Huang's Blog

👋 Welcome to my page where I share about interesting topics I learn on the way.

Currently I am a Master's student in Computer Science at ETH Zürich, visiting University of Oxford. I'm interested Human and Machine Intelligence, with a focus on the social aspects of coordination and cooperation.
At the same time, I work as a Research Assistant at the Jinesis Lab focusing on multi-agent systems. I imagine a future where the internet is ethically populated both by AI and humans.
You can find a set of university notes I've written in the Notes section.

#thoughts are preliminary readings and general musings on a topic.
#article are deeper dives, research summaries, detailed explorations, and reference material.

Through Humanity, Measures, and Impossibility

We analyze briefly phenomena of social-scientification of computer science, of measuring historically human things, and the classical idea of limits of human reach, contrapposed with the longing of wanting more and more.

On Norms and Instructions

This short note explores the connection of rules to the language and the difference between precise and ambiguous rules. It analyzes briefly the concept of intentions and the legislative process, with a focus on artificial societies and its future.

AI Social Norms

What we shall expect from AI, and how as a society we will need to engage with this problem.

Optimizing Single-Core CPU software

Fully exploiting the abilities of modern hardware is hard. Commonly written software does not fully exploit all of the underlying hardware optimization abilities, and often needs specialized software to achieve better utilization. This post will explore some of the most common optimization techniques for single-core binaries executing in modern hardware. We will cover concepts as Instruction-Level Parallelism, cache analysis for specific computations and briefly touch over vectorization. We conclude with a short example showcasing how these techniques are actually used and an analysis of these results. We report a maximum of 10x speedup over the standard naïve matrix-matrix multiplication code.

Hello World

First blog post