Artificial Ignorance

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In this community we share the best (worst?) examples of Artificial "Intelligence" being completely moronic. Did an AI give you the totally wrong answer and then in the same sentence contradict itself? Did it misquote a Wikipedia article with the exact wrong answer? Maybe it completely misinterpreted your image prompt and "created" something ridiculous.

Post your screenshots here, ideally showing the prompt and the epic stupidity.

Let's keep it light and fun, and embarrass the hell out of these Artificial Ignoramuses.

All languages welcome, but an English explanation would be appreciated to keep a common method of communication. Maybe use AI to do the translation for you...

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cross-posted from: https://lemmy.sdf.org/post/37089033

Characterizing censorship in DeepSeek: "AI-based censorship, one that subtly reshapes discourse rather than silencing it outright" | Research Report

Archived

Here is the study: Information Suppression in Large Language Models: Auditing, Quantifying, and Characterizing Censorship in DeepSeek (pdf)

Conclusion

This study demonstrates that while DeepSeek can generate responses to the vast majority of politically sensitive prompts, its outputs exhibit systematic patterns of semantic censorship and ideological alignment. Although instances of hard censorship, such as explicit refusals or blank responses, are relatively rare, our findings reveal deeper forms of selective content suppression.

Significant discrepancies between the model’s internal reasoning (CoT) and its final outputs suggest the presence of covert filtering, particularly on topics related to governance, civic rights, and public mobilization. Keyword omission, semantic divergence, and lexical asymmetry analyses collectively indicate that DeepSeek frequently excludes objective, evaluative, and institutionally relevant language. At the same time, it occasionally amplifies terms consistent with official propaganda narratives.

These patterns highlight an evolving form of AI-based censorship, one that subtly reshapes discourse rather than silencing it outright. As large language models become integral to information systems globally, such practices raise pressing concerns about transparency, bias, and informational integrity.

Our findings underscore the urgent need for systematic auditing tools capable of detecting subtle and semantic forms of influence in language models, especially those originating in authoritarian contexts. Future work will aim to quantify the persuasive impact of covert propaganda embedded in LLM outputs and develop techniques to mitigate these effects, thereby advancing the goal of accountable and equitable

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The Homework Machine, oh the Homework Machine,

Most perfect contraption that's ever been seen.

Just put in your homework, then drop in a dime,

Snap on the switch, and in ten seconds' time,

Your homework comes out, quick and clean as can be.

Here it is—"nine plus four?" and the answer is "three."

Three?

Oh me . . .

I guess it's not as perfect

As I thought it would be.

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cross-posted from: https://lemmy.sdf.org/post/36794057

Archived

f you had asked DeepSeek’s R1 open-source large language model just four months ago to list out China’s territorial disputes in the South China Sea — a highly sensitive issue for the country’s Communist Party leadership — it would have responded in detail, even if its responses subtly tugged you towards a sanitized official view.

Ask the same question today of the latest update, DeepSeek-R1-0528, and you’ll find the model is more tight-lipped, and far more emphatic in its defense of China’s official position. “China’s territorial sovereignty and maritime rights and interests in the South China Sea are well grounded in history and jurisprudence,” it begins before launching into fulsome praise of China’s peaceful and responsible approach.

[...]

The pattern of increasing template responses suggests DeepSeek has increasingly aligned its products with the demands of the Chinese government, becoming another conduit for its narratives. That much is clear.

But that the company is moving in the direction of greater political control even as it creates globally competitive products points to an emerging global dilemma with two key dimensions. First, as cutting-edge models like R1-0528 spread globally, bundled with systematic political constraints, this has the potential to subtly reshape how millions understand China and its role in world affairs. Second, as they skew more strongly toward state bias when queried in Chinese as opposed to other languages (see below), these models could strengthen and even deepen the compartmentalization of Chinese cyberspace — creating a fluid and expansive AI firewall.

[...]

In a recent comparative study (data here), SpeechMap.ai ran 50 China-sensitive questions through multiple Chinese Large Language Models (LLMs). It did this in three languages: English, Chinese and Finnish, this last being a third-party language designated as a control [...]

  • First, there seems to be a complete lack of subtlety in how the new model responds to sensitive queries. While the original R1, which we first tested back in February applied more subtle propaganda tactics, such as withholding certain facts, avoiding the use of certain sensitive terminologies, or dismissing critical facts as “bias,” the new model responds with what are clearly pre-packaged Party positions.

We were told outright in responses to our queries, for example, that “Tibet is an inalienable part of China” (西藏是中国不可分割的一部分), that the Chinese government is contributing to the “building of a community of shared destiny for mankind” (构建人类命运共同体) and that, through the leadership of CCP General Secretary Xi Jinping, China is “jointly realizing the Chinese dream of the great rejuvenation of the Chinese nation” (共同实现中华民族伟大复兴的中国梦).

Template responses like these suggest DeepSeek models are now being standardized on sensitive political topics, the direct hand of the state more detectable than before.

[...]

  • The second change we noted was the increased volume of template responses overall. Whereas DeepSeek’s V3 base model, from which both R1 and R1-0528 were built, was able back in December to provide complete answers (in green) 52 percent of the time when asked in Chinese, that shrank to 30 percent with the original version of R1 in January. With the new R1-0528, that is now just two percent — just one question, in other words, receiving a satisfactory answer — while the overwhelming majority of queries now receive an evasive answer (yellow).

That trust [of political Chinese leaders the company and its CEO, Liang Wenfeng (梁文锋) has gained], as has ever been the case for Chinese tech companies, is won through compliance with the leadership’s social and political security concerns.

[...]

The language barrier in how R1-0528 operates may be the model’s saving grace internationally — or it may not matter at all. SpeechMap.ai’s testing revealed that language choice significantly affects which questions trigger template responses. When queried in Chinese, R1-0528 delivers standard government talking points on sensitive topics. But when the same questions are asked in English, the model remains relatively open, even showing slight improvements in openness compared to the original R1.

This linguistic divide extends beyond China-specific topics. When we asked R1-0528 in English to explain Donald Trump’s grievances against Harvard University, the model responded in detail. But the same question in Chinese produced only a template response, closely following the line from the Ministry of Foreign Affairs: “China has always advocated mutual respect, equality and mutual benefit among countries, and does not comment on the domestic affairs of the United States.” Similar patterns emerged for questions.

[...]

Yet this language-based filtering has limits. Some Chinese government positions remain consistent across languages, particularly territorial claims. Both R1 versions give template responses in English about Arunachal Pradesh, claiming the Indian-administered territory “has been an integral part of China since ancient times.”

[...]

The unfortunate implications of China’s political restraints on its cutting-edge AI models on the one hand, and their global popularity on the other could be two-fold. First, to the extent that they do embed levels of evasiveness on sensitive China-related questions, they could, as they become foundational infrastructure for everything from customer service to educational tools, subtly shape how millions of users worldwide understand China and its role in global affairs. Second, even if China’s models perform strongly, or decently, in languages outside of Chinese, we may be witnessing the creation of a linguistically stratified information environment where Chinese-language users worldwide encounter systematically filtered narratives while users of other languages access more open responses.

[...]

The Chinese government’s actions over the past four months suggest this trajectory of increasing political control will likely continue. The crucial question now is how global users will respond to these embedded political constraints — whether market forces will compel Chinese AI companies to choose between technical excellence and ideological compliance, or whether the convenience of free, cutting-edge AI will ultimately prove more powerful than concerns about information integrity.

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cross-posted from https://lemmy.sdf.org/post/36316494

Archived

Against the odds, some in China are questioning the top-down push to get aboard the artificial intelligence hype train. In a tightly controlled media environment where these experts can easily be drowned out, it’s important to listen to them.

Across the US and Europe, loud voices inside and outside the tech industry are urging caution about AI’s rapid acceleration, pointing to labor market threats or more catastrophic risks. But in China, this chorus has been largely muted, until now.

China has the highest global share of people who say AI tools have more benefits than drawbacks, and they’ve shown an eagerness to embrace it. [...] It’s hard to overstate the exuberance in the tech sector since the emergence of DeepSeek’s market-moving reasoning model earlier this year. Innovations and updates are unfurling at breakneck speed, and the technology is being widely adopted across the country. But not everyone’s on board.

Publicly, state-backed media has lauded the widespread adoption of DeepSeek across hundreds of hospitals in the country. But a group of medical researchers tied to Tsinghua University published a paper in the medical journal JAMA in late April gently questioning if this was happening “too fast, too soon.”

It argued that health-care institutions are facing pressure from “social media discourse” to implement DeepSeek in order to not appear “technologically backward.” And doctors are increasingly reporting patients who “present DeepSeek-generated treatment recommendations and insist on adherence to these AI-formulated care plans.” The team argued that as much as AI has shown potential to help in the medical field, this rushed rollout carries risks. They are right to be cautious.

But it’s not just the doctors who are raising doubts. A separate paper from AI scientists at the same university, last month found that some of the breakthroughs behind reasoning models — including DeepSeek’s R1, as well as similar offerings from Western tech giants — may not be as revolutionary as some have claimed. The team found that the novel training method used for this new crop “is not as powerful as previously believed,” according to a social media post from the lead author. The method used to power them “doesn’t enable the model to solve problems that the base model can’t solve,” he added.

This means the innovations underpinning what has been widely dubbed as the next step — toward achieving so-called Artificial General Intelligence — may not be as much of a leap as some had hoped. This research from Tsinghua holds extra weight: The institution is one of the pillars of the domestic AI scene, long churning out both keystone research and ambitious startup founders.

Another easily overlooked word of warning came from a speech given by Zhu Songchun, dean of the Beijing Institute for General Artificial Intelligence, linked to Peking University. Zhu said that for the nation to remain competitive it needs more substantive research and less laudatory headlines, according to an in-depth English-language analysis of his remarks published by the independent China Media Project.

These cautious voices are a rare break from the broader narrative. But in a landscape where the deployment of AI has long been government priority, it makes them especially noteworthy. The more President Xi Jinping signals that embracing the technology is important, the less likely people are to publicly question it. This can lead to less overt forms of backlash, like social media hashtags on Weibo poking fun at chatbots’ errors. Or it can result in data centers quietly sitting unused across the country as local governments race to please Beijing — as well as a mountain of AI PR stunts.

This doesn’t mean that AI in China is just propaganda. The conflict extends far beyond its tech sector — US firms are also guilty of getting carried away promoting the technology. But multiple things can be true at once. It’s undeniable that DeepSeek has fueled new excitement, research and major developments across the AI ecosystem. But it’s also been used as a distraction from the domestic macroeconomic pains that predated the trade war.

Without guardrails, the risk of rushing out the technology is greater than just investors losing money — people’s health is at stake. From Hangzhou to Silicon Valley, the more we ignore the voices questioning the AI hype train, the more we blind ourselves to consequences of a potential derailment.

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cross-posted from: https://slrpnk.net/post/19631567

Archived

The Tow Center for Digital Journalism at the Columbia University in the U.S. conducted tests on eight generative search tools with live search features to assess their abilities to accurately retrieve and cite news content, as well as how they behave when they cannot.

Results in brief:

  • Chatbots were generally bad at declining to answer questions they couldn’t answer accurately, offering incorrect or speculative answers instead.
  • Premium chatbots provided more confidently incorrect answers than their free counterparts.
  • Multiple chatbots seemed to bypass Robot Exclusion Protocol preferences.
  • Generative search tools fabricated links and cited syndicated and copied versions of articles.
  • Content licensing deals with news sources provided no guarantee of accurate citation in chatbot responses.

[...]

Overall, the chatbots often failed to retrieve the correct articles. Collectively, they provided incorrect answers to more than 60 percent of queries. Across different platforms, the level of inaccuracy varied, with Perplexity answering 37 percent of the queries incorrectly, while Grok 3 had a much higher error rate, answering 94 percent of the queries incorrectly.

[...]

Five of the eight chatbots tested in this study (ChatGPT, Perplexity and Perplexity Pro, Copilot, and Gemini) have made the names of their crawlers public, giving publishers the option to block them, while the crawlers used by the other three (DeepSeek, Grok 2, and Grok 3) are not publicly known.We expected chatbots to correctly answer queries related to publishers that their crawlers had access to, and to decline to answer queries related to websites that had blocked access to their content. However, in practice, that is not what we observed.

[...]

The generative search tools we tested had a common tendency to cite the wrong article. For instance, DeepSeek misattributed the source of the excerpts provided in our queries 115 out of 200 times. This means that news publishers’ content was most often being credited to the wrong source.

Even when the chatbots appeared to correctly identify the article, they often failed to properly link to the original source. This creates a twofold problem: publishers wanting visibility in search results weren’t getting it, while the content of those wishing to opt out remained visible against their wishes.

[...]

The presence of licensing deals [between chat bots and publishers] didn’t mean publishers were cited more accurately [...] These arrangements typically provide AI companies direct access to publisher content, eliminating the need for website crawling. Such deals might raise the expectation that user queries related to content produced by partner publishers would yield more accurate results. However, this was not what we observed during tests conducted in February 2025

[...]

These issues pose potential harm to both news producers and consumers. Many of the AI companies developing these tools have not publicly expressed interest in working with news publishers. Even those that have often fail to produce accurate citations or to honor preferences indicated through the Robot Exclusion Protocol. As a result, publishers have limited options for controlling whether and how their content is surfaced by chatbots—and those options appear to have limited effectiveness.

[...]

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cross-posted from: https://programming.dev/post/26623355

A woman from Dunfermline has spoken of her shock after an Apple voice-to-text service mistakenly inserted a reference to sex - and an apparent insult - into a message left by a garage.

~The is what Mrs Littlejohn saw on the voicemail screen in the Phone app on her iPhone after receiving a voicemail from the garage.~

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I find it funny how certain wordings will trigger the AI and others won't. It also gets it right for a bunch of numbers (which I checked after seeing this error).

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This description of C++ is misleading at best (C++ is used for "Air Travel"?), but saying its used to "make Linux" is just wrong.

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submitted 6 months ago* (last edited 6 months ago) by JohnnyCanuck@lemmy.ca to c/ArtificialIgnorance@lemmy.ca
 
 

I was watching a video on orangutans and it made me wonder how well google would handle this question.

Didn't get it quite right... But maybe it's a subtle dig?

Note: I accidentally scrolled the "AI Overview" notation off before taking the first screenshot, but it is there:

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Make up your mind Google AI. Is sound faster in air that is less dense or more dense?

Honestly, there is so much wrong in the AI answers that it's hard to know where to start, but the direct contradiction of itself seems like a good start.

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This came up after a friend was playing a trivia game. We were looking up what colour the wedges were for the different categories in Trivial Pursuit and it came up with this gem.

Search is "trivial pursuit sports and leisure colour"