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Ask HN: What is the state of other types of AI?
Hi HN,
This is a question I've found myself wondering a lot recently. There's obviously been lots of recent progress in the state-of-the-art for LLMs and generative AI, but is this progress trickling over to other areas of AI such as machine/deep learning?
If so, what are some resources to get up to speed quickly?
- PaulHoule 2y agoGenerative AI is is a subset of deep learning which is a subset of machine learning. Note there are a huge number of other ML approaches, two that are useful in tabular data (where LLMs tend to fail) are https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression https://scikit-learn.org/stable/modules/linear_model.html#lo... and https://scikit-learn.org/stable/modules/ensemble.html https://scikit-learn.org/stable/modules/ensemble.html of which XGBoost is still winning competitions. There is also the “old AI” based on logic that is doing well, see Donald Knuth’s notes on developments in SAT https://www.inf.ufrgs.br/~MRPRITT/lib/exe/fetch.php?media=inf5504:7.2.2.2-satisfiability.pdf https://www.inf.ufrgs.br/~MRPRITT/lib/exe/fetch.php?media=in...
- sk11001 2y agoIt’s good and useful. Some use cases in NLP have moved over to using LLMs, many use cases in NLP/vision are basically using pre trained models and adding some simple similarity search or simple classification on top of it. Then you have a bunch of deep learning and non-dl methods for forecasting, tabular data, search ranking etc.