# Processing

Testing processing visualizations in Jekyll

# Solving XOR with a Perceptron

Solving the XOR problem using a simplel neural network

# A Brief History of the Electron

A brief history of the electron

# Physics of Fitness – Sled Push

Physics of the sled push. Here we see Neal Maddox at the 2014 CrossFit Games pushing a sled with short handles.

The sled push has appeared at the CrossFit Games in 2011, 2012, 2014, 2019, and 2020. Only in 2011 was the sled heavy. Every other event was a relatively light sled push. And there is something common with the fastest times with the sled push: they are mostly upright, not bent over like the heavy sled push. Yet, year after year, the world’s fittest athletes attack the sled push–whether in training or in competition–bent over.

# Feynman Net

Using GPT3 to answer introductory physics questions

## Introduction

The basic idea here starts with a question: How well can GPT3 trained on the Feynman Lectures answer physics questions? GPT3 (Generative Pre-trained Transformer 3) is OpenAI’s new autoregressive language model for natural language processing. This model has shown significant improvements in (see examples) the most basic tasks such as summarization, answering questions, and comprehension. What makes GPT3 so impressive is that it’s a generalized neural network with billions of parameters, and its success has only been rivaled with narrow focused neural networks. Here are some applications from beta testers.

# Coursera - Deep Learning Specialization

Coursera - Deep Learning Specialization

## Overview

Deep Learning is a the basis of general artifical intelligence. At the heart of this field are ‘neural nets’, which are mathematical nodes connected in specific ways to generate desired outcomes. The ways neural nets are connected hasn’t been developed into closed-form processes yet and the desired outcome is specific. The most infamous is Alpha Go.

# Coursera - Computational Social Science Specialization

Coursera - Computational Social Science Specialization

# Overview

Computational Social Science is a blending of computer science methods (namely ‘Data Science’), social sciences (the data source), and complexity science (e.g. networks). Some of my favorite papers:

• Bose-Einstein condensation in complex networks – this work connects Bose Einstein Condensates to complex networks, which can then be used to learn about the topology of networks. This application is discussed in the next paper.
• The physics of the Web – Barabasi explores the various intracies of networks, such as the basics (e.g. random vs scale-free), how these basics relate to power outages, and the internet. My favorite concept in this paper is the application of ‘fitness’. In short, the fitter a network node the easier it is for the node to make new connections. The concept is the rich-get-richer scenario, one seen across many domains.
• Prestige drives epistemic inequality in the diffusion of scientific ideas – Clauset argues that the spread of scientific ideas is like a competition. Great ideas are more fit, so they spread more easily. Considering that ideas originate from people, Clauset et al investigate the role of institutions. They find that ‘‘research from prestigious institutions spreads more quickly and completely than work of similar quality originating from less prestigious institutions.’’
• Good Fences: The Importance of Setting Boundaries for Peaceful Coexistence – Co-authored with the brilliant Yaneer Bar-Yam, the team was able to accurately predict ethnic violence based on social structures. They found that intermediate ‘patches’ of ethnicity (e.g. religion, language, etc) were the key factor in violence. When patches are small or large the violence minimizes. The intermediate sizes led to Us-vs-Them behaviors. Bar-Yam et al also found that places with distinct boundaries helped to mitigate violence (e.g. rivers, mountains). They propose three ways to resolve ethnic violence: (1) accelerate mixing, (2) accelerate separation, (3) a good fence.

# Dunbar’s Problem

Humanity lives in a world with a population that far exceeed Dunbar’s Number. How does this impact the stability of our civilization? IMAGE SOURCE

Dunbar’s Number is the limiting number of trusting relationships that a person can maintain. Tribes are known to naturally split after surpassing a population of 150 members. Splitting of the tribes creates a natural Us-vs-Them dynamic. This dynamic often leads to war between tribes unless the two groups can cooperative dynamics. As is typically seen in game simulations, a group in a cooperative environment can switch to retaliatory leading to complete dominance over all other groups. This begs the question, what amount of war is necessary for long term stability of peace?

# Graduate School Research - PhD

PhD work

### Introduction

Feynman, Schwinger, and Tomonaga were awarded the 1965 Nobel Prize in physics for their contributions to quantum electrodynamics. To date this theory–QED–is the most precise theory ever developed. It has the precision of predicting the distance between you and the moon with an error of measuring from your chin or the top of your head. However, Feynman also lamented

It seems that very little physical intuition has yet been developed in this subject. In nearly every case we are reduced to computing exactly the coefficient of some specific term. We hvave no way to get a general ideas of the result to be expected…We have been computing terms like a blind man exploring a new room, but soon we must develop some concept of this room as a whole, and to have some general idea of what is contained in it. As a specific challenge, is there any method of computing the anomalous moment of the electron which, on first rough approximation, gives a fair approximation to the $$\alpha$$ term…