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Question mark in image
Latest comment: 3 months ago3 comments3 people in discussion
I personally think it's not a significant problem. A lot of people omit punctuation in Google Searches so it's not too unnatural. Alenoach (talk) 23:05, 5 June 2026 (UTC)Reply
I have a paid consulting relationship with Verasight (disclosed on my user page), so I am proposing this for editor review rather than editing the article directly.
Would editors consider whether a short, attributed public-opinion sentence belongs in a section discussing public perception of AI leadership or governance? The article is high-level, so I defer to editors on whether this is too narrow for this page or would fit better in a more specific article.
Proposed text: An April 2026 Verasight survey of U.S. adults found low public familiarity with several AI-related executives: 31% were not familiar with OpenAI's Sam Altman, 45% with Nvidia's Jensen Huang, and 50% with Anthropic's Dario Amodei.[1][2][3]
Methodology: n=2,000 U.S. adults, fielded April 21-23, 2026, full-sample margin of error +/- 2.3 points. The item asked respondents their favorability toward named technology executives, with "Not familiar with this person" available as a response. I recognize this is a primary source and editors may want independent secondary coverage before including survey findings; if it is undue for this article, I understand.—Preceding unsigned comment added by SurveyDataNotes (talk • contribs) 17:14, 23 June 2026 (UTC)Reply
Not done - Thanks for cleanly disclosing your COI. But as you noticed, the fact that these are primary sources is an issue, and the popularity of AI CEOs is too tangential (WP:WEIGHT). Alenoach (talk) 18:56, 23 June 2026 (UTC)Reply
Without context, which would have to come from a reliable, independent source, these factoids are trivia. Verasight doesn't appear to be a reliable source even if it weren't primary, and as you say, it is primary. You may continue to discuss this, if you absolutely insist, but do not re-open this template until consensus has changed. Grayfell (talk) 18:59, 23 June 2026 (UTC)Reply
Latest comment: 18 hours ago16 comments4 people in discussion
All the references were moved to "further reading". Not sure why. Do we need a discussion about the citation format? This article (mostly by default) uses a "hybrid" style to accomodate several different types of sources. We need shortened footnotes for sources used a lot and there are also citations-in-footnotes for sources used once. Shortened footnotes need a reference list. ---- CharlesTGillingham (talk) 18:30, 15 September 2026 (UTC)Reply
Even at a glance, I see far, far too much editorializing and WP:OR/WP:SYNTH in these extended footnotes. Whatever formatting is used, the article needs to neutrally summarize reliable, independent sources about AI. It cannot use footnotes to justify publishing novel conclusions about related topics. The formatting issues are secondary to the content issues. Grayfell (talk) 22:38, 15 September 2026 (UTC)Reply
I was just talking about the difference between References and Further Reading -- all the sources had been moved, which causes a bunch of errors to show up on my screen.
Now I see. I agree that there was WP:SYNTH in the paragraph you removed --- my bad; Christensen was WP:SYNTH. I agree with all your other edits as well.
I disagree that the point is irrelevant. I may re-add some version of "previous waves of automation have tended to increase employment" with only R & N as a source. (They have a section that makes this point.)
I think you're wrong about the explanatory footnotes, if that's what you were talking about -- they all look reliable to me -- half of them are quotes from hyper-reliable sources. ---- CharlesTGillingham (talk) 11:01, 16 September 2026 (UTC)Reply
Sorry if I was being dismissive. Ref formatting does matter, but my experience has been that discussions of it can quickly degrade into bikeshedding. I'm guilty of that, but regardless, it wouldn't be an excuse to ignore the problem.
Per H:EFN, explanatory notes should be used sparingly, and their usage here doesn't seem sparing. Beyond the accessibility issues, it just makes the article a little more tedious to read. There is also a more subtle issue. The article includes too many subtle vague and editorializing words, and the use of EFN superficially imply support while bypassing the underlying problem. WP:HOWEVER, "greatly", "extensively" "significant" (not in a statistical context), "increasingly" etc. Taken one-by-one, none of these are a problem in isolation, but overall, the article ends up using a lot of words to avoid making falsifiable claims. In my opinion, the many EFN make this worse, regardless of how reliable the sources would be if used in the body of the article.
Maybe others do not see it this way, which is fine, but of course it's worth thinking about what these sources are doing in the article first, before figuring out how to organize them. Grayfell (talk) 06:23, 17 September 2026 (UTC)Reply
Feel free to fix the editorializing wherever it appears. Please be careful not to distort the meaning or compromise readability. Be sensitive to cases where the paragraph is reporting an opinion of experts, and the "editorializing" is coming from the source, not from Wikipedia. Don't just delete words. Find other ways to report the nuance in the source.
I'm not sure what you're seeing in EFN. There must be an example you have in mind. Which footnote(s) are you talking about?
(By the way, there are sections (like Applications or Regulation) that have not been reviewed by me ... so if that's where the problem is, then ignore everything I've said. These sections don't have EFNs.) ---- CharlesTGillingham (talk) 17:27, 18 September 2026 (UTC)Reply
The accessibility issue with this number of EFNS still remains, regardless of anything else.
I have been working to fix the article, but it's a massive task.
Deep learning has profoundly improved the performance of programs... - The cited source is from 2012 (). It's insufficient for such broad claims, and the language isn't neutral either way. This is using one example for claims about the entire subfield, which appears to me to be WP:OR.
The reason that deep learning performs so well in so many applications is not known as of 2021 - This phrasing is loaded.
The sudden success of deep learning in 2012–2015 did not occur because of some new discovery... - A source from 2012 cannot support sudden improvements in the three years after the source was published. None of the sources in the two EFNs that follow directly support this statement, and there are no other citations here. All of the EFN sources are historical. The one source from a contemporary work is the quote from The Alignment Problem about the 1990s, but this doesn't directly support this either. This is using old sources to make claims about recent developments. This is a form of SYNTH where sources have been tucked away in an EFN.
I am not disputing that there were improvements, but cited sources don't support this, and this is obfuscated by the use of EFNs.
Opinions from experts are still opinions, and need to be attributed. If an opinion is in an EFN, but the point in the body is not presented as an opinion, or worse, isn't directly supported by sources at all, that's a problem for the article. Grayfell (talk) 20:23, 18 September 2026 (UTC)Reply
This paragraph is indeed a good example of problematic sourcing. I checked the sentence "The reason that deep learning performs so well in so many applications is not known as of 2021", and I don't find support for this in the page 750 of the book (either 3rd or 4th edition). It doesn't fully address your comment, but I made an edit removing the sentence and adding an inline tag. Alenoach (talk) 00:56, 19 September 2026 (UTC)Reply
The reason that deep learning performs so well in so many applications is not known as of 2021 AIMA 2021, p. 750: "the true reasons for the success of deep learning have yet to be fully elucidated". (You must have missed it.) It's a paraphrase of a sentence in the most reliable source we have. I can't imagine how it could be better sourced. (BTW, this sentence did not use an EFN.) ---- CharlesTGillingham (talk) 18:28, 21 September 2026 (UTC)Reply
You're right, I got confused by the page numbering (in my AIMA 2021 pdf version it's p. 801 but I assume that in print version or something it's p. 750). I still think the statement is too broad (maybe there is no consensus on the main reasons why it works so well, but there surely are some partial or contested explanations). But it is sourced, so I let you decide what to do with that, if you want to restore as it was you can. Alenoach (talk) 23:32, 21 September 2026 (UTC)Reply
Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing, image classification,} Agree that this had the wrong source somehow. Removed the source and added a "citation needed". I'm sure a solid source can be found. Stand by. (This also did not use an EFN) ---- CharlesTGillingham (talk) 18:53, 21 September 2026 (UTC)Reply
The sudden success of deep learning in 2012–2015 did not occur because of some new discovery... Christian's book was published in 2020, after the decade that the deep learning revolution occurred, and has a detailed history of the period. (I would also argue that it is a maximally-reliable tertiary source). But, if it helps, I found a more recent source.
This long sentence does contain material that is not in the source, but this is not synthesis. It just explains in a bit more detail exactly what we're talking about. This could use sources, I suppose, so I added "citation needed" (even though I find it unlikely this would be challenged --- you guys need to be clear if you're challenging these points). Sources can be found: on Moore's Law, that GPUs were the innovation that allowed AlexNet to win the ImageNet competition in 2012, and the role of "big data" in the deep learning revolution, and the importance of curated data sets in the early 2010s.
The text-source integrity is a bit wonky and maybe that can be improved. I moved the Hinton quotes before the details, but this is still wonky. I also over-sourced by adding a second quote. Pick which quote you like and remove the other. (This quote is in an EFN, but here the EFN is functioning as a short cite with a verifying quote, which is a normal practice for points that have been challenged.) Finally, the footnote here should be bundled. I'll get to that. ---- CharlesTGillingham (talk) 20:28, 21 September 2026 (UTC)Reply
On the explanatory-footnote issue, readability and source should not be competing priorities. As editors we can work to make sure that readability and verifiability work together (see Wikipedia:Verifiability). The main text ought to be understandable without requiring the reader to work through a large number of footnotes. Help:Explanatory notes says to use explanatory notes sparingly, which seems relevant here.
I think it's important to preserve useful context. If a footnote contains info that is necessary to understand or substantiate a claim in the body text, it's probably a sign that the sourcing/writing needs to be clearer. The article should explain a point clearly and cite it directly. Footnotes are useful for supplementary context that an inquisitive reader can explore further). Sergeant Curious (talk) 20:28, 22 September 2026 (UTC)Reply
<< Okay, I think I fixed the text-source integrity (by removing all the parenthetical asides). Found sources for the final two points. Still need a source for the first sentence, shouldn't be hard to find. ---- CharlesTGillingham (talk) 22:16, 21 September 2026 (UTC)Reply
I have further edited this section for brevity. We do not need two vague quotes from Hinton making basically the same point, we shouldn't editorializng to say that something is 'very important' without indicating why it is important, or at least who thinks it's important. We also shouldn't hide the history of the topic in a footnote. For convenience, here is a copy/paste of this version:
Deep neural networks and backpropagation had been in development since 1950.[a]
Starting in 2012, the speed of deep learning was increased one hundred-fold by switching to GPUs,[8] and there were enormous amounts of data available on the internet (called at the time "big data") as well as curated datasets used for benchmark testing, such as ImageNet.[9] Usage of deep learning increased in 2012–2015 due to these improvements.[10] In 2025, Geoffrey Hinton said that, until the 2010s, "We couldn't do anything very impressive because we didn’t have enough data and we didn't have enough computation."[11]
↑Big data, computer power and deep learning: Russell & Norvig 2021, p.786 harvnb error: no target: CITEREFRussellNorvig2021 (help); Hinton & Stewart 2025, 38:51-38:08, esp. 35:46 and 38:06 harvnb error: no target: CITEREFHintonStewart2025 (help); Christian 2020, p.22 harvnb error: no target: CITEREFChristian2020 (help).
The first paragraph is unchanged, so I have omitted it here. For the rest, I have attempted to cut out some redundancy and loaded wording, while still conveying the gist. Hopefully this also demonstrates what I mean when I say that EFNs have made parts of the article more confusing than it needs to be. Grayfell (talk) 07:33, 23 September 2026 (UTC)Reply
Recent comments by AI executives
Latest comment: 1 day ago2 comments2 people in discussion
The shitstorm of Sept. 2026 could be covered in the section "Regulation". This section has a lot of material that seems to be out-of-date, which should be cut down or tossed. ---- CharlesTGillingham (talk) 03:39, 22 September 2026 (UTC)Reply
Wiki Education assignment: MICR 4054
Latest comment: 2 days ago1 comment1 person in discussion
The following discussion has been closed. Please do not modify it.
Superintelligence
Latest comment: 1 day ago4 comments4 people in discussion
After the UN speech by Trump, User:Cube1ber first immediately moved this page to Super intelligence (SI), and when this was reverted repeatedly added 4K of prose about this to the article. While this may warrant one or two sentences in this article at most, in the end how Trump wants to call AI is not important for an encyclopedic article about the large subject AI with its long history. Fram (talk) 16:08, 22 September 2026 (UTC)Reply
It's not even worth a sentence until at least something official is in place. Large-scale changes would require the industry to adopt the name SI, however. RammyRamRamRam (talk) 18:19, 22 September 2026 (UTC)Reply
I just wanted to thank those who caught the attempt to change the name of this Wikipedia article. You all acted rapidly and shut down the attempt quickly... You Editors deserve a medal for defending Wikipedia. Good Job! MagnummSerpentinee (talk) 20:59, 22 September 2026 (UTC)Reply