Diminishing returns
Comment on Do you think Google execs keep a secret un-enshittified version of their search engine and LLM?
magiccupcake@lemmy.world 1 day agoDon’t forget the fundamental scaling properties of llms, that openai even used as the basis for strategy to make chat gpt 3.5.
But basically llm performance is logarithmic. It’s easier to get rapid improvements early on. But at later points like we are now require exponentially more compute, training data, and model sizes to get now small level of improvements.
Even if we get a 10x in compute, model size, and training data (which is fundamentally finite), the improvements aren’t going to be groundbreaking or solve any of the inherent limitations of the technology.
dyathinkhesaurus@lemmy.world 22 hours ago
TropicalDingdong@lemmy.world 1 day ago
💯
Scaling is a quick and dirty way to get performance improvements. But there is no guarantee that we get any more interesting behavior or don’t get like, wildly more interesting behavior with an additional 10x’ing. The fact is we simply don’t know what emergent properties might exist at a networks size we simply phsyically can’t scale to right now. Its important to not assume things like “its just going to be diminishing returns”, because while that is most likely the case, precisely this thinking is why google wasn’t the first to make an LLM, even though they had discovered/ invented the underlying technology. Yet another 10x scaling didnt result in just diminishing returns, but fundamentally new network properties.
And that principal holds across networked systems (social networks, communication networks, fungal and cellular communication networks). We truly do not know what will result from scaling the complexity of the network. It could move the needle from 95% to 96.5% accuracy. Or it could move it to a range of accuracy that isn’t measurable in human terms (its literally more accurate than we have the capability of validating). Or it could go from 95% to 94%. We simply don’t know.