Based on an analysis of #DHd conference abstracts, I trace the evolution of #CLS methods from 2014 to 2025: from omnipresent #NetworkAnalysis and #Annotation, to the first appearance of #TopicModeling and #SentimentAnalysis, to #DeepLearning and #GenerativeAI.

However, neither this method had a satisfactory data output that would suit the #RomChords project’s purposes. Even the state-of-the-art polyphonic detection algorithms (such as the one developed by Celemony Melodyne) produce a lot of noise, and cleaning the data to a standard usable for the project would be enormously laborious.

(Although it is possible that in a few years, progress in #DeepLearning will make this method much more effective and efficient).

#RomaniChords

7/20

@mcc #DeepLearning based software has been in use for quite some time now. We have seen all those toys like real-time video filters, image enhancers/upscalers, all that is basically the same thing as an image generator, the only new trick is coupling the image model with a language model and using random noise as input. Instead of giving the machine something ugly to prettify, you give it some grainy grey salt and pepper randomness like an analogue television tuned to a dead channel and tell it what kind of patterns to search for and amplify in that noise.
And LLMs are really just autocomplete on steroids. They literally just predict the next word and then the next one after that, just the next word that leads down one of the nearest pathways of high enough probability. It's a billion-dimensional landscape of language, but it doesn't know anything, it just has memorised everything and built a map of all the common patterns in all kinds of texts and how likely they are to occur in relation to all the preceding text.
It's just because LLMs have become quite good at producing text that looks like it has been written by a human being, people tend to believe they are actually intelligent. In fact, an #LLM is just a #ChineseRoom --no mind in there, just statistical rules about language.

The trick is to make models smaller and more lightweight, training them on much smaller, human curated, high quality, data sets, resulting in software that can be run on low-spec hardware like a smartphone or a laptop and still produce decent results. The training data sets can be managed by volunteer online communities, and people can help training those models by having their computers donate computing power, crunching numbers for the community open source AI.
There isn't any money to be made that way, just simple AI tools that can be used by anybody with a laptop, no Internet needed, that's why all the focus is on the humongous ML models instead. Models that need huge data centres for their training, are completely closed proprietary systems, and only run on the servers of their owners, forcing everybody to pay rent for their usage, payable in money or data, everyone doing business in machine learning is trying to get the world addicted to their stuff. Small standalone ML models trained for a more specific purpose can be made much more energy efficient and also quite often much more useful and reliable. Unlike the blockchain, for which few serious use cases exist outside of recording who "owns" some huge number, machine learning can actually be used to solve problems for which humans use their intelligence.

However, machine learning forces us to rethink the entire concept of "intellectual property". IMHO, it's just bonkers that somebody can claim to own a piece of information. As soon as anything is out, you can't stop anybody from using it in any way they like. You can't stop anybody from making hundreds of copies of photos, cutting them to pieces and reassembling them to collages. You also can't stop anybody from downloading images from the Internet or grabbing them from video and training #StableDiffusion models on those. You can't stop people from training fun sized LLMs on the ebook collections on their hard drives. The technology is out there, people know how to use it, they will use it and not care very much whether it is legal or not. Instead of trying to get the AI companies to pay for their unlicensed use of data, maybe we should just declare all AI research outside of intellectual property rights, putting all the resulting AI software in the public domain, and demand all source code to be public, free, and open. Also, nothing generated by any of those models should ever become anybody's intellectual property. THAT would be nice. Make it so that any AI which ever broke or bent any copyrights during training automatically turns into a public domain black hole which sucks everything it touches into the public domain. Let people generate as many AI hallucinations as they like, just tell them that they don't own the results, or rather, everybody does. Intellectual property sucks, anyway, even more than physical private property. If AI companies can't create I.P. based monopolies, they will never make any money, so they'll just fold if they are forced to publish everything free of charge, free to use.

@mcc It's not that #AI isn't real, but people are rather unaware of what "artifical intelligence" is. AI is a term that is and has always been a branding, a label used for marketing. It's nothing more that a bunch of several branches of computer science that are about solving problems with computers for which humans need intelligence. The idea of a hypothetical Artificial General Intelligence (AGI), an artificial mind that is as intelligent as a human or even superintelligent, has been around for as long as programmable universal computers, but it is basically just a myth, just a prophecy; a bunch of AI researchers from different branches of the AI research tree have been dreaming of the homunculus growing from their respective branch any time soon. For the last 15 years or so, it has been the #DeepLearning branch; a long time ago when I was little, it was the #ExpertSystem branch. Evolutionary algorithms might get back into the spotlight next, since machine learning is running out of steam, using bigger and bigger models and datasets for diminishing returns won't go on for very much longer. Basically, there are those who want to build an abstract model of a mind based upon philosophical theories of what intelligence might be, then there are those who want to model something resembling a brain with simple linear algebra, basically building huge billion-dimensional tensors and labelling them "artificial neural networks", and then there are the people who think you need a body and an environment with which you interact in order to become intelligent, and that the best way to become intelligent is some sort of artificial life that evolves. They are the ones who let algorithms "mutate" and then select those mutations that work better than the original one. They are the ones who build tiny robots and help them "evolve".

Well, with our current machine learning models and hardware, we won't get very much closer to anything like the human brain because an artificial neural network of that capacity would need a ludicrous amount of power and resources, we wouldn't be able to run any other software on any computer whatsoever on this planet because they all would be running a single instance of that artificial intelligence, and it still wouldn't be enough by several orders of magnitude. We would need all the power plants on this planet a thousand times over to do what a single human brain does powered by a piece of chocolate cake and a cup of tea. Unless there is some significant breakthrough very soon, an artificial neural network that rivals a human brain isn't going to happen anytime soon. Besides, I'm with the artificial life people, I don't believe a mind in a box is even possible, you need agency in the real world in order to become intelligent.