Summary

In this blog I aim to try building using open source tools where possible. The benefits are price, control, knowledge and eventually quality. In the shorter term though the quality will trail the paid versions. My belief is we can construct AI applications to be self correcting sort of like how your camera auto focuses for you. This process will involve a lot of computation so using a paid service could be costly. This for me is the key reason to choose solutions using free tools.

Summary

This post demonstrates how to automatically transform a scientific paper (or any text/audio content) into a YouTube video using AI. We’ll leverage several powerful tools, including large language models (LLMs), Whisper for transcription, Stable Diffusion for image generation, and FFmpeg for video assembly. This process can streamline content creation and make research more accessible.

Overview

Our pipeline involves these steps:

1. Audio Generation (Optional): If starting from a text document, we’ll use a text-to-speech service (like NotebookLM, or others) to create an audio narration.
2. Transcription: We’ll use Whisper to transcribe the audio into text, including timestamps for each segment.
3. Database Storage: The transcribed text, timestamps, and metadata will be stored in an SQLite database for easy management.
4. Text Chunking: We’ll divide the transcript into logical chunks (e.g., by sentence or time duration).
5. Concept Summarization: An LLM will summarize the core concept of each chunk.
6. Image Prompt Generation: Another LLM will create a detailed image prompt based on the summary.
7. Image Generation: Stable Diffusion (or a similar tool) will generate images from the prompts.
8. Video Assembly: FFmpeg will combine the images and audio into a final video.

Prerequisites

• Hugging Face CLI: Install it to download the Whisper model: pip install huggingface_hub
• Whisper: Install the whisper-timestamped package, or your preferred Whisper implementation.
• Ollama: You’ll need a running instance of Ollama to access the LLMs.
• Stable Diffusion WebUI (or similar): For image generation.
• FFmpeg: For video and audio processing. Ensure it’s in your system’s PATH.
• Python Libraries: Install necessary Python packages: pip install pydub sqlite3 requests Pillow (and any others as needed).

**1️⃣ Audio Generation (Optional)

If you’re starting with a text document, you’ll need to convert it to audio. Several cloud services and libraries can do this. For this example, we’ll assume you have an audio file (audio.wav).

Summary

In this post I explore Robert Bridson’s paper: Fast Poisson Disk Sampling in Arbitrary Dimensions and provide an example python implementation. Additionally, I introduce an alternative method using Cellular Automata to generate Poisson disk distributions.

Poisson disk sampling is a widely used technique in computer graphics, particularly for applications like rendering, texture generation, and particle simulation. Its appeal lies in producing sample distributions with “blue noise” characteristics—random yet evenly spaced, avoiding clustering.

Introduction

Activation functions are a component of neural networks they introduce non-linearity into the model, enabling it to learn complex patterns. Without activation functions, a neural network would essentially act as a linear model, regardless of its depth.

Key Properties of Activation Functions

• Non-linearity: Enables the model to learn complex relationships.
• Differentiability: Allows backpropagation to optimize weights.
• Range: Defines the output range, impacting gradient flow.

In this post I will outline each of the most common activation functions how they are calculated and when they should be used.

Summary

In this post I will implement a Support Vector Machine (SVM) in python. Then describe what it does how it does it and some applications of the instrument.

What Are Support Vector Machines (SVM)?

Support Vector Machines (SVM) are supervised learning algorithms used for classification and regression tasks. Their strength lies in handling both linear and non-linear problems effectively. By finding the optimal hyperplane that separates classes, SVMs maximize the margin between data points of different classes, making them highly effective in high-dimensional spaces.

Summary

This post is about color wars: a grid containing dynamic automata at war until one dominates.

Implementation

The implementation consists of two core components: the Grid and the CellularAutomaton.

1️⃣ CellularAutomaton Class

The CellularAutomaton class represents individual entities in the grid. Each automaton has:

• Attributes: ID, strength, age, position.
• Behavior: Updates itself by aging, reproducing, or dying based on simple rules.

2️⃣ Grid Class

The Grid manages a collection of automata. It:

44. What does it mean to Fit a Model?

Answer
Fitting a model refers to the process of adjusting the model’s internal parameters to best match the given training data. It’s like tailoring a suit – you adjust the fabric and stitching to make it fit the wearer perfectly.

Key Terms:

1. Model: A mathematical representation that captures patterns in data. Examples include linear regression, decision trees, neural networks, etc.

2. Parameters: These are the internal variables within the model that determine its behavior. For instance:

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.