Summary

The Nagel-Schreckenberg (NaSch) model is a traffic flow model which uses used cellular automata to simulate and predict traffic on roads.

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Design of the Nagel-Schreckenberg Model

1. Discrete Space and Time:

   • The road is divided into cells, each representing a fixed length (e.g., a few meters).
   • Time advances in discrete steps.

2. Vehicle Representation:

   • Each cell is either empty or occupied by a single vehicle.
   • Each vehicle has a velocity (an integer) which determines how many cells it moves in a single time step.

Rules of the Model:

• The NaSch model uses local rules to update the state of each vehicle at every time step. These rules are:

1. Acceleration:

Summary

I started with this paper A developmentally descriptive method forquantifying shape in gastropod shells and bridged the results to a cellular automata approach.

An example of the shell we are modelling:

Steps

1️⃣ Identify the Key Biological Features

The paper outlines the logarithmic helicospiral model for shell growth, where:

• The shell grows outward and upward in a spiral shape.
• Parameters like width growth (\(g_w\)), height growth (\(g_h\)), and aperture shape dictate the final form.

These features describe how the shell expands over time in a predictable geometric pattern.

Summary

This page is the first in a series of posts about Cellular Automata.

I believe that we could get the first evidence of AI through cellular automata.

A recent paper Intelligence at the Edge of Chaos found that LLM’s trained on more complex data generate better results. Which makes sense in a human context like the harder the material is I study the smarter I get. We need to find out why this is also the case with machines. The conjecture of this paper is that creating intelligence may require only exposure to complexity.

Summary

Retrieval-Augmented Generation (RAG) is a powerful technique that enhances large language models (LLMs) by allowing them to use external knowledge sources.

An Artificial Intelligence (AI) system consists of components working together to apply knowledge learned from data. Some common components of those systems are:

• Large Language Model (LLM): Typically the core component of the system, often there is more than one. These are large models that have been trained on massive amounts of data and can make intelligent predictions based on their training.

Summary

Retrieval-Augmented Generation (RAG) has become the dominant approach for integrating external knowledge into LLMs, helping models access information beyond their training data. However, RAG comes with limitations, such as retrieval latency, document selection errors, and system complexity. Cache-Augmented Generation (CAG) presents an alternative that improves performance but does not fully address the core challenge of small context windows.

RAG has some drawbacks - There can be significant retrieval latency as it searches for and organizes the correct data.
- There can be errors in the documents/data it selects as results for a query. For example it may select the wrong document or give priority to the wrong document. - It may introduce security and data issues 2️⃣.
- It introduces complication
- an external application to manage the data (Vector Database) - a process to continually update this data when the data goes stale

LLM Agents

Agents are used enhance and extend the functionality of LLM’s.

In this tutorial, we’ll explore what LLM agents are, how they work, and how to implement them in Python.

What Are LLM Agents?

An agent is an autonomous process that may use the LLM and other tools multiple times to achieve a goal. The LLM output often controls the workflow of the agent(s).

What is the difference between Agents and LLMs or AI?

Agents are processes that may use LLM’s and other agents to achieve a task. Agents act as orchestrators or facilitators, combining various tools and logic, whereas LLMs are the underlying generative engines.

Some FREE AI courses on Agents I recommend doing

Agents were important in Machine Learning development last year.

These are some courses I have done and recommend they are all free.
A good reason to do courses and look at youtube videos is you will learn current applications of AI and may get ideas for new applications.

AI Agentic Design Patterns with AutoGen

AI Agentic Design Patterns with AutoGen

Topics: Agents, Microsoft, Autogen.

Using Ollama

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Introduction

Ollama is the best platform for running, managing, and interacting with Large Language Models (LLM) models locally. For Python programmers, Ollama offers seamless integration and robust features for querying, manipulating, and deploying LLMs. In this post I will explore how Python developers can leverage Ollama for powerful and efficient AI-based workflows.

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1️⃣ What is Ollama?

Ollama is a tool designed to enable local hosting and interaction with LLMs. Unlike cloud-based APIs, Ollama prioritizes privacy and speed by running models directly on your machine. Key benefits include:

Hugo: A Static Site Generator

In this post I give an introduction to what I think is the best static site generator: Hugo.

What is Hugo?

Hugo is an open-source static site generator written in Go. It takes structured content, often written in Markdown, and compiles it into static HTML, CSS, and JavaScript files.

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Setting Up Hugo: A Quickstart Guide

Follow these steps to set up your first Hugo site

**1️⃣ Install Hugo

First, ensure you have Hugo installed. Use your package manager of choice:

1️⃣ Introduction

In the era of AI-powered search, retrieval-augmented generation (RAG), and recommendation systems, efficient vector search is a necessity. While many vector databases exist, most require heavy infrastructure.

Enter ChromaDB: a lightweight, open-source vector database optimized for rapid prototyping and local AI applications.

2️⃣ What is ChromaDB?

Definition:

ChromaDB is a vector database for storing and querying embeddings. It provides an easy-to-use interface for AI developers to integrate similarity search into their applications.