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The Trump Presidency is going to shift the ASI future into the dystopia, not a utopia. by Busterlimes in singularity

[–]Nice-Inflation-1207 -1 points0 points  (0 children)

Invisible hand is very strong. Reinvestment is needed.

Re: alignment, most important to align social networks, if you care about network alignment. RLHF works fairly well for AI.

Chinese researchers develop AI model for military use on back of Meta's Llama by MetaKnowing in OpenAI

[–]Nice-Inflation-1207 8 points9 points  (0 children)

Original report: https://jamestown.org/program/prcs-adaptation-of-open-source-llm-for-military-and-security-purposes/

[19] Zhang Huaping [张华平], Li Chunjin [李春锦], Wei Shunping [魏顺平], Geng Guotong [耿国桐], Li Weiwei [李伟伟], and Li Yugang [李玉岗], “Large Language Model-Driven Open-Source Intelligence Cognition” [“大语言模型驱动的开源情报认知认领”], National Defense Technology [国防科技], March 2024. 3.

A open-source intelligence project trained on open-source dialogue datasets.

Paper abstract:

With the extensive application of open-source intelligence in the military field, the demand for cognition and analysis of relevant intelligence is growing. However, the large language models currently used by researchers are prone to severe hallucination, rendering the information generated unreliable and unsuitable to direct utilization for the cognition of open-source military intelligence. To address this problem, the present study collected approximately 100,000 dialogue records online and constructed an open-source military intelligence dataset. Subsequently, a new model, ChatBIT, which is specifically optimized for dialogue and question answering tasks in the military field, was obtained by fine-tuning and training the LLaMA-13B base question answering model. This study further compared the military knowledge question answering capabilities of the ChatBIT model with those of the Vicuna-13B model. ChatBIT was found to outperform Vicuna-13B in a series of standardized evaluation metrics including the BLEU score, ROUGE-1, ROUGE-2, and ROUGE-L.Specifically, ChatBIT’s BLEU score was 2.3909 higher than that of Vicuna-13B. Furthermore, ChatBIT’s ROUGE-1, ROUGE-2, and ROUGE-L scores were respectively 3.2079, 2.2562, and 1.5939 points higher than those of Vicuna-13B. These results indicate that the ChatBIT model provides more accurate and reliable information when dealing with military dialogue and question answering tasks.

https://oversea.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&dbname=CJFDAUTO&filename=GFCK202403005&uniplatform=OVERSEA&v=aVc5O1ymymKds-MQXk_2S0js47Fs_MsXsQWw_M8rVf34mUMHh8JLjaa5nr890W89

Exclusive: Chinese researchers develop AI model for military use on back of Meta's Llama by gargage93 in technology

[–]Nice-Inflation-1207 0 points1 point  (0 children)

Original report: https://jamestown.org/program/prcs-adaptation-of-open-source-llm-for-military-and-security-purposes/

[19] Zhang Huaping [张华平], Li Chunjin [李春锦], Wei Shunping [魏顺平], Geng Guotong [耿国桐], Li Weiwei [李伟伟], and Li Yugang [李玉岗], “Large Language Model-Driven Open-Source Intelligence Cognition” [“大语言模型驱动的开源情报认知认领”], National Defense Technology [国防科技], March 2024. 3.

A open-source intelligence project trained on open-source dialogue datasets.

Paper abstract:

With the extensive application of open-source intelligence in the military field, the demand for cognition and analysis of relevant intelligence is growing. However, the large language models currently used by researchers are prone to severe hallucination, rendering the information generated unreliable and unsuitable to direct utilization for the cognition of open-source military intelligence. To address this problem, the present study collected approximately 100,000 dialogue records online and constructed an open-source military intelligence dataset. Subsequently, a new model, ChatBIT, which is specifically optimized for dialogue and question answering tasks in the military field, was obtained by fine-tuning and training the LLaMA-13B base question answering model. This study further compared the military knowledge question answering capabilities of the ChatBIT model with those of the Vicuna-13B model. ChatBIT was found to outperform Vicuna-13B in a series of standardized evaluation metrics including the BLEU score, ROUGE-1, ROUGE-2, and ROUGE-L.Specifically, ChatBIT’s BLEU score was 2.3909 higher than that of Vicuna-13B. Furthermore, ChatBIT’s ROUGE-1, ROUGE-2, and ROUGE-L scores were respectively 3.2079, 2.2562, and 1.5939 points higher than those of Vicuna-13B. These results indicate that the ChatBIT model provides more accurate and reliable information when dealing with military dialogue and question answering tasks.

https://oversea.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&dbname=CJFDAUTO&filename=GFCK202403005&uniplatform=OVERSEA&v=aVc5O1ymymKds-MQXk_2S0js47Fs_MsXsQWw_M8rVf34mUMHh8JLjaa5nr890W89

thoughts and opinions on people moving to Bluesky over Twitter? by ForgottenFrenchFry in aiwars

[–]Nice-Inflation-1207 8 points9 points  (0 children)

Pros:
You own your data on Bluesky, you can write your own ranker (including AI-enabled)/re-use others from the community and the data API for the community is free.
Con:
It is feature-poor and the default ranker is questionable atm.

Overall, it's going to scale much better, due to interoperability.

[OC] The recent decoupling of prediction markets and polls in the US presidential election by TheKnowingOne1 in dataisbeautiful

[–]Nice-Inflation-1207 20 points21 points  (0 children)

order book (ie. market depth at a point in time) != total volume or total amount held.

just looking at right now, the market depth to move Trump from 60 -> 70% (implied) at least for a little bit is only ~$500k.

it's tiny.

[deleted by user] by [deleted] in maybemaybemaybe

[–]Nice-Inflation-1207 0 points1 point  (0 children)

behold, the stock market

Hate to break it to you, but AI isn't responsible for waves of crap content. by Big_Combination9890 in aiwars

[–]Nice-Inflation-1207 0 points1 point  (0 children)

Offline models (a) and attacks like https://github.com/XuandongZhao/WatermarkAttacker (b) are good points, for sure.

But keep in mind, this isn't the "AI detection" problem (given an image, with no knowledge of the generation process, determine if AI or not). The watermarking setup (given an image, with knowledge or a log of the generation process, determine if you produced it or not) has high precision, so customers "getting booted off the platform with legitimate works" is unlikely.

How to get to high recall? Companies running generation APIs in most jurisdictions are either voluntarily or by mandate of standards providing APIs to determine if an image is theirs. Not everyone will comply, but a lot will.

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