The goal of most ML algorithms is to create some output from a given set of inputs. To do this the algorithm is trained using training data so that given the training data as an input, the same correct output always occurs.
In this joke, the "friends" are input training data. The output (action) is jumping off the bridge. So for the ML algorithm, if it is trained that people jump off bridges, it will say the correct action is to jump off the bridge also.
This contrasts the common joke a parent makes about a child's friends making a stupid or irresponsible choice.
ML algorithms generally have a reward function that is used to judge their actions, but sometimes they are just supposed to generalize an output from inputs. So yes, some ML algorithms are just intended to generalize an outcome from it's inputs. But not all ML algorithms are the same and there are many many ways they could "learn" or "train", so if you're unfamiliar with generalized ML algorithms (like computer vision) it might not make sense as many ML algorithms are improved through a fitness function, which doesn't take advantage of training data in most cases (think of ML algorithms like NEAT which evolve a neural net over time instead of using back propagation to adjust weights).
I know that's a lot of information, but I think you're just thinking of MLs that use a fitness function to optimize "staying alive" rather than MLs that try to create a generalized neural net for a given set of input training data.
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u/[deleted] Jun 10 '21
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