Leveraging Large Language Models for Generalized Artificial Intelligence: An Exploration in Flexible System Design

Short and sweet exploration of how LLM's can be used as Intelligent filters as a springboard for General Artificial Intelligence application design.

Abstract

What if we could integrate "flexibility" into system design? This article explores the potential of Large Language Models (LLMs) as a springboard for Generalized Artificial Intelligence (GAI). By harnessing LLMs' contextual understanding, we propose a flexible approach to system design where LLMs, like Chat-GPT, serve as intelligent filters by matching use cases to recommended architectures, and generating the foundational elements for software engineers to build upon.

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Introduction

We explore a concept that extends the capabilities of LLMs from natural language understanding to Generalized Artificial Intelligence (GAI). By leveraging LLMs' contextual comprehension, we propose a paradigm shift in system design. LLMs can be employed as intelligent filters to match use cases with recommended architectures or automated workflows and generate the initial coding and architectural frameworks. This inherent contextual comprehension makes LLMs a promising candidate for propelling the development of GAI.

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The Power of Contextual Comprehension

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LLMs exhibit an ability to grasp context and categorize information. They can discern not only the words used but also the context in which they are employed. This contextual understanding enables LLMs to distinguish between different use cases and recommend appropriate solutions in the forms of predefined categories.

LLMs excel in recognizing patterns of similarity, for example: “What’s your address?” “What neighborhood do you live in?” “Where’s the nearest intersection to your house?” “What stores do you live by?”

System developers can start thinking in ≈ instead of =

Expanding upon this capability, LLMs prove invaluable in comprehending technical system use cases and architectures. This is particularly evident since LLMs underwent training on extensive technical documentation for major technological products. What does this mean? This implies that LLMs possess an understanding of system specifications, use cases, and task summaries. Consequently, even if a human input into a library of automated workflow processes is inaccurate or imprecise, the LLM can intelligently select the correct workflow, thanks to the algorithm's contextual flexibility. In essence, it already comprehends that a Pomeranian is a dog, is a canine, is named Fluffy, its’ purpose is to cuddle and have expensive vet bills and is often referred to as Drama Queen.

Example:

Business need: I need to put this collar on my dog

Contextual Task: Match collar with dog workflow

Human entry for the definition of automated workflow in the library: Pomeranian

LLM: Pomeranian = Dog

LLM recommendation: Use Pomeranian workflow

Value proposition: Enhanced fault tolerance and flexibility??

From Use Case to System Design

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Because LLMs have already been trained on vast amounts of technical documentation, there is no need to create a separate dataset of use cases matched with the system architecture or workflows that solved the problem. LLMs are capable comprehension engines that can categorize these use cases in most instances.

Example: Healthcare GAI Application

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Automating System Design

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The innovative aspect of using LLMs in this manner is the potential for automating parts of the system deployment process. LLMs can generate the initial coding and architectural outlines based on the identified use case, business model, and industry. Software engineers can then build upon these foundations, significantly reducing development time and effort.

Example: Marketing General Artificial Intelligence Application

Retrieval Augmented Generation or Domain-Specific LLM

In scenarios where use cases involve abundant specialized terminology or domain-specific conversational language, the General Artificial Intelligence (GAI) can enhance its performance by leveraging Retrieval Augmented Generation (RAG) LLMs or Domain Specific LLMs.

Example: Data Cleaning GAI Application

Conclusion

Large Language Models have the capacity to grow beyond language processors to become part of the necessary architecture for General Artificial Intelligence solutions. Their contextual understanding empowers them to recommend architectures and generate coding frameworks for diverse use cases. This innovative approach has the potential to revolutionize AI system design, and drive efficiency and innovation across industries. As we unlock potential, we are paving the way for Generalized Artificial Intelligence systems using LLM’s as intelligent filters and recommendation systems.

Mark McQuade Puck Fernsten Nathan Lile Madelyn Romberg Muntaser Syed Rohan Vardhan Md Jibanul Haque Jiban Kanak Choudhury Laura Li Ryan Ries Anna Joo Fee Tatsiana Sokalava, MBA, SDS? Annija Eizenarma Maryam A Hassani Sylvia Bouloutas Carrie Mah ?????? Leena Sukumar Sena Kim Chelsea Goddard Milly Wang Janet Gehrmann Maria Pienaar Shrunga Divakara Chavalmane Maria Attarian Kirthiga Reddy Shannon Ellis Raymond Lee Diana Solatan Jonathan Simon Oana Olteanu Anushree Goenka Aquibur Rahman Katie Wilson Jiquan Ngiam Hila Emanuel Golan Adam Steinle Deborah Magid Florian (Flo) Boymond Nikki Farb MARILYN BETSABE ALVARADO QUIROZ Angelique Schouten Startup Oasis Hector Jirau, Ph.D. Frank Gruber Vincent Granville Richard Cotton Y?Combinator Steve Nouri Sahab Aslam WVV Capital SignalFire Generative AI Adam Sterling OpenAI Dave Mathews kyosuke togami Ginger Siedschlag Adam Smith Spyro Ananiades Khobaib Zaamout, Ph.D. PeterPeter FitzGibbon ChatGPT ??Jepson Taylor Andrew Ng Union Square Ventures Alvin Foo Brian Costa Ana Maria Echeverri Michael Kearns Forbes Ali Shadman Ciaran Coulter