I'm referring to the below image which I came across.

enter image description here

The explanation seemed intuitive at first but I don't think I understand how it works. The image says that the performance of traditional machine learning algorithms plateau after a certain amount of data while that of deep learning algorithms gets better with more data. Does it have to with the feature learning where deep learning methods automatically learn the important features as opposed to the manual feature selection for machine learning models? If so, can you please elaborate on that.


In part feature learning has a lot to do with the fact that deep learning models can learn more data distributions. Although the graph that you are showing isn't completely true. For instance you can see the No Free Lunch Theorem for machine learning, that states that no machine learning model is universally any better than any other.

Either way, the goal of machine learning is not to seek a universal learning algorithm. Instead our goal is to understand what kind of dat distributions are relevant to the the real world that an AI agent experiences, and what kinds of machine learning algorithms performs well on data drawn from the kinds of data generating distributions we care about.

In conclusion, when facing a machine learning problem you should try to find the model that best learns the kind of data you are trying to work with and this graph is poorly informative and has no foundations.

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