![]() ![]() And once an algorithm has been trained and tested for accuracy, humans still have to engineer it into software, market it and - the list goes on.Ĭlearly, there’s plenty of work for people in this seemingly automated field, but landing a role like machine learning engineer requires cutting-edge technical knowledge. With too little data, though, the algorithm works flawlessly on its training dataset only to flop in the real world. If an algorithm gets too much data, it can “ overfit,” incorporating meaningless correlations into its model. Humans build the algorithms and curate training datasets for them, which is no simple task. ![]() In that way, machine learning algorithms are groundbreakingly independent, capable of something many humans struggle with: self-improvement.Īt the same time, machine learning is a human creation. ![]()
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