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We're not that far from a major turning point. Currently these models don't provide an adequate enough confidence measure that prevents them from maximizing th
by preommr 3y ago
We're not that far from a major turning point.
Currently these models don't provide an adequate enough confidence measure that prevents them from maximizing their potential. In the next few years we're going to reach a point where models will be able to tell if something is possible and avoid hallucinating, guaranteeing much better correctness. Something like that would be absolutely killer.
If you add on a top-down approach using a framework, such that it can architect a system down into small individual components, then that's a recipe for a really great workflow. The models we have now really shine in doing automated unit tests, and small bits of code to avoid limits with context size. Making the interfaces obvious enough, and being able to glue things together using obvious connections seems very possible.
I really do think that in the next few years we're going to see one of these tools really do well.
- soacm 3y agoNo one is taking in consideration output decay? What data are these tools going to use 10 years from now? I am not an ML engineer but these models are not able to reason and are just as good as the training set. Please debate on these points.