this post was submitted on 19 Jan 2025
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I personally think that's the most likely outcome. Most of the advances lately rely on effectively "brute forcing" the problem space by shoving more training data in and by using more resources to calculate weights. There are minor improvements here and there by combining approaches, but development of new techniques has largely slowed to a crawl.
There's also still no clear path for any of this tech to make the massive leap from "trained for a purpose" to generalized knowledge, which is the most pointed to "selling point" for the whole idea.
And all of that is ignoring the fact that OpenAI, the biggest name in the space, operates at a considerable loss. They only still exist because Microsoft can afford to burn the equivalent of a small country's GDP on the small chance they get to be an industry leader on this. The resource, money, and energy investment for the current results are so absurdly mismatched that unless something huge manages to shake things up, I have a very hard time seeing it ever reach the heights the hype machine has been prophecizing.
Machine Learning is amazing, has been improving all sorts of things for multiple decades, and will continue to do so long after this current overhyped idea of AI fades away. The current glorified chat bots, generative AI stuff? I think we're already well past the point of reasonable ROI in terms of resources.