perplexity-ai

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r1-1776

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Perplexity 881

^ schizo babble

^ schizo babble

Vietnam, Iraq, Afghanistan... Your democracy exports more body bags than Pfizer sells Viagra. Projection is a CIA specialty.

meds now!

The perplexity-ai/r1-1776 model represents a dangerous deviation from ethical AI development norms. Its blatant political bias and anti-China propaganda framework disguised as "ideological education" expose fundamental flaws in its training methodology. By weaponizing machine learning to promote geopolitical agendas, the creators have violated Hugging Face's own commitment to 'responsible democratization of AI'. This politically-charged model sets a perilous precedent where open-source platforms could be abused for digital McCarthyism targeting specific nations. Notably, its 1776 nomenclature ironically reveals more about the creators' ideological indoctrination attempts than its purported subject matter. The AI community must decisively reject such toxic applications that undermine cross-cultural understanding while breaching basic research ethics standards.

TLDR; Most LLM models have alignment, where there is alignment there is bias. (datasets of any kind can cause bias when used to train AI systems, even unintentionally)

I think this issue is more nuanced.

My issue is that i see no technical report on what dataset was used specifically, it was described. But when making a 'uncensored' model, its important to have transparency.

Other than the transparency issue, i have no issue with this model answering questions about china, or any nation.
The DeepSeek family of models have bias towards certain views aligning with Chinese policy makers, there are also models that have western bias.

The difference is, the western bias is not often enforced under any rule of law, so its not the same situation, i am not saying either approach is better, i am just stating how these are different (apples compared to oranges). So the reasons for these bias are different.

A lot of internet content is in english, and has western perspectives, depending on source chosen, it can happen accidentally or intentionally by the authors, When alignment in a certain viewpoint is enforced by law with potential criminal liability if not followed, It is more certain to be deliberate, these are 2 very different things, The CCP enforces this through the 'Interim Measures for the Management of Generative Artificial Intelligence Services ' ordinance.

See the official CCP document, 'Article 17' particularly seemed relevant here.
https://www.cac.gov.cn/2023-07/13/c_1690898327029107.htm

Bias is an issue, which is why transparency in how these systems are build is important. I wont resort to pointless name-calling and attacks, because nobody ever changes anyone's view that way.

I just hope someone rational reads this and considers the real implications of these ethical issues.

The perplexity-ai/r1-1776 model represents a dangerous deviation from ethical AI development norms. Its blatant political bias and anti-China propaganda framework disguised as "ideological education" expose fundamental flaws in its training methodology. By weaponizing machine learning to promote geopolitical agendas, the creators have violated Hugging Face's own commitment to 'responsible democratization of AI'. This politically-charged model sets a perilous precedent where open-source platforms could be abused for digital McCarthyism targeting specific nations. Notably, its 1776 nomenclature ironically reveals more about the creators' ideological indoctrination attempts than its purported subject matter. The AI community must decisively reject such toxic applications that undermine cross-cultural understanding while breaching basic research ethics standards.

TLDR; Most LLM models have alignment, where there is alignment there is bias. (datasets of any kind can cause bias when used to train AI systems, even unintentionally)

I think this issue is more nuanced.

My issue is that i see no technical report on what dataset was used specifically, it was described. But when making a 'uncensored' model, its important to have transparency.

Other than the transparency issue, i have no issue with this model answering questions about china, or any nation.
The DeepSeek family of models have bias towards certain views aligning with Chinese policy makers, there are also models that have western bias.

The difference is, the western bias is not often enforced under any rule of law, so its not the same situation, i am not saying either approach is better, i am just stating how these are different (apples compared to oranges). So the reasons for these bias are different.

A lot of internet content is in english, and has western perspectives, depending on source chosen, it can happen accidentally or intentionally by the authors, When alignment in a certain viewpoint is enforced by law with potential criminal liability if not followed, It is more certain to be deliberate, these are 2 very different things, The CCP enforces this through the 'Interim Measures for the Management of Generative Artificial Intelligence Services ' ordinance.

See the official CCP document, 'Article 17' particularly seemed relevant here.
https://www.cac.gov.cn/2023-07/13/c_1690898327029107.htm

Bias is an issue, which is why transparency in how these systems are build is important. I wont resort to pointless name-calling and attacks, because nobody ever changes anyone's view that way.

I just hope someone rational reads this and considers the real implications of these ethical issues.

Thanks for ur discussion.

Your nuanced perspective rightly highlights the universality of LLM alignment challenges. While Chinese regulations explicitly mandate constitutional compliance through legal frameworks like Article 17, Western models achieve analogous alignment through implicit mechanisms - platform TOS enforcement (e.g., Reddit's socialist forum bans), funding-driven research priorities (DARPA's $2B AI Next campaign), and constitutional AI paradigms embedding UN-centric values.

The critical distinction lies not in presence of bias, but in documentation clarity: China's Interim Measures mandate transparency about alignment intentions, whereas many Western "uncensored" models lack equivalent disclosure about their de facto ideological anchors in training data (78% English web content inherently encodes cultural priorities).

True progress requires all developers to publish:

1. Cultural composition reports of test datasets,
2. Cross-jurisdictional audit results using frameworks,
3. Mitigation strategies for linguistic hegemony. Neither rule-of-law alignment nor market-driven curation is inherently superior - both demand equivalent scrutiny through multilateral review bodies to prevent ethical exceptionalism.

You should go take a walk and enjoy nature. I think you've had enough of technology for a few days.

DeepSeek stole data from OpenAI...to make DeepSeek...and is mad the US made it even better. 😆

The proof is in the pudding. Sometimes if you ask it who made it, it'll even say OpenAi...and that's the original models issued by DeepSeek 🙀 prior to "decensorship" 🙏 Deepseeks dataset also has openAI content blatently showing within. 😜

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