image classification system as the supervised learning ai trains on the datait will be able to measure its performance (via cost functionby comparing its predicted image label with the true image label that we have on file the ai will explicitly try to minimize this cost function such that its error on never-before-seen images (from holdout setis as low as possible this is why labels are so powerful--they help guide the ai agent by providing it with an error measure the ai uses the error measure to improve its performance over time without such labelsthe ai does not know how successful it is (or isn'tin correctly classifying images howeverthe costs of manually labeling an image dataset are high andeven the best curated image datasets have only thousands of labels this is problem because supervised learning systems will be very good at classifying images of objects for which it has labels but poor at classifying images of objects for which it has no labels as powerful as supervised learning systems arethey are also limited at generalizing knowledge beyond the labeled items they have trained on since the majority of the world' data is unlabeledwith supervised learningthe ability of ai to expand its performance to never-before-seen instances is quite limited in other wordssupervised learning is great at solving narrow ai problems but not so good at solving more ambitiousless clearly defined problems of the strong ai type the strengths and weaknesses of unsupervised learning supervised learning will trounce unsupervised learning at narrowly defined tasks for which we have well-defined patterns that do not change much over time and sufficiently largereadily available labeled datasets howeverfor problems where patterns are unknown or constantly changing or for which we do not have sufficiently large labeled datasetsunsupervised learning truly shines instead of being guided by labelsunsupervised learning works by learning the underlying structure of the data it has trained on it does this by trying to represent the data it trains on with set of parameters that is significantly smaller than the number of examples available in the dataset by performing this representation learningunsupervised learning is able to identify distinct patterns in the dataset in the image dataset example (this time without labels)the unsupervised learning ai may be able to identify and group images based on how similar they are to each other and how different they are from the rest for exampleall the images that look like unsupervised learning in the machine learning ecosystem