ML is a much broader field than just neural networks. The hype for ML, in general, I think is warranted. We hit an inflection point when AWS launched and scalable processing power became cheap. It became cheap to process tons of data and generate insights. I don't have hard numbers on this, but probably 90-95% of machine learning used in practice is NOT neural networks, and have accuracies in the 90%+ arena. So ML in general -- sure, hype warranted.
Neural networks are the new hot topic, and the hype isn't fully warranted yet. TensorFlow made them very popular in the developer community; this is a good thing because it's spurring more investment and research in ANNs. But for any given problem, odds are that a neural network is not the best (ie, most accurate or cheapest) way to solve it. Neural networks do have specific problem domains where they are the state of the art, but for most other problem domains there exists a better solution. So I'd say that neural networks are a little over-hyped right now, but with a new generation of developers learning about and experimenting with ANNs, that will change in a few years. I think we're about to see an explosion of ANN usefulness over the next few years.
TLDR: ML is very useful but is more than neural networks; neural networks need a little more progress to catch up to the tensorflow hype.
ML is a much broader field than just neural networks. The hype for ML, in general, I think is warranted. We hit an inflection point when AWS launched and scalable processing power became cheap. It became cheap to process tons of data and generate insights. I don't have hard numbers on this, but probably 90-95% of machine learning used in practice is NOT neural networks, and have accuracies in the 90%+ arena. So ML in general -- sure, hype warranted.
Neural networks are the new hot topic, and the hype isn't fully warranted yet. TensorFlow made them very popular in the developer community; this is a good thing because it's spurring more investment and research in ANNs. But for any given problem, odds are that a neural network is not the best (ie, most accurate or cheapest) way to solve it. Neural networks do have specific problem domains where they are the state of the art, but for most other problem domains there exists a better solution. So I'd say that neural networks are a little over-hyped right now, but with a new generation of developers learning about and experimenting with ANNs, that will change in a few years. I think we're about to see an explosion of ANN usefulness over the next few years.
TLDR: ML is very useful but is more than neural networks; neural networks need a little more progress to catch up to the tensorflow hype.