4 ms·
It has a well define loss function with a numerical value. The improvement of this value can be a type of difficulty. Check some other comments I’ve made on thi
by alchemist1e9 4y ago
It has a well define loss function with a numerical value. The improvement of this value can be a type of difficulty. Check some other comments I’ve made on this post for how it might work.
- pshc 4y agoIt's an interesting idea for sure, but loss doesn't go down forever. I think this ends with a highly overfitted network that grinds to a halt as the loss function hits local minima. Even if you get past that, there's no consensus mechanism or finalization as it stands, and validating solutions is relatively expensive.
- alchemist1e9 4y agoWe only just started thinking about this and I suspect these issues are solvable in a protocol. For instance using cross validation there must be a distributed protocol to control over fitting. I’m not sure validation is so expensive if the data is small enough. Actually maybe that’s a way to approach this, two type of block that are paired and share the rewards in some way. One that proposes better a better splice of weights and another that proves they are better out of sample. Give it a few weeks and with GPT-4s help I think we can find some promising approaches.