One AI to Do It All

Welcome curious enthusiasts, knowledge seekers and hardcore researchers.

The world of AI is moving fast, but how does the current generation of AI differ from the previous? One important distinction is that the previous generation needed to be trained on millions of datapoints and thousands of times just to get good at one task. With Generative AI, the models work out of the box on a multitude of tasks and domains. These polymathic abilities are on display in today’s edition with LLMs traversing the challenging domains of law, medicine, software and perhaps the most complex of all… Discord.

The System

Let’s start.

GPT-doctor: Customizing Large Language Models for Medical Consultation

Wen Wang, Zhenyue Zhao, Tianshu Sun

Key Topics: Large Language Models, Medical Consultation, Customization, Healthcare AI, GPT Models

Link: here | AI Score: 🚀 | Interest Score: 🧲 🧲🧲 | Reading Time: ⏰⏰⏰

The paper discusses customizing a Large Language Model (LLM) called GPT-doctor for medical consultations. They trained the model using real doctors' consultation records and medical knowledge from databases. The study shows that the fine-tuned GPT-doctor model performs similarly to human doctors in terms of medical expertise and consumer preferences. This advancement could have significant benefits for the healthcare industry, such as improving access to medical advice and reducing costs.

Weaving Pathways for Justice with GPT: LLM-driven automated drafting of interactive legal applications

Quinten Steenhuis, David Colarusso, Bryce Willey

Key Topics: Generative AI, Legal Automation, Human-AI Collaboration, Interactive Legal Applications, Large Language Models

Link: here | AI Score: 🚀 🚀 | Interest Score: 🧲 🧲 | Reading Time: ⏰ ⏰

Result: The paper discusses the use of generative AI to automate the drafting of interactive legal applications, particularly to assist self-represented litigants. It explores a hybrid approach of automated drafting with human review to improve the efficiency and accuracy of the process. The authors highlight the challenges and benefits of using large language models like GPT-4 for this purpose, emphasizing the importance of human intervention in complex legal documents. The study presents a tool called the Assembly Line Weaver, which aids in scanning templates for variables and generating draft guided interviews. Overall, the paper suggests that a collaborative approach between AI and human editors can significantly enhance the automation of legal forms for better accessibility and usability.

Can LLMs Configure Software Tools

Jai Kannan

Key Topics: Large-Language Models, Software Tool Configuration, Hyperparameter Variability, Statistical Analysis, Bayesian Optimization

Link: here | AI Score: 🚀 🚀 | Interest Score: 🧲 🧲 | Reading Time: ⏰ ⏰

Result: The paper explores the use of Large-Language Models (LLMs) for software tool configuration. It addresses three research questions related to the variability of hyperparameters within and across use cases, the performance comparison of LLMs with state-of-the-art methods, and the search space for finetuning hyperparameters. The study involves statistical analysis, ANOVA tests, and comparisons with Bayesian optimization. The findings suggest that LLMs can provide a sensible search space for hyperparameter configuration and offer competitive advantages in software tool configuration.

Generation Z's Ability to Discriminate Between AI-generated and Human-Authored Text on Discord

Dhruv Ramu, Rishab Jain, Aditya Jain

Key Topics: AI-generated Content, Human-Computer Interaction, Generation Z, Social Media Platforms, Perception Analysis

Link: here | AI Score: 🚀 | Interest Score: 🧲 🧲🧲 | Reading Time: ⏰ ⏰

Result: This study investigates how Generation Z individuals perceive and classify AI-generated text compared to human-authored text on social media platform Discord. The research involved 335 participants and explored factors like education, familiarity with AI technology, and experience with Discord. Findings suggest that individuals with more experience with AI may exhibit overconfidence or bias in distinguishing between human and AI-generated content. The study highlights the importance of understanding the impact of AI on human-computer interactions for Generation Z.

Accuracy of a Vision-Language Model on Challenging Medical Cases

Thomas Buckley, James A. Diao, Adam Rodman, Arjun K. Manrai

Key Topics: Vision-Language Models, Medical Diagnostics, GPT-4V, Clinical Reasoning, Multimodal AI

Link: here | AI Score: 🚀 🚀 🚀 | Interest Score: 🧲 🧲 | Reading Time: ⏰ ⏰

Result: The research paper evaluates the performance of the Generative Pre-trained Transformer 4 with Vision model (GPT-4V) on challenging medical cases from the New England Journal of Medicine (NEJM) Image Challenge. The study compares the accuracy of GPT-4V to human respondents across different levels of difficulty, skin tones, and image types. Results show that GPT-4V outperformed human respondents in diagnosing medical cases, demonstrating its potential in assisting with clinical reasoning tasks. The paper highlights the importance of multimodal reasoning using both text and images for improved diagnostic performance in AI models.