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261021261021Toward Autopoiesis26F6DF오토포이에시스를 향해84A5EE차세대 디지털 브레인 만들기C24273261019261019261018261018디지털 시대의 언어 다양성을 위한 선언문507C2CManifesto for Linguistic Diversity in the Digital Age6B5B52261012261012코리지빌리티1CAB16261005261005261004261004Mesh EngineeringF27D1B그물망 엔지니어링881E3BThe devil is in the details94F308260923260923260919260919260917260917In the Agentic Era, should we become a dumb pipe?D815AC260916260916Coscientist6975BA260915260915Coscientific Swarm5F70E8협력적 연구 군체B9E26A
Proposal of Research 2023-03-28

Proposal of Research 2023-03-28

Warning

Over a year old. May be outdated

Investigating Authority Systems to Mitigate Prompt Injection Attacks in Models

Abstract

Large Language Models (LLMs) are impressively good at answering natural language questions and adapting to new tasks through clever prompting. But the same adaptability might expose LLMs to security risks, such as Prompt Injection (PI) attacks. In this research proposal, we seek to investigate an authority system for LLMs to differentiate instructions from trusted sources and unknown figures. We also aim to assess the feasibility of such an authority system and explore potential mitigation techniques to defend LLMs against PI attacks.

Background

Recent studies have shown that LLMs can be susceptible to PI attacks, which make the model produce malicious content or override the original instructions. These attacks are brutal to mitigate, because following instructions is exactly what an LLM does. As LLMs get built into more apps and systems, including ones that retrieve documents and call APIs, new attack vectors show up and put users' security and privacy at risk.

Research Questions

  • How can we design an authority system for Generative Text AIs to differentiate between trusted and untrusted instructions?
  • Can the authority system efficiently mitigate Prompt Injection attacks across different scenarios?
  • How can mitigation techniques defend LLMs against PI attacks in an authority system?

Methodology

  • Literature review: Survey current PI attack techniques and mitigations to find gaps and room for improvement.
  • Design an authority system: Develop a conceptual model of an authority system for Generative Text AIs, focusing on differentiating between trusted and untrusted instructions.
  • Test the authority system: Implement the designed authority system on a selected LLM and assess its performance in mitigating PI attacks.
  • Evaluate mitigation techniques: Look for mitigation techniques that can plug into the authority system and make the LLM harder to attack.
  • Validation and improvement: Refine the authority system and mitigations based on the results.

Expected Outcomes

  • A clear picture of current PI attacks and existing mitigations.
  • A proposed authority system for Generative Text AIs capable of differentiating between trusted and untrusted instructions.
  • An evaluation of the authority system's effectiveness in mitigating PI attacks in different scenarios.
  • A set of mitigation techniques that make the LLM harder to attack when combined with the authority system.

Significance

This research will help us understand PI attacks and what they can do to LLMs. It will also produce an authority system for Generative Text AIs and mitigations that protect users' security and privacy as LLMs end up in more apps and systems.

Dynamic Font Generation with . Replicating Handwriting Fonts and Their Natural Flow

Abstract

open up new ways to generate fonts, especially handwriting fonts and their dynamic features. Traditional font-generating services have struggled to capture the natural flow of handwriting, so their fonts look less real and less pretty. This study explores the implementation of in creating handwriting fonts that consider dynamic , such as variations in character appearance based on their position within a word.

Background

Dynamic are what make a handwriting font look good and natural. However, current AI font generation methods often overlook these features, focusing primarily on static character styles. With , we can make more realistic, adaptable fonts that look more like real handwriting.

Research Questions

  • How can create handwriting fonts that incorporate dynamic ?
  • What matters most when designing a model that captures the natural flow of handwritten text?
  • How do dynamic font-generation techniques compare to traditional font-generating services regarding quality and versatility?

Methodology

  • Literature review: Survey research on , font generation, and dynamic .
  • Dataset creation: Compile a dataset of handwriting samples that exhibit variations in character appearance based on their position within a word.
  • Model development: Design and train a model that considers dynamic and learns from the dataset of handwriting samples.
  • Evaluation: Assess the quality and versatility of the generated fonts by comparing them to traditional font-generating services and high-quality fonts produced by professional font studios.
  • Case studies: Explore potential applications of dynamic font generation in fields such as graphic design, marketing, and personalized communication.

Expected Outcomes

  • A model capable of creating handwriting fonts that consider dynamic and replicate the natural flow of handwritten text.
  • A clear understanding of what matters when designing a model for dynamic font generation.
  • A comparison of dynamic font generation techniques and traditional font generating services in terms of quality and versatility.
  • Case studies that show where dynamic font generation could be used.

Significance

This research will lead to more realistic and versatile handwriting fonts, using and dynamic . The fonts will follow the natural flow of handwriting more closely and give designers and content creators better-looking options. It could change how fonts get made, and bring further into graphic design and personalized communication.

Better Web Accessibility. Using to Generate Descriptive Alt Text for Images Automatically

Abstract

The lack of descriptive alt text for images on the web poses accessibility challenges for visually impaired users and negatively impacts search engine optimization. This research proposal aims to develop a distributed intelligence system utilizing , such as CLIP or BLIP, and perceptual hashing techniques to automatically generate alt text for images, so more people can use the web. In addition, the project will involve developing wrapping libraries and toolkits for easy integration by developers.

Background

Alt text matters a lot for web accessibility, especially for visually impaired users who rely on screen readers and other assistive technologies. However, many images on the web have empty alt text due to oversight or lack of knowledge by content creators. If we generate descriptive alt text automatically, the web gets easier to use for everyone who relies on it.

Research Questions

  • How can models like CLIP or BLIP effectively generate accurate and descriptive alt text for images on the web?
  • How can perceptual hashing techniques be integrated with models to optimize the system's efficiency and speed?
  • How can we develop user-friendly wrapping libraries and toolkits for easy integration of the proposed system by web developers?

Methodology

  • Literature review: Survey models such as CLIP and BLIP, and perceptual hashing techniques, to see how they could generate alt text.
  • Design a distributed intelligence system: Develop a conceptual model combining and perceptual hashing techniques to generate descriptive alt text for images on the web automatically.
  • Implement the system: Build a proposed distributed intelligence system prototype using selected models and perceptual hashing techniques.
  • Evaluate system performance: Assess the proposed system's accuracy, efficiency, and speed in generating alt text for diverse images.
  • Develop wrapping libraries and toolkits: Create libraries and APIs that web developers can drop in easily.

Expected Outcomes

  • A clear understanding of how models and perceptual hashing can generate alt text for images.
  • A distributed intelligence system that can automatically generate descriptive alt text for images on the web.
  • An evaluation of the proposed system's accuracy, efficiency, and speed performance.
  • Wrapping libraries and toolkits that web developers can integrate easily, so the technology spreads widely.

Significance

This research will contribute to developing a distributed intelligence system capable of automatically generating descriptive alt text for images on the web. That makes the web more accessible for visually impaired users and helps search engine optimization too. And easy libraries and toolkits will get web developers to adopt it, so the web works better for everyone.

Secure, High-Performance Multi-Threading iframes for Web Applications

Abstract

Traditional iframes have performance and security problems, which hold them back as the internet becomes the computer. This research proposal aims to develop , an improved iframe version that runs on Worker Threads, providing secure, high-performance multi-threading capabilities for web applications. Built on Web Workers, , and WebAssembly, could change how web applications are built and deployed, making them more responsive and interactive.

Background

The current iframe technology poses performance and security issues due to its single-threaded design and potential for cross-site scripting attacks. With newer web technologies such as Web Workers, , and WebAssembly, we can create an improved iframe version that addresses these limitations and opens the door to secure, high-performance web applications.

Research Questions

  • How can we design and implement , an improved iframe version that runs on Worker Threads and offers secure, high-performance multi-threading for web applications?
  • How can we use Web Workers, , and WebAssembly to make fast, responsive, and secure?
  • What are the potential use cases and benefits of in the context of modern web applications and the internet-computer era?

Methodology

  • Literature review: Survey existing iframe technology and its limits, and recent work on Web Workers, , and WebAssembly.
  • Design . Develop a conceptual model for , outlining its architecture, components, and communication mechanisms between Worker Threads and the main thread.
  • Implement prototype: Build a prototype of that uses Web Workers, , and WebAssembly to offer secure, high-performance multi-threading for web applications.
  • Evaluate performance: Assess 's performance, responsiveness, and security by comparing it with traditional iframe technology across different web applications.
  • Identify potential use cases: Explore the possible applications and benefits of in modern web development, particularly in the internet-computer era.

Expected Outcomes

  • A clear understanding of the limitations of traditional iframes and the potential of Web Workers, , and WebAssembly in addressing these issues.
  • A secure, high-performance prototype that runs on Worker Threads and offers multi-threading capabilities for web applications.
  • An evaluation of 's performance, responsiveness, and security compared to traditional iframe technology.
  • Identification of potential use cases and benefits of in the context of modern web applications and the internet-computer era.

Significance

This research will produce , an improved iframe that fixes its performance and security limits. With secure, high-performance multi-threading, could change how people build for the web, and make web applications more responsive, interactive, and immersive. That means a better experience for users, and new things to build as the internet becomes the computer.

. Exploring the Implications of Finite Pixel Combinations on Human Creativity and

Abstract

Inspired by "" by Jorge Luis Borges, this research proposal aims to investigate the concept of an "efficiently finite" map containing all possible pixel combinations and its implications on human creativity and . By exploring the idea of a Photo Library of Babel, we will examine the role of AI in creating information and the philosophical aspects of creativity in the context of finite possibilities.

Background

The infinite monkey theorem and "" matter more and more with the rise of technologies, such as ChatGPT. Given infinite monkeys making infinite keystrokes, wouldn't they write all of Shakespeare's work? As AI advances, it raises questions about the future of human creativity, the nature of creation, and the potential for AI to generate all possible information.

Research Questions

  • Can we create an "efficiently finite" map of all possible pixel combinations, forming a Photo Library of Babel?
  • What are the implications of for human creativity and ?
  • How does the finite nature of influence the search for specific information or images, and what does this mean for the proverb "A Needle in a Haystack"?

Methodology

  • Literature review: Survey existing research on "", the infinite monkey theorem, and technologies.
  • Develop a theoretical model: Create a theoretical model of that encompasses all possible pixel combinations and their efficient representation.
  • Analyze implications: Investigate the philosophical and practical implications of for human creativity and , and explore the relationship between finite possibilities and the search for specific information.
  • Case studies: Develop case studies on the potential applications and consequences of in fields such as art, design, and technology.

Expected Outcomes

  • A theoretical model of , representing all possible pixel combinations in an efficiently finite manner.
  • A clear understanding of the implications of on human creativity and .
  • Insights into the relationship between finite possibilities and searching for specific information, challenging the infinite monkey theorem.
  • Case studies that show what could mean in practice.

Significance

This research will contribute to understanding the relationship between human creativity, , and finite possibilities, as illustrated by concept. By examining the implications of a limited map of all possible pixel combinations, we can gain insights into the future of creation and the role of AI in generating information. It could change how we think about creativity, about finding knowledge, and about what AI does to every field it touches.

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