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 wit

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.