←back to thread

251 points slyall | 2 comments | | HN request time: 0s | source
Show context
DeathArrow ◴[] No.42058383[source]
I think neural nets are just a subset of machine learning techniques.

I wonder what would have happened if we poured the same amount of money, talent and hardware into SVMs, random forests, KNN, etc.

I don't say that transformers, LLMs, deep learning and other great things that happened in the neural network space aren't very valuable, because they are.

But I think in the future we should also study other options which might be better suited than neural networks for some classes of problems.

Can a very large and expensive LLM do sentiment analysis or classification? Yes, it can. But so can simple SVMs and KNN and sometimes even better.

I saw some YouTube coders doing calls to OpenAI's o1 model for some very simple classification tasks. That isn't the best tool for the job.

replies(10): >>42058980 #>>42059047 #>>42059100 #>>42059544 #>>42059813 #>>42060244 #>>42060447 #>>42060561 #>>42060833 #>>42062658 #
1. empiko ◴[] No.42059544[source]
Deep learning is easy to adapt to various domains, use cases, training criteria. Other approaches do not have the flexibility of combining arbitrary layers and subnetworks and then training them with arbitrary loss functions. The depth in deep learning is also pretty important, as it allows the model to create hierarchical representations of the inputs.
replies(1): >>42060613 #
2. f1shy ◴[] No.42060613[source]
But is very hard to validate for important or critical applications