Advanced's recent activity

  1. Comment on Should I stop using Kagi because they do business with Yandex? in ~tech

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    Kagi is not a independent search engine.

    Kagi is not a independent search engine.

    3 votes

  2. Comment on Looking for DRM-free book recommendations in ~books

  6. Comment on 2024 United States election megathread in ~society

  7. Comment on Inside the plan to use AI to purge US voter rolls in ~society

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    Oh man, they are kind of late to report on this. I think the only thing they have new here is their FOIA request with their analysis. This article seems to be : Old Info + FOIA Request + Flashy...

    Oh man, they are kind of late to report on this.

    I think the only thing they have new here is their FOIA request with their analysis.

    This article seems to be :

    Old Info + FOIA Request + Flashy Title.

    Also, please add the source tag, I actually have 404 Media on my filter.

    6 votes

  9. Comment on The Vatican’s anime mascot is now an AI porn sensation in ~tech

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    I literally cannot understand how the author sit down and decided to write this article. Never thought that 404 Media downfall is so soon. They had good investigations. I guess nothing stay good...

    I literally cannot understand how the author sit down and decided to write this article.

    Never thought that 404 Media downfall is so soon. They had good investigations.

    I guess nothing stay good forever, time to add their domain to my filters.

    6 votes

  11. Comment on The Vatican’s anime mascot is now an AI porn sensation in ~tech

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    2 things: This the unpaywalled link. What the heck is this Vice quality article.

    2 things:

    1. This the unpaywalled link.
    2. What the heck is this Vice quality article.
    14 votes

  13. Comment on Elon Musk now says it's 'pointless' to build a $25,000 Tesla for human drivers in ~transport

  14. Comment on Elon Musk now says it's 'pointless' to build a $25,000 Tesla for human drivers in ~transport

  18. Comment on Assessing the ability of GPT-4o to visually recognize medications and provide patient education in ~health

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    Abstract

    Various studies have investigated the ability of ChatGPT (OpenAI) to provide medication information; however, a new promising feature has now been added, which allows visual input and is yet to be evaluated. Here, we aimed to qualitatively assess its ability to visually recognize medications, through medication picture input, and provide patient education via written and visual output. The responses were evaluated by accuracy, precision and clarity using a 4-point Likert-like scale. In regards to handling visual input and providing written responses, GPT-4o was able to recognize all 20 tested medications from packaging pictures, even with blurring, retrieve their active ingredients, identify formulations and dosage forms and provide detailed, yet concise enough, patient education in an almost completely accurate, precise and clear manner with a score of 3.55 ± 0.605 (85%). In contrast, the visual output through GPT-4o generated images illustrating usage instructions contained many errors that would either hinder the effectiveness of the medication or cause direct harm to the patient with a poor score of 1.5 ± 0.577 (16.7%). In conclusion, GPT-4o is capable of identifying medications from pictures and exhibits contrasting patient education performance between written and visual output with very impressive and poor scores, respectively.

    1 vote

  20. Comment on Generative models for source code: Fine-tuning techniques for structured pattern learning in ~tech

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    Abstract

    : This study addresses the problem of how to automatically generate source code that is not only functional, but also well-structured, readable, and maintainable. Existing generative models for source code often produce functional code, but they lack consistency in structure and adherence to coding standards, essential for integration into existing application development projects and long-term software maintenance. By training the model on specific code structures, including a dataset with Italian annotations, the proposed methodology ensures that the generated code is compliant with both the functional requirements and the pre-defined coding standards. The methodology proposed in this study applies transfer learning techniques on the DeepSeek Coder model, to refine pre-trained models to generate code that integrates additional structuring constraints. By training the model on specific code structures, including a dataset with Italian comments, the proposed methodology ensures that the generated code meets both functional requirements and coding structure. Experimental results, evaluated using the perplexity metric, demonstrate the effectiveness of the proposed approach, which impacts the goals of reducing errors, and ultimately improves software development quality.

    2 votes