#Advanced AI Systems

[jcmarchi]

AI Doesn’t Necessarily Give Better Answers If You’re Polite

New Post has been published on https://thedigitalinsider.com/ai-doesnt-necessarily-give-better-answers-if-youre-polite/

AI Doesn’t Necessarily Give Better Answers If You’re Polite

Public opinion on whether it pays to be polite to AI shifts almost as often as the latest verdict on coffee or red wine – celebrated one month, challenged the next. Even so, a growing number of users now add ‘please’ or ��thank you’ to their prompts, not just out of habit, or concern that brusque exchanges might carry over into real life, but from a belief that courtesy leads to better and more productive results from AI.

This assumption has circulated between both users and researchers, with prompt-phrasing studied in research circles as a tool for alignment, safety, and tone control, even as user habits reinforce and reshape those expectations.

For instance, a 2024 study from Japan found that prompt politeness can change how large language models behave, testing GPT-3.5, GPT-4, PaLM-2, and Claude-2 on English, Chinese, and Japanese tasks, and rewriting each prompt at three politeness levels. The authors of that work observed that ‘blunt’ or ‘rude’ wording led to lower factual accuracy and shorter answers, while moderately polite requests produced clearer explanations and fewer refusals.

Additionally, Microsoft recommends a polite tone with Co-Pilot, from a performance rather than a cultural standpoint.

However, a new research paper from George Washington University challenges this increasingly popular idea, presenting a mathematical framework that predicts when a large language model’s output will ‘collapse’, transiting from coherent to misleading or even dangerous content. Within that context, the authors contend that being polite does not meaningfully delay or prevent this ‘collapse’.

Tipping Off

The researchers argue that polite language usage is generally unrelated to the main topic of a prompt, and therefore does not meaningfully affect the model’s focus. To support this, they present a detailed formulation of how a single attention head updates its internal direction as it processes each new token, ostensibly demonstrating that the model’s behavior is shaped by the cumulative influence of content-bearing tokens.

As a result, polite language is posited to have little bearing on when the model’s output begins to degrade. What determines the tipping point, the paper states, is the overall alignment of meaningful tokens with either good or bad output paths – not the presence of socially courteous language.

An illustration of a simplified attention head generating a sequence from a user prompt. The model starts with good tokens (G), then hits a tipping point (n*) where output flips to bad tokens (B). Polite terms in the prompt (P₁, P₂, etc.) play no role in this shift, supporting the paper’s claim that courtesy has little impact on model behavior. Source: https://arxiv.org/pdf/2504.20980

If true, this result contradicts both popular belief and perhaps even the implicit logic of instruction tuning, which assumes that the phrasing of a prompt affects a model’s interpretation of user intent.

Hulking Out

The paper examines how the model’s internal context vector (its evolving compass for token selection) shifts during generation. With each token, this vector updates directionally, and the next token is chosen based on which candidate aligns most closely with it.

When the prompt steers toward well-formed content, the model’s responses remain stable and accurate; but over time, this directional pull can reverse, steering the model toward outputs that are increasingly off-topic, incorrect, or internally inconsistent.

The tipping point for this transition (which the authors define mathematically as iteration n*), occurs when the context vector becomes more aligned with a ‘bad’ output vector than with a ‘good’ one. At that stage, each new token pushes the model further along the wrong path, reinforcing a pattern of increasingly flawed or misleading output.

The tipping point n* is calculated by finding the moment when the model’s internal direction aligns equally with both good and bad types of output. The geometry of the embedding space, shaped by both the training corpus and the user prompt, determines how quickly this crossover occurs:

An illustration depicting how the tipping point n* emerges within the authors’ simplified model. The geometric setup (a) defines the key vectors involved in predicting when output flips from good to bad. In (b), the authors plot those vectors using test parameters, while (c) compares the predicted tipping point to the simulated result. The match is exact, supporting the researchers’ claim that the collapse is mathematically inevitable once internal dynamics cross a threshold.

Polite terms don’t influence the model’s choice between good and bad outputs because, according to the authors, they aren’t meaningfully connected to the main subject of the prompt. Instead, they end up in parts of the model’s internal space that have little to do with what the model is actually deciding.

When such terms are added to a prompt, they increase the number of vectors the model considers, but not in a way that shifts the attention trajectory. As a result, the politeness terms act like statistical noise: present, but inert, and leaving the tipping point n* unchanged.

The authors state:

‘[Whether] our AI’s response will go rogue depends on our LLM’s training that provides the token embeddings, and the substantive tokens in our prompt – not whether we have been polite to it or not.’

The model used in the new work is intentionally narrow, focusing on a single attention head with linear token dynamics – a simplified setup where each new token updates the internal state through direct vector addition, without non-linear transformations or gating.

This simplified setup lets the authors work out exact results and gives them a clear geometric picture of how and when a model’s output can suddenly shift from good to bad. In their tests, the formula they derive for predicting that shift matches what the model actually does.

Chatting Up..?

However, this level of precision only works because the model is kept deliberately simple. While the authors concede that their conclusions should later be tested on more complex multi-head models such as the Claude and ChatGPT series, they also believe that the theory remains replicable as attention heads increase, stating*:

‘The question of what additional phenomena arise as the number of linked Attention heads and layers is scaled up, is a fascinating one. But any transitions within a single Attention head will still occur, and could get amplified and/or synchronized by the couplings – like a chain of connected people getting dragged over a cliff when one falls.’

An illustration of how the predicted tipping point n* changes depending on how strongly the prompt leans toward good or bad content. The surface comes from the authors’ approximate formula and shows that polite terms, which don’t clearly support either side, have little effect on when the collapse happens. The marked value (n* = 10) matches earlier simulations, supporting the model’s internal logic.

What remains unclear is whether the same mechanism survives the jump to modern transformer architectures. Multi-head attention introduces interactions across specialized heads, which may buffer against or mask the kind of tipping behavior described.

The authors acknowledge this complexity, but argue that attention heads are often loosely-coupled, and that the sort of internal collapse they model could be reinforced rather than suppressed in full-scale systems.

Without an extension of the model or an empirical test across production LLMs, the claim remains unverified. However, the mechanism seems sufficiently precise to support follow-on research initiatives, and the authors provide a clear opportunity to challenge or confirm the theory at scale.

Signing Off

At the moment, the topic of politeness towards consumer-facing LLMs appears to be approached either from the (pragmatic) standpoint that trained systems may respond more usefully to polite inquiry; or that a tactless and blunt communication style with such systems risks to spread into the user’s real social relationships, through force of habit.

Arguably, LLMs have not yet been used widely enough in real-world social contexts for the research literature to confirm the latter case; but the new paper does cast some interesting doubt upon the benefits of anthropomorphizing AI systems of this type.

A study last October from Stanford suggested (in contrast to a 2020 study) that treating LLMs as if they were human additionally risks to degrade the meaning of language, concluding that ‘rote’ politeness eventually loses its original social meaning:

[A] statement that seems friendly or genuine from a human speaker can be undesirable if it arises from an AI system since the latter lacks meaningful commitment or intent behind the statement, thus rendering the statement hollow and deceptive.’

However, roughly 67 percent of Americans say they are courteous to their AI chatbots, according to a 2025 survey from Future Publishing. Most said it was simply ‘the right thing to do’, while 12 percent confessed they were being cautious – just in case the machines ever rise up.

* My conversion of the authors’ inline citations to hyperlinks. To an extent, the hyperlinks are arbitrary/exemplary, since the authors at certain points link to a wide range of footnote citations, rather than to a specific publication.

First published Wednesday, April 30, 2025. Amended Wednesday, April 30, 2025 15:29:00, for formatting.

[mollypaup]

listening to a streamer whose political opinions i find mildly irritating sometimes and suddenly snapping into focus at the words "population collapse" like 'please don't launch into an ecofascist rant'

[tempesttz]

unnecessary rant about an

unnecessary (but very odd) debate

i know this is not my usual post but tumblr just suggested me another account (as it does), so i went to check them out. openly endogenic, wonderful. inclusive, check. "if i block you, you're probably not inclusive enough." sure? then i scrolled down a bit more, and... tw: mentions of discourse, nazis, the holocaust, and ai art. i'm also just not very nice, so tw for that too.

".....ai art is still art and arguing otherwise is spreading nazi bullshit regardless of if you personally like it or not...." ....what? at this point i'm thinking okay, op has no clue what a nazi is or something. right? right??? there was a link, so i, an unwitting fool looking for more elaboration on this take, clicked it.

"blocked a long time follower because they were being reactionary. here is your reminder that regardless of your stance on copyright, as soon as you start regurgitating that ai art isn't art, you are spreding the rhetoric authoritarianism. you are being reactionary and conservative. in fact, you are literally spreading nazi shit. read up if you have the spoons for it: link here. the focus should be on mitigating harm to those more directly impacted, not on trying to erase the art now exist.s not on ai arts legitimacy as art." i'm not going to just sit here and say "oh wow weird take, point and laugh guys." that would be weird and frankly no better than places like r/fdc and r/systemscringe. so instead, we're gonna break this down: first off: what is degenerate art? well, let's check their wikipedia link.

"Degenerate art (German: Entartete Kunst) was a term adopted in the 1920s by the Nazi Party in Germany to describe modern art. During the dictatorship of Adolf Hitler, German modernist art, including many works of internationally renowned artists, was removed from state-owned museums and banned in Nazi Germany on the grounds that such art was an "insult to German feeling", un-German, Freemasonic, Jewish, or Communist in nature. Those identified as degenerate artists were subjected to sanctions that included being dismissed from teaching positions, being forbidden to exhibit or to sell their art, and in some cases being forbidden to produce art." okay, so op is claiming that dislike of ai art is comparable to the suppressing and banning of large amounts of art in nazi germany. which is a wild take. but why is it wild? 1. ai generators clearly do not experience much suppression or banning in places considering that they are an active threat to artists. 2. the main issue with ai image generation is that it is stealing from actual artists to create their images and putting people who have trained for years to hone their skills at risk of losing their jobs. this diminishes the amount of artists who will actually pursue a career in that field, thereby reducing the amount of actual artists and directly harming the art community. 3. a lot of people will lie about being ai "artists," attempting to claim the work as actual art. 4. the concept of comparing something like this to the holocaust in general is just... wildly insensitive, frankly. this should be common sense, but there seems to be a distinct lack of it here anyways. 5. people are allowed to have opinions? you can think ai art is a valid form of art. i'll think you're weird, but that's a valid opinion. some people don't think certain genres of music are art. some people don't think certain kinds of art should be considered art. for example, those pendulum paintings that were everywhere, and might still be everywhere. i saw a lot of discourse about those. some people did not consider them to be art, or at least not on par with things like large, dedicated paintings. does that make the people who have that opinion nazis? .....no????? there isn't a moral to this post. it probably shouldn't exist. i just saw this and needed to rant, and decided to make you all my unwitting victims, lol. if you agree with op, then... i don't know, have a nice day? maybe stop conflating something like ai art (which is basically inconsequential unless you are in a community it effects or witnessing a downgrade of media quality due to its usage in production) to the holocaust (one of if not the worst historical event to this day in history)? okay wait, i have a moral! you can dislike things or have an opinion without it having to be taken to the total extreme. for anyone who read through this entire thing, thank you for sticking around! have a cookie. 🍪

[icicleteeth]

Every time a post either mentions TES 6 or makes me think about TES 6 I'm that spongebob meme of the guy with the spear, stopping myself from blackpilling all over again....

[demonstars]

he’s on the right path for the wrong reason?

more like the wrong path for the right logic

[darshanan-blog]

Automotive and Mobility Track at #TiEcon 2025 - Future is here

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[amrutmnm]

The Role of AI in Evolving Surface Radar Systems

The Surface Radars Market is experiencing a transformative shift, driven by the integration of Artificial Intelligence (AI). This evolution is enhancing the capabilities of surface radar systems, leading to improved detection, surveillance, and operational efficiency. This article explores how AI is reshaping the surface radars market, the benefits it brings, and the future implications for defense and civil applications.

Understanding Surface Radars

Surface radars are pivotal in defense and civil operations, providing high-performance detection, surveillance, and communication capabilities. They play a crucial role in real-time threat detection, border security, air defense, naval surveillance, and the protection of strategic infrastructure. The demand for advanced surface radar systems has been on the rise, driven by the need for enhanced security measures and the modernization of military capabilities.

The Role of AI in Surface Radar Systems

The integration of AI into surface radar systems marks a significant advancement in radar technology. AI algorithms enable the processing of vast amounts of radar data in real-time, providing immediate and meaningful insights that empower decision-making. This integration enhances object detection and classification, allowing radar systems to differentiate between various objects with greater accuracy. In defense applications, this means improved identification of potential threats, leading to more effective responses.

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Enhancing Object Detection and Classification

AI-driven radar analytics have revolutionized object detection and classification. Machine learning algorithms process extensive radar data, enabling systems to distinguish between different objects such as vehicles, aircraft, and unmanned aerial vehicles (UAVs). This capability is particularly beneficial in defense scenarios, where accurately identifying potential threats is critical. By analyzing historical and real-time data, AI can predict future movements and anticipate events, enhancing situational awareness and response strategies.

Predictive Maintenance and System Reliability

AI contributes significantly to predictive maintenance in radar systems. By analyzing operational data, AI algorithms can identify anomalies and predict potential system failures before they occur. This proactive approach ensures optimal performance and longevity of radar assets, reducing downtime and maintenance costs. For instance, AI-powered systems can monitor the health of radar components, alerting operators to issues that may require attention, thereby enhancing overall system reliability.

AI-Driven Radar Analytics in Surveillance

In surveillance applications, AI-driven radar analytics enhance the detection and tracking of objects over vast areas. This capability is vital for border security, coastal surveillance, and the protection of critical infrastructure. AI-enhanced radar systems provide real-time monitoring and accurate threat detection, enabling timely interventions and bolstering national security measures.

Integration with Unmanned Surface Vehicles (USVs)

The integration of AI-powered surface radars with Unmanned Surface Vehicles (USVs) is projected to achieve significant growth. USVs equipped with advanced radar systems benefit from enhanced detection, tracking, and surveillance functionalities. AI enables these autonomous vehicles to navigate complex maritime environments, perform critical missions such as intelligence, surveillance, and reconnaissance (ISR), mine detection, and anti-submarine warfare, thereby increasing their operational efficiency and effectiveness.

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Challenges and Considerations

Despite the advancements, integrating AI into surface radar systems presents challenges. Ensuring the security and reliability of AI algorithms is paramount, as vulnerabilities could be exploited, leading to compromised radar operations. Additionally, the complexity of AI systems requires substantial investment in research and development, as well as specialized expertise to implement and maintain these technologies effectively.

Future Outlook

The future of the Surface Radars Market is closely tied to ongoing advancements in AI and machine learning. As AI technologies continue to evolve, we can anticipate further enhancements in radar capabilities, including improved accuracy, faster data processing, and more sophisticated predictive analytics. These developments will not only strengthen defense operations but also expand the applications of surface radars in civil sectors such as air traffic control, weather monitoring, and disaster management.

The integration of AI into the Surface Radars Market represents a significant leap forward in radar technology. By enhancing detection capabilities, enabling predictive maintenance, and improving surveillance operations, AI is set to play a pivotal role in the evolution of surface radar systems. As the demand for advanced security and surveillance solutions grows, the synergy between AI and surface radars will undoubtedly be a cornerstone in meeting these emerging challenges.

[ailatestupdate]

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[mkcecollege]

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To know more : https://mkce.ac.in/blog/advanced-robotics-as-the-next-frontier-in-engineering-automation/

[fusiondynamics]

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[likitakans]

AI and Health: New Technologies Paving the Way for Better Treatment

Artificial intelligence (AI) is expanding rapidly in the health sector, and it is revolutionizing our medical system. With the help of AI, new technologies are being developed that are not only helpful in accurately diagnosing diseases but are also playing an important role in personalized treatment and management.

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