5 ms·
I once was obsessed at finding the holy Grail of optimization and scaling, but then came to the conclusion that every system is optimized and scaled differently
by z3t4 3y ago
I once was obsessed at finding the holy Grail of optimization and scaling, but then came to the conclusion that every system is optimized and scaled differently. Different systems have different bottlenecks.
- slively 3y agoSo true, rarely is anything the “best” or “better”, but instead each thing is a bucket of trade offs we choose from. Maybe frustrating, but also what makes engineering genuinely interesting.
- uptownfunk 3y agoThis sounds similar to the no free lunch theorem in AI
- jjgreen 3y agoHuh? NFL is a theorem on optimisation, not AI
- cubefox 3y agoIt's originally a theorem about supervised learning, which turned out to be the prototypical application (via CNNs) of deep learning. Deep learning is now mostly synonymous with AI. Though I'm not aware of any NFL theorem for reinforcement learning or unsupervised learning.
- mumblemumble 3y agoGiven that the most well-known use case for optimization algorithms is training machine learning models, it seems to me like a perfectly reasonable, if buzzword-oriented, thing to say.