Rishub C R (Craftsman) CraftsMan-Labs

Research Plan

Title: Enhancing AI-Powered NPCs for Immersive Gaming and Skill Development

Introduction

The integration of artificial intelligence (AI) in gaming has revolutionized the way non-player characters (NPCs) interact with players, creating more immersive and dynamic experiences. My research aims to further this innovation by developing AI-powered gaming engines where NPCs possess their own voices and can perform complex actions, similar to those seen in games like Call of Duty and Fortnite. This aligns with Professor research interests in entertainment computing, game AI, and educational technology.

Objectives

Develop AI-Powered NPCs: Create NPCs with advanced voice capabilities and realistic behaviors using generative AI models.
Enhance Player-NPC Interaction: Implement natural language processing (NLP) and machine learning (ML) to enable NPCs to engage in meaningful conversations and adapt to player actions.

Research Plan

Title: Optimizing Large-Scale Model Training Using Distributed and Federated Learning Techniques

Introduction

The rapid advancement in artificial intelligence (AI) and machine learning (ML) has led to the development of increasingly large and complex models. Running these models efficiently requires splitting them across multiple accelerated compute units, such as GPUs, and ensuring they work seamlessly as a single system. This research aims to optimize the performance of such large-scale models using distributed and federated learning techniques, aligning with Professor Akira Nukada's expertise in high performance computing, performance optimization, and GPU computing.

Objectives

Develop Techniques for Model Splitting: Create methods to effectively split large models across multiple GPUs.
Optimize Inter-GPU Communication: Enhance the communication protocols between GPUs to minimize latency and maximize throughput.

Research Plan: Self-Improving Multi-Agent Simulation Ecosystem with Vision-Language Models (VLMs)

Research Objective

Create an Ecosystem for Autonomous Self-Improvement: Develop a system where Large Language Models (LLMs) can autonomously identify their limitations, research solutions, and fine-tune themselves to enhance their performance in multi-agent simulations. This system will generate its own synthetic data using advanced models like GPT-4 to work on knowledge distillation and integrate advanced self-improvement techniques.
Distributed Computing for LLMs: Implement a distributed computing framework to run large LLMs like LLaMA70B across multiple GPUs globally, enabling efficient and scalable model training and inference.
Incorporate Vision-Language Models (VLMs) into Robotics: Leverage VLMs to enhance the intelligence of robotic systems, enabling them to perform complex tasks through natural language instructions and visual inputs.

Background

Relevant Research:

Codestral Mamba by Mistral AI

Here's a comprehensive guide to install and use the Mamba-Codestral model:

Installation

Install all requirements

pip install "transformers>=4.43" --upgrade

Basic Usage

Here's a simple example of how to use the Llama 3.1 8B Instruct model:

from transformers import pipeline
import torch

Benchmark	GPT-4O	Llama 3.1 405B
MMLU (5-shot)	88.7%	87.3%
HumanEval	90.2%	90.0%
MATH	76.6%	73.8%
MGSM	90.5%	96.8%

Key observations:

Set up your development environment:

Install Python and pip if you haven't already. Then, create a new directory for your project and set up a virtual environment:

Install Node JS

https://nodejs.org/en/download/package-manager

mkdir flask-vercel-app

Title

Ensemble Everything Everywhere: Multi-scale Aggregation for Adversarial Robustness

Introduction

The paper addresses the critical issue of adversarial examples that challenge the robustness and reliability of deep neural networks. The authors propose a novel approach that enhances adversarial robustness by leveraging multi-resolution input representations and dynamic self-ensembling of intermediate layer predictions. This method aims to align machine perception with human perception, particularly in the context of adversarial attacks that exploit the differences between the two.

Key Concepts

Adversarial Examples: These are small, often imperceptible perturbations applied to images that lead to misclassification by neural networks, despite remaining recognizable to humans. The existence of such examples highlights a significant gap between machine and human vision.

Title

Agentic Retrieval-Augmented Generation for Time Series Analysis

Overview

The proposed framework introduces an innovative approach to time series analysis through an Agentic Retrieval-Augmented Generation (RAG) model. This model is designed to tackle the inherent challenges of time series data, such as complex spatio-temporal dependencies and distribution shifts, by employing a hierarchical, multi-agent architecture.

Framework Architecture

Master Agent: The top-level agent that orchestrates the entire process by analyzing user requests and routing them to the appropriate specialized sub-agent.
Sub-Agents: Each sub-agent is tailored for specific time series tasks (e.g., forecasting, anomaly detection, imputation). They utilize smaller, pre-trained language models (SLMs) that are fine-tuned for their respective tasks.