Basically Just a Linux Diary

A while ago, I asked my girlfriend what programming language it did its work in. It said, "Ruby on Rails." Since it was working at that moment, I googled what that was. Soon after, I ended up on the "Machine code" Wikipedia page, more confused than ever. It compared this to telling me that it was an Impressionist painter, and ten minutes later seeing me on the Wikipedia page for "Shapes."

I've decided to take up a computer-learning journey. I'll hit it from a couple angles. There's learning HTML, CSS, Javascript, and Ruby. There's wading my way through using Linux for the first time. There's also regular old computer science, starting from "how does a circuit work." I learn better when I write stuff down, so here's where I'll do that.

Notes from AI Ethics by Mark Coeckelbergh

Great book! Very understandable for me, a computer illiterate. It also challenged my anti-AI bias in a couple of ways. It provides a quick history of AI and contemporary uses of it. Coeckelbergh goes from the symbolic AI and expert systems of the 80s to the connectionist neural networks of now. I had vibed out what AI, software, and algorithms were before, but this cleared it up:

AI is technologically simulated intelligence. While we usually mean human intelligence, that doesn't necessarily have to be the bar. AI is software, and software is made up of algorithms. Algorithms are a set of instructions that transform input data into output data. 

Machine learning, what we think of as "AI" nowadays, is

"A machine or software that can automatically learn: not in the way humans learn, but based on a computational and statistical process. Feeding on data, learning algorithms can detect patterns or rules in the data and make predictions for future data."

Machine learning and the subfield deep learning often use neural networks. Neural networks are,

"...based on the idea that instead of representing higher cognitive functions, we need to build interconnected networks based on simple units. Proponents claim that this is similar to how the human brain works: cognition emerges from interactions between simple processing units, called “neurons” (which, however, are not like biological neurons). Many interconnected neurons are used."

A previously popular type of AI, symbolic AI, is often based on decision trees. Decision trees require that decisions or rules be made based on data ahead of time, and that those rules are coded into the system by programmers. With machine learning, the rules come from patterns recognized by the AI and are not coded ahead of time. They are unknown to us, a "black box."

AI is already used in many facets of our lives, including in ways we might not immediately expect. I knew about recommendation systems and video game NPCs, but I didn't know that autopilot and self-driving cars used AI too. There's also the different subfields of AI like, "machine learning, computer vision, natural language processing, expert systems, evolutionary computation, and so on." These subfields interact with these facets in various ways that understandably aren't covered in full.

Coeckelbergh separates the actual issues with AI from the "Frankenstein complex" that imagines every AI to be a supergenius Terminator-in-waiting. In reality,

"There seems to be a real risk that in the near future the systems will not be smart enough and that we will insufficiently understand their ethical and societal implications and nevertheless use them widely."

There's also the ethical points of how much moral agency AI has, whether we should be the ones to prescribe it moral status at all, how good AI is at simulating human connection, how low the bar is for using AI maliciously, the inscrutability of AI to the public, explainability, and energy consumption.

I learned about Bostrom's paperclip maximizer and Searle's Chinese room thought experiments from this book too! More to read for later :]

Fun Stuff

*all of this was shown to me by my girlfriend and/or friends

The XZ Backdoor Hack. Guy notices a very small performance issue with Debian, upends the carefully-cultivated plan taking place over years that would've been disastrous for users.

Evil Map was shown to me when I started getting into Linux and learning that it was not, in fact, a sole operating system but a family. In the words of Jacob Geller, "this clears everything up!!!"

Origins of the GNU/Linux copypasta. According to Wikipedia fumbling, Richard Stallman's project GNU tried to make their own free and open-source operating system. They got most of the way there, but struggled with making the kernel work. Linux Torvalds had separately built his own kernel based on Unix. Multiple people at GNU ported in his kernel and boom, Linux as we know it. Stallman is stalwart about referring to Linux as GNU/Linux, hence the copypasta. I don't really know enough about intellectual property or whatever to make a judgement

Richard Stallman is also famous for, unprompted, eating something from his foot once. The video is age-restricted on Youtube.