this post was submitted on 08 Oct 2024
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Well, that's kind of shitty. I know those models can run up to five figures, and if those rules aren't enforced uniformly across the board for everyone then it does just seem like they're targeting a particular class of creator.
As a side note, I find it funny that the article refers to then as "AI models" when no AI is typically involved.
Saying it's AI even when it's completely irrelevant makes it modern and cool though.
The technical definition of AI in academic settings is any system that can perform a task with relatively decent performance and do so on its own.
The field of AI is absolutely massive and includes super basic algorithms like Dijsktra’s Algorithm for finding the shortest path in a graph or network, even though a 100% optimal solution is NP-Complete, and does not yet have a solution that is solveable in polynomial time. Instead, AI algorithms use programmed heuristics to approximate optimal solutions, but it’s entirely possible that the path generated is in fact not optimal, which is why your GPS doesn’t always give you the guaranteed shortest path.
To help distinguish fields of research, we use extra qualifiers to narrow focus such as “classical AI” and “symbolic AI”. Even “Machine Learning” is too ambiguous, as it was originally a statistical process to finds trends in data or “statistical AI”. Ever used excel to find a line of best fit for a graph? That’s “machine learning”.
Albeit, “statistical AI” does accurately encompass all the AI systems people commonly think about like “neural AI” and “generative AI”. But without getting into more specific qualifiers, “Deep Learning” and “Transformers” are probably the best way to narrow down what most people think of when they here AI today.
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