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Just an observation on ML-- If the key to making ML useful has been huge sets of training examples, then that shows that current ML algorithms are much worse t

Just an observation on ML-- If the key to making ML useful has been huge sets of training examples, then that shows that current ML algorithms are much worse than humans at generalizing from a small number of examples. That suggests that current ML algorithms are leaving a lot on the table in terms of how much they are able to learn from each example, and there is a lot of room for improvement.

trentmc 10y ago
You're absolutely right - there is a lot of room for improvement. One of the big research threads in ML is better unsupervised learning - to learn from data without labels. Another hot topic is adversarial networks, which generate their own training data by playing against each other. (15 years ago this was "competitive co-evolution";) > If the key to making ML useful has been huge sets of training examples BTW this is only for some ML approaches and problems. There are some problems where getting more data is just way too expensive; e.g. building a model from silicon manufacturing where each mfg. run costs $50M; i.e. each datapoint costs $50M. Guess what - there are still useful ML-y things you can do here. Less extreme, there are many problems with only 100-1000 training points available, and you can still do a lot.