a very serious issue
Our interpretation
- The surrounding text identifies the issue as poor handling of rare cases with little training data.
- The ordinary reading concerns mainstream data-driven deep learning, not every AI method or an assertion that neural networks cannot reason at all.
- Very serious means substantial performance or reliability limitations, not a claim that the problem is impossible to mitigate.
- For this continuously reported technical field, current is operationalized as the 2022 state of the art up to the post date, rather than an accounting quarter.
Counts as supported if: Contemporary research documents substantial deficiencies on rare classes or situations in mainstream deep-learning systems, despite stronger performance where training data are plentiful.
Counts as contradicted if: Representative contemporary evaluations show that rare-case limitations are generally small or robustly resolved in the relevant methods.
Retrospective: these criteria were written after the outcome was public.
Current assessment
Was it true for the period described?
Outcome: Supported
The dated evidence matches what the claim committed to, under the criteria frozen before grading.
Scored on a plain reading. The speaker named no measure for this claim, so it was judged on the reading an informed reader would take, stated below under our interpretation. Both reviews had to find that reading fair.
- Evidence as of
- Deadline state when assessed
- No horizon
- Assessment recorded
- Policy version
- 2026-10-28.7
Rationale
A research synthesis publicly available four days before the post identifies poor rare-class performance as a substantial limitation of conventional deep learning. That supports the ordinary reading of a serious practical issue. Existing mitigation methods show progress, so the evidence does not support stronger claims of universal failure or impossibility.
Limitations
- Evidence is strongest for visual recognition.
- Seriousness is qualitative rather than tied to a speaker-specified threshold.
- The comparison with reasoning is a description of the targeted methods, not a separately established ranking of all AI approaches.
Evidence (2 items)
Supports the claim: International Journal of Computer Vision; author manuscript rendered by ar5iv,
Using conventional methods (e.g., Cross-Entropy loss, or simple fine-tuning, etc.) will result in the poor performance of the tail classes.
Why it matters here: The contemporary synthesis documents poor learning of rare classes and substantial limitations in conventional deep-learning methods. It also reviews mitigation methods, supporting a serious practical problem without implying that every method fails.
- Publisher
- International Journal of Computer Vision; author manuscript rendered by ar5iv
- Kind of source
- Peer-reviewed paper
- Describes events of
- Public from
Context only: arXiv,
Experiments on long-tailed CIFAR, ImageNet, Places, and iNaturalist 2018 manifest the new state-of-the-art for long-tailed recognition.
Why it matters here: PaCo demonstrates progress on several long-tailed benchmarks using changes within deep learning. This qualifies any stronger interpretation that the problem had no useful mitigation, but does not show it was generally solved in 2022.
- Where
- Abstract
- Publisher
- arXiv
- Kind of source
- Preprint, not peer reviewed
- Describes events of
- Public from
Where it was said (1)
Said once in the sources this register covers.
: Marcus on AI, Dear Elon Musk, here are five things you might want to consider about AGI
But for the kinds of AI techniques that are currently popular, and which rely more on big data than on reasoning, it’s a very serious issue.
- Where
- paragraph 10
- Published
- 2022-05-31
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment marcus-dear-elon-musk-here-are-five-things-a53d89-a1: Current. Outcome: Supported. Evidence as of 2026-10-28. Public date not recorded.
Recorded events
Assessment publishedBy this site
First assessment published after both reviews and the final-text check passed.
About assessment marcus-dear-elon-musk-here-are-five-things-a53d89-a1.