> Predicting the grade of an essay isn't fundamentally different than classifying an email as spam https://media.giphy.com/media/3oz8xLd9DJq2l2VFtu/giphy.gif

> Predicting the grade of an essay isn't fundamentally different than classifying an email as spam https://media.giphy.com/media/3oz8xLd9DJq2l2VFtu/giphy.gif https://media.giphy.com/media/3oz8xLd9DJq2l2VFtu/giphy.gif It is fundamentally very different, as the former requires domain knowledge (i.e. to a larger extent) and the latter does not (i.e. to a lesser extent). It's just not that simple; even in doing the things you mention, the algorithm will be marginally better than a different algorithm. The magic lies in the 1% that the algorithm misclassifies, not the 99% that it doesn't. The ML community is centered around establishing high classification rates of static data sets. This will soon change, when people realize "99.9% accuracy on a fixed data set" is a meaningless metric for real artificial intelligence, not the monkey-see-monkey-do stuff that is currently considered "state-of-the-art". There's about to be a big reckoning in this field, similarly to what happened in 1905.