I do want to push back a little bit on the anti-AI framing though. Really, I wanted to push back initially, but people are (rightfully!) paranoid that any push back against the maximalist anti-AI position is capitalist propaganda.
Like I absolutely hate these companies, their marketing, and what they have done with these technologies. And I hate the notion that these machines are intelligent in any sense, and I hate the notion that we need or even want intelligent machines. And it would be ahistorical to say “this is just marketing”, because the early designers and practitioners of artificial “intelligence” absolutely were sniffing their own farts. And I hate what AI slop has done to art and artists and the value of our art. And I hate the AI “aesthetic”. Please just do your own goddamn artwork. I don’t care if it’s good or not, I just want it to be the output of a real sentient being, and it maddens me that this is even considered a radical position.
…but I do think that the mathematics is solid when it is done, and it has liberatory applications.
math nerd shit
For example, classical statistical learning is very well-posed and mathematically careful, and has been used all the time in communications, web applications, bioinformatics, weather prediction, etc. Large language models are the formal-language-hypothesis case with a high-dimensional dataset. LLMs are not as well understood yet. Furthermore, the LLMs are usually part of a larger system. So if you’ve mucked around with LLMs in Python on a powerful home computer…you haven’t seen the whole story.
Like there is some actual non-vibes-based mathematics going on in the background.
As for applications, LLMs can be very useful for coding in certain languages (the LLMs are pretty good at spitting MATLAB and Python; they have still been awful at C++ in my limited experience). And they can be excellent for machine translation. (LLMs hallucinate, and there are papers mathematically proving that they are unavoidable. Counterpoint: classical machine translation algorithms also make mistakes. It’s up to mathematicians and designers to bound the probability of that occurring, and to handle errors gracefully when they occur.)
Emphasis here is on can be. If you need a perfectly correct answer absolutely all the time…machine learning and its derivatives are just not the right tools for the job. The mathematics says this right from the start. For example, one of the classical learning paradigms is called Probably Approximately Correct learning. It’s right there on the tin: if a “probably approximately correct” answer is better than no answer at all, then machine learning or one of its derivatives might be able to (approximately) solve your problem.
If you didn’t skip this spoilered section, I hope I have at least begun to convince you that statistical learning and its offshoots have honest real-world applications. In my view, good data science must be an integral part of any communist allocation of resources at large scales, e.g. production forecasting, weather prediction, logistics, etc.
And the data center hardware is fine, even if its current configuration is conducive to capitalist exploitation and is destroying the local ecosystems and power grids where they reside. Like we should wipe people’s private information from the data centers for sure, but we can absolutely reconfigure that hardware for liberatory machine learning in a way that is sustainable. Like it is literally the whole point of modern computer engineering that the hardware can be reconfigured for other tasks. Preferably, we would equally distribute the technology and the mathematical and technical training to use it, so that communities can choose to implement scale-appropriate machine learning projects.
The People’s Slop Machine
Yes, this would be a bad outcome, and it is possible if we seize the means of production without due regard for how it gets used in the future. But the “Slop Machine” part is not inevitable. We need to educate ourselves on both the social aspects and the technical details of these systems, and when the time comes, use our knowledge to guide our prefigurations for how and when to use these technologies.
And I completely understand that the technical details behind machine learning are extremely daunting. It’s been a multi-year project on my end, and I’m still learning. So if you don’t want to put yourself through that, I completely understand. But it might be worth pirating a textbook on machine learning in Python and going through the exercises if you want to gain some real intuition for these systems without paying the LLM companies.
more nerd shit
Raschka’s book is good if you don’t care about the theory, although it doesn’t focus on LLMs and predates ChatGPT. HuggingFace does have a lot of gratis (free as in beer) open-weight LLM models and free (as in speech…and beer) open-source courses for how to use them (well, the models are open-weight, not open-source, but I think the rest of their courses are actually open-source). I was able to run most of the courses locally on my modest hardware.
A socialist advocating for socialism?? WELL I NEVER!!
Socialism is when the bourgeois State owns 50% of the means of producing slop 🙄
The People’s Slop Machine
Some call it congress
I do want to push back a little bit on the anti-AI framing though. Really, I wanted to push back initially, but people are (rightfully!) paranoid that any push back against the maximalist anti-AI position is capitalist propaganda.
Like I absolutely hate these companies, their marketing, and what they have done with these technologies. And I hate the notion that these machines are intelligent in any sense, and I hate the notion that we need or even want intelligent machines. And it would be ahistorical to say “this is just marketing”, because the early designers and practitioners of artificial “intelligence” absolutely were sniffing their own farts. And I hate what AI slop has done to art and artists and the value of our art. And I hate the AI “aesthetic”. Please just do your own goddamn artwork. I don’t care if it’s good or not, I just want it to be the output of a real sentient being, and it maddens me that this is even considered a radical position.
…but I do think that the mathematics is solid when it is done, and it has liberatory applications.
math nerd shit
For example, classical statistical learning is very well-posed and mathematically careful, and has been used all the time in communications, web applications, bioinformatics, weather prediction, etc. Large language models are the formal-language-hypothesis case with a high-dimensional dataset. LLMs are not as well understood yet. Furthermore, the LLMs are usually part of a larger system. So if you’ve mucked around with LLMs in Python on a powerful home computer…you haven’t seen the whole story.
Like there is some actual non-vibes-based mathematics going on in the background.
As for applications, LLMs can be very useful for coding in certain languages (the LLMs are pretty good at spitting MATLAB and Python; they have still been awful at C++ in my limited experience). And they can be excellent for machine translation. (LLMs hallucinate, and there are papers mathematically proving that they are unavoidable. Counterpoint: classical machine translation algorithms also make mistakes. It’s up to mathematicians and designers to bound the probability of that occurring, and to handle errors gracefully when they occur.)
Emphasis here is on can be. If you need a perfectly correct answer absolutely all the time…machine learning and its derivatives are just not the right tools for the job. The mathematics says this right from the start. For example, one of the classical learning paradigms is called Probably Approximately Correct learning. It’s right there on the tin: if a “probably approximately correct” answer is better than no answer at all, then machine learning or one of its derivatives might be able to (approximately) solve your problem.
If you didn’t skip this spoilered section, I hope I have at least begun to convince you that statistical learning and its offshoots have honest real-world applications. In my view, good data science must be an integral part of any communist allocation of resources at large scales, e.g. production forecasting, weather prediction, logistics, etc.
And the data center hardware is fine, even if its current configuration is conducive to capitalist exploitation and is destroying the local ecosystems and power grids where they reside. Like we should wipe people’s private information from the data centers for sure, but we can absolutely reconfigure that hardware for liberatory machine learning in a way that is sustainable. Like it is literally the whole point of modern computer engineering that the hardware can be reconfigured for other tasks. Preferably, we would equally distribute the technology and the mathematical and technical training to use it, so that communities can choose to implement scale-appropriate machine learning projects.
Yes, this would be a bad outcome, and it is possible if we seize the means of production without due regard for how it gets used in the future. But the “Slop Machine” part is not inevitable. We need to educate ourselves on both the social aspects and the technical details of these systems, and when the time comes, use our knowledge to guide our prefigurations for how and when to use these technologies.
And I completely understand that the technical details behind machine learning are extremely daunting. It’s been a multi-year project on my end, and I’m still learning. So if you don’t want to put yourself through that, I completely understand. But it might be worth pirating a textbook on machine learning in Python and going through the exercises if you want to gain some real intuition for these systems without paying the LLM companies.
more nerd shit
Raschka’s book is good if you don’t care about the theory, although it doesn’t focus on LLMs and predates ChatGPT. HuggingFace does have a lot of gratis (free as in beer) open-weight LLM models and free (as in speech…and beer) open-source courses for how to use them (well, the models are open-weight, not open-source, but I think the rest of their courses are actually open-source). I was able to run most of the courses locally on my modest hardware.