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If you take data, and effectively do extremely lossy compression on it, there is still a way for that data to theoretically be recovered.

This is extremely wrong and your entire argument rests on this single sentence’s accuracy so I’m going to focus on it.

It’s very, very easy to do a lossy compression on some data and wind up with something unrecognizable. Actual lossy compression algorithms are a tight balancing act of trying to get rid of just the right amount of just the right pieces of data so that the result is still satisfactory.

LLMs are designed with no such restriction. And any single entry in a large data set is both theoretically and mathematically unrecoverable. The only way that these large models reproduce anything is due to heavy replication in the data set such that, essentially, enough of the “compressed” data makes it through. There’s a reason why whenever you read about this the examples are very culturally significant.

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What? I’ve already written the design documentation and done all the creative and architectural parts that I consider most rewarding. All that’s left for coding is answering questions like “what exactly does the API I need to use look like?” and writing a bunch of error handling if statements. That’s toil.

Definitely depends on the person. There are definitely people who are getting 90% of their coding done with AI. I’m one of them. I have over a decade of experience and I consider coding to be the easiest but most laborious part of my job so it’s a welcome change.

One thing that’s really changed the game recently is RAG and tools with very good access to our company’s data. Good context makes a huge difference in the quality of the output. For my latest project, I’ve been using 3 internal tools. An LLM browser plugin which has access to our internal data and let’s you pin pages (and docs) you’re reading for extra focus. A coding assistant, which also has access to internal data and repos but is trained for coding. Unfortunately, it’s not integrated into our IDE. The IDE agent has RAG where you can pin specific files but without broader access to our internal data, its output is a lot poorer.

So my workflow is something like this: My company is already pretty diligent about documenting things so the first step is to write design documentation. The LLM plugin helps with research of some high level questions and helps delve into some of the details. Once that’s all reviewed and approved by everyone involved, we move into task breakdown and implementation.

First, I ask the LLM plugin to write a guide for how to implement a task, given the design documentation. I’m not interested in code, just a translation of design ideas and requirements into actionable steps (even if you don’t have the same setup as me, give this a try. Asking an LLM to reason its way through a guide helps it handle a lot more complicated tasks). Then, I pass that to the coding assistant for code creation, including any relevant files as context. That code gets copied to the IDE. The whole process takes a couple minutes at most and that gets you like 90% there.

Next is to get things compiling. This is either manual or in iteration with the coding assistant. Then before I worry about correctness, I focus on the tests. Get a good test suite up and it’ll catch any problems and let you reflector without causing regressions. Again, this may be partially manual and partially iteration with LLMs. Once the tests look good, then it’s time to get them passing. And this is the point where I start really reading through the code and getting things from 90% to 100%.

All in all, I’m still applying a lot of professional judgement throughout the whole process. But I get to focus on the parts where that judgement is actually needed and not the more mundane and toilsome parts of coding.

The language model isn’t teaching anything it is changing the wording of something and spitting it back out. And in some cases, not changing the wording at all, just spitting the information back out, without paying the copyright source.

You could honestly say the same about most “teaching” that a student without a real comprehension of the subject does for another student. But ultimately, that’s beside the point. Because changing the wording, structure, and presentation is all that is necessary to avoid copyright violation. You cannot copyright the information. Only a specific expression of it.

There’s no special exception for AI here. That’s how copyright works for you, me, the student, and the AI. And if you’re hoping that copyright is going to save you from the outcomes you’re worried about, it won’t.

Makes sense to me. Search indices tend to store large amounts of copyrighted material yet they don’t violate copyright. What matters is whether or not you’re redistributing illegal copies of the material.

If I understand correctly they are ruling you can by a book once, and redistribute the information to as many people you want without consequences. Aka 1 student should be able to buy a textbook and redistribute it to all other students for free. (Yet the rules only work for companies apparently, as the students would still be committing a crime)

A student can absolutely buy a text book and then teach the other students the information in it for free. That’s not redistribution. Redistribution would mean making copies of the book to hand out. That’s illegal for people and companies.

It seems like a lot of people misunderstand copyright so let’s be clear: the answer is yes. You can absolutely digitize your books. You can rip your movies and store them on a home server and run them through compression algorithms.

Copyright exists to prevent others from redistributing your work so as long as you’re doing all of that for personal use, the copyright owner has no say over what you do with it.

You even have some degree of latitude to create and distribute transformative works with a violation only occurring when you distribute something pretty damn close to a copy of the original. Some perfectly legal examples: create a word cloud of a book, analyze the tone of news article to help you trade stocks, produce an image containing the most prominent color in every frame of a movie, or create a search index of the words found on all websites on the internet.

You can absolutely do the same kinds of things an AI does with a work as a human.

Wikipedia has a whole list of citations on this very sentence lol.

There is near unanimous consensus among economists that tariffs are self-defeating and have a negative effect on economic growth and economic welfare

https://en.m.wikipedia.org/wiki/Tariff

Tariffs are a net negative. Always. The things produced will not be competitive on the global market, if they were, we’d already be making them. The higher prices always destroy more jobs than they create. Retaliatory tariffs destroy even more jobs. The higher prices drive down demand and make the working class consumer poorer. Always.

There’s no economic upside to tariffs, over any time horizon. They create a small number of jobs in a specific sector at a very expensive cost. Some politicians might decide that the enormous economic cost is worth it for other reasons, but a net positive they are not.

My place of work has a pretty high rate of pronoun signaling and I’ve found it immensely useful. Not just for the usual androgynous names line Pat or Elliott, but also I work with people all around the world, how would you refer to Jung Bae? Judging by the number of foreign people who have never seen my name, I imagine it goes both ways. And, yes, I also work with a number of nonbinary and trans people so of course it helps there too.

Some people refer to everybody, even those they know, as they/them and I honestly kinda like it. Been considering taking that habit on myself.

I think when you consider the rate of advancement of any technological species, “roughly the same level as us” basically implies that they got started at exactly the same time. Even an extra thousand years of technological advancement would put them far ahead of us. A million years would put them unimaginably far ahead.

On a cosmic scale, that’s nothing. That’s a tight window and given the like 8 billion years that planets with the required elements have had to form, I would doubt that no other species had a chance to surpass us.

ITT: A bunch of people who have never heard of information theory suddenly have very strong feelings about it.

Models are not improving? Since when? Last week? Newer models have been scoring higher and higher in both objective and subjective blind tests consistently. This sounds like the kind of delusional anti-AI shit that the OP was talking about. I mean, holy shit, to try to pass off “models aren’t improving” with a straight face.

Love that the picture associated with this article is Trump staring into the eclipse. Fucking moron.

If we’re in a simulation, it’s probably a massive universe-spanning one. We’re just a blip, both within the scale of the space of the universe and within the history of time of the universe. In that case, we’re not important enough for a simulation creator to even care to adjust our capabilities at all. They’re not watching us. We’re not the point of the simulation.

It can’t be expressed in any integer-based notation without an infinite number of digits. Only when expressed in some bases which are themselves, irrational. It’s infinity either way.

The number which famously has an infinite number of digits? I thought we were arguing against the real-ness of infinity.

Also note: the method I was describing is one of the ways in which pi can be calculated.

It destroys meaningful operations it comes into contact with, and requires invisible and growing workarounds to maintain (e.g. “countably” infinite vs “uncountably” infinite) which smells of fantasy, philosophically speaking.

This isn’t always true. The convergent series comes to mind, where an infinite summation can be resolved to a finite number.