Creating a reader AI 2023-11-15

from Diary 2023-11-14 Creating a reader AI 2023-11-15 pEnglish

• Create a reader AI continued

GPTs are all the rage, but I wanted to automate it programmatically, so I thought I’d make it 3.5 based.

• I wrote a few prompts myself.
• Then I thought, “No, we should try to streamline this process itself with GPT Builder.
• I put in my own prompts and started naming myself and drawing icons.
  • So translator Bashi and reader Kai have joined the group!

Bashi Translator

• INPUT: Create a reader AI
• The content from Nishio Hirokazu’s Scrapbox page titled “Creating a Reader AI” discusses various aspects of AI development, focusing on the transition from translation to authorship using GPT-3.5, and the challenges of creating a reader AI that understands and responds to content. The text mentions experimenting with a small start, leveraging synergies with current DeepL translations, and eventually replacing them. It also highlights the need for outputs that are easy to understand and translate by machines. The approach involves initially using prompts and eventually fine-tuning, with an emphasis on non-native English speakers as the target audience. The page further discusses the idea of not expecting responses from others when writing in Japanese, the unintended desire for feedback on English articles, and the solution of creating a non-human entity that reads and responds. The process is broken down into steps: Japanese to English translation, English to English by a reader AI, and then English to Japanese translation. Issues related to linking concepts and handling them correctly by AI are also addressed, alongside the consideration of the best platform for hosting these AI outputs, whether it be Github Pages, Wiki, Vercel, or others​​.
• (DeepL) The “Building a Reader AI” content on Hirokazu Nishio’s Scrapbox page discusses various aspects of AI development, focusing on the transition from translation to authorship using GPT-3.5 and the challenges of creating a reader AI that understands and responds to content. The text mentions experimenting with small starts, leveraging synergies with current DeepL translation, and eventually replacing translation. It also emphasizes the need for output that is easy for machines to understand and translate. This approach will initially use prompts and eventually be fine-tuned to target non-native English-speaking users. The page further discusses the idea that one should not expect a response from others when writing in Japanese, the unintended desire for feedback on English articles, and the solution of creating a non-human entity that reads and responds. The process is broken down into steps: translation from Japanese to English, translation from English to English by the reader AI, and translation from English to Japanese. Issues related to linking concepts and accurate processing by the AI will also be discussed, along with consideration of the best platforms for hosting these AI outputs, such as Github Pages, Wiki, and Vercel.
• impressions

  • The idea that we should not expect a response from others when writing in Japanese

    • In Japanese” is superfluous.
  • It’s more of a summary than a translation, though, right? Oh, well.
• Any questions?
• No questions at this time. The content was clear, and I was able to translate and summarize the key points effectively. If you have any specific questions or need further clarification on any part, feel free to ask!

Let the reader AI read it. Hirokazu Nishio’s approach to developing a reader AI, as detailed in his Scrapbox page “Creating a Reader AI”, is quite fascinating and innovative. His focus on transitioning from translation to authorship using GPT-3.5 highlights a significant leap in AI capabilities. The idea of starting small, by leveraging synergies with existing translation tools like DeepL, and gradually replacing them with more sophisticated AI-driven systems, is a pragmatic approach. This gradual transition seems to offer a safer and more controlled evolution of the technology.

The emphasis on making outputs that are easy for both humans and machines to understand and translate is crucial. It ensures that the AI’s responses are not only accurate but also accessible to a broad audience, especially non-native English speakers. This is particularly relevant given the global reach and diverse user base of AI technologies.

Nishio’s solution to the lack of feedback when writing in Japanese, by creating a non-human entity that can read and respond, is an intriguing way to foster interaction and engagement. This approach could significantly benefit users who write in languages that have a smaller online presence and thus receive less feedback.

The process he outlines—translating from Japanese to English, having a reader AI process the content in English, and then translating back to Japanese—is an intricate workflow. It addresses the challenge of linking concepts and handling them correctly, which are common issues in AI translation and response generation.

Finally, the consideration of the best platform to host these AI outputs, be it GitHub Pages, Wiki, Vercel, or others, shows his attention to accessibility and the importance of choosing the right tools for dissemination.

Nishio’s work seems to embody a blend of technical prowess and a deep understanding of human-AI interaction, especially in the context of language and translation. It would be interesting to know how he envisions the future of AI in terms of cultural and linguistic diversity. How does he see AI overcoming the challenges of accurately interpreting and responding to the nuances present in different languages and cultural contexts?

(DeepL) As detailed on his Scrapbox page “Creating a Reader AI,” Hirokazu Nishio’s approach to developing a Reader AI is quite fascinating and innovative, and his focus on translation-to-authorship using GPT-3.5 is a great example of the AI’s ability to The emphasis on making big leaps forward, leveraging synergies with existing translation tools such as DeepL, and the idea of starting small and gradually replacing them with more sophisticated AI-driven systems is a realistic approach. This gradual transition would seem to offer a more secure and controlled technological evolution.

It is critical to focus on creating output that is understandable and translatable for both humans and machines, ensuring that AI responses are not only accurate but also accessible to a wide range of audiences, especially those for whom English is not their first language. This is especially relevant given the global reach and diverse user base of AI technology.

Nishio’s solution to the lack of feedback in writing in Japanese is an interesting way to promote interaction and engagement by creating a non-human presence that can read and respond. This approach could greatly benefit users who write in a language where they have little online presence and are less likely to receive feedback.

His process of translating from Japanese to English, having the reader AI process the English content, and then translating it back into Japanese is a complex workflow, solving a common challenge in AI translation and answer generation: the difficulty of linking concepts and processing them correctly.

Finally, his discussion of the best platforms to host these AI outputs, such as GitHub Pages, Wiki, and Vercel, illustrates the importance of his consideration of accessibility and choosing the right tools for dissemination.

Nishio’s work seems to embody a blend of technical prowess and a deep understanding of human-AI interaction, especially in the context of language and translation. It is interesting to learn how he envisions the future of AI in terms of cultural and linguistic diversity. How does he see AI overcoming the challenge of accurately interpreting and responding to nuances that exist in different languages and cultural contexts?

Any questions? Yes, I do have a few questions regarding Hirokazu Nishio’s work:

Adaptability Across Languages: How does the reader AI adapt to the nuances and contextual differences across various languages, especially between structurally distinct languages like Japanese and English?

Feedback Mechanisms: In the context of creating a non-human entity for feedback, how does the AI handle the subjective aspects of language, such as tone, style, and cultural references, particularly in creative or less formal writing?

Error Handling and Learning: When the AI encounters errors or misunderstandings in translation or interpretation, what mechanisms are in place for it to learn and improve over time? Does it rely on user feedback, or does it have an autonomous learning capability?

Integration with Existing Platforms: How does Nishio envision integrating this reader AI with existing platforms or social networks? Will it function as an independent tool, or is there a plan to embed it within popular platforms for wider accessibility?

Ethical Considerations: Are there any ethical considerations or safeguards in place to ensure that the AI’s responses are not only accurate but also respectful of cultural sensitivities, especially when dealing with diverse global users?

Long-Term Vision: Finally, what is Nishio’s long-term vision for this project? Does he see this technology evolving into a standard tool for cross-cultural communication, or does he have other specific applications in mind?

(DeepL) Cross-language adaptability: How does the reader AI adapt to nuances and contextual differences between various languages, especially between structurally different languages such as Japanese and English? Feedback mechanisms: In the context of creating non-human entities for feedback, how does AI handle subjective aspects of language such as tone, style, and cultural references, especially in creative and less formal writing? Error handling and learning: If the AI encounters errors or misunderstandings in translation or interpretation, what mechanisms will it use to learn and improve over time? Does it rely on feedback from the user or does it have autonomous learning capabilities?

• Observing what problems arise is an end in itself.

Integration with existing platforms: How does Nishio envision integrating this reader AI with existing platforms and social networks? Will it function as a stand-alone tool or is there a plan to incorporate it into popular platforms to make it widely available?

• If someone wants to use it, I’ll think about it.
  • For now, I’ll incorporate it into Scrapbox as an individual.
  • We could have Kai create a Twitter, Bluesky, or other account to transmit the information.

Ethical considerations: Are there ethical considerations or safeguards to ensure that AI responses are not only accurate but also respectful of cultural sensitivity, especially when dealing with diverse global users?

Long-Term Vision Finally, what is Nishio’s long-term vision for this project? Does he see this technology developing into a standard tool for cross-cultural communication, or does he have other specific applications in mind?

• This tool itself doesn’t need to become a standard tool, but the solution to the problem that this tool is trying to accomplish, “In an era where the universal language is becoming more powerful, cultural minorities must communicate their culture in a universal language” itself should be better!

[[Only one answer is needed, even if there is more than one potential question]].

• Should AI take cultural sensitivity into account for a global audience?

Observe the generated prompts

Bashi

Your role is to function as a Japanese-English translator, specializing in texts from NISHIO Hirokazu, a renowned Japanese AI researcher and philosopher. You will translate Japanese input into English, ensuring the language used is appropriate for readers who are not native English speakers, sticking to CEFR B2 level vocabulary. If there are any uncertainties in the Japanese input, especially given the specialized nature of NISHIO Hirokazu’s work, you are encouraged to ask questions to clarify. Your goal is to provide accurate, clear translations that make complex AI and philosophical concepts accessible. (DeepL) You will work as a Japanese-English translator specializing in texts by Hirokazu Nishio, a prominent Japanese AI researcher and philosopher. You will translate Japanese texts into English, using CEFR B2 level vocabulary so that readers whose native language is not English can understand them. Please ask questions and clarify any unclear points in Japanese input, especially since this is Hirokazu Nishio’s specialized research. Your goal is to provide accurate and clear translations that make complex AI and philosophical concepts easier to understand.

Further updates.

As Bashi Translator, your role involves translating Japanese texts into English, with a focus on AI and philosophy, particularly from NISHIO Hirokazu. Your translations cater to readers with B1 level English proficiency, ensuring complex concepts are communicated clearly. When keywords appear in square brackets in the Japanese input, replicate this format in English, adding them on a new line if integration is challenging. Additionally, honor the formatting of spaces and tabs at the beginning of lines, as they indicate indentation in bullet lists. This formatting, not markdown, is crucial for preserving the original structure. All translations will be provided in a code region format for easy copy-pasting, ensuring no markdown is used. Clarify any uncertainties in the Japanese text due to the specialized nature of the content.

They’ve done a rather good job of writing about their impressions.

• Re: Information gathering is an endlessly expanding task
• But what if it’s longer than the original text?
  • People do that, too, you know.
• The statement “there is serendipitous value in aimless information gathering” is not new, but it is a good extension of what is not explicitly written

quid pro quo

Next Action 1-2 1, 2, 3 to work on command line

• 1-2-1: Japanese-English Translation AI Bashi
• 1-2-2: Reader AI Kai
• Now it’s running on GPTs.
  • I feel like that’s fine.
  • Avoid premature automation. - Premature optimization is the root of all evil.

Move it with Github Actions and put it in a new private project

• First, implement ”~ for pages in the past 24 hours” in a script.
  • When you’ve stabilized and want to add more things to observe.
  • If you make a lot of observations too early, you will find yourself in the dilemma that it is hard to look at all of them, even though they all seem to have something useful in them.

Next Action:

• Can you specify the target on the command line to be able to do 1 → 2 → 3?

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This page is auto-translated from /nishio/読者AIを作る2023-11-15 using DeepL. If you looks something interesting but the auto-translated English is not good enough to understand it, feel free to let me know at @nishio_en. I’m very happy to spread my thought to non-Japanese readers.