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>Model training, though, is an up-front cost, not dissimilar to the cost needed to buy those Sun servers and Cisco routers in the dot-com era... This is only p
by vannevar 3y ago
>Model training, though, is an up-front cost, not dissimilar to the cost needed to buy those Sun servers and Cisco routers in the dot-com era...
This is only partially true even now, and I expect it will be less true in the future as continuous training becomes the norm. As Hericlitus said, you cannot step in the same river twice, and data drift is a real issue when modeling complex processes. It will become even more of an issue as the widespread use of models starts to impact the data streams they rely on for training: acting on a prediction can change the underlying assumptions that produced the prediction in the first place.
- version_five 3y agoNot that I disagree but I think this is what's going to pop the AI bubble. It doesn't actually work that great for a lot of stuff, anything that requires true generalization as opposed to just fitting a particular distribution. That doesn't make it useless, but it makes it way less useful than people believe in waves of optimism. It's what killed the last hype cycle and it will again.
- gmerc 3y agoIt didn’t pop the cloud bubble that you have to do constant maintenance. In the end, a new class of experts arises to handle these challenges, paid for by the layoffs of these made redundant by AI