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All of which can be reformulated as predictions: * Predict which strategy will arise given a metric to optimize * Predict the next action a human operator wou

All of which can be reformulated as predictions: * Predict which strategy will arise given a metric to optimize * Predict the next action a human operator would perform here * Predict which action yields the most likes / smiles / upvotes * Predict which output will have the most citations if it was an article in a scientific journal Your remark isn’t a rebuttal but a reaffirmation. You have fallen prey to the bias of not thinking in sufficiently high generality.

logicallee 8y ago
True but vacuously so. Any system that has output can be rephrased the way you just phrased it: * Photoshop predicts the output of a given a set of buttons, filters, UI states, etc. * Your car's steering system predicts the wheel outputs given steering wheel inputs. * The abs( ) operator predicts the absolute value of a number. -- If we want to make a funny analogy with ML using my first example: this model -- i.e. the entire Photoshop software -- is trained in a slow and manual (not automated) iterative process against the cost function "whether Photoshop engineers and managers will ship it as the next version of Photoshop". It's repeatedly tested against it (or through whatever training algorithm its designers want, including waterfall. The training algorithm doesn't have to be good - but anything they use to design the software by definition is the training algorithm for the model - under this stretched analogy / way of thinking about it.) I just mention this to show the absurdity of this way of thinking about it - like the entire Adobe campus is just one giant training algorithm for the "next version of Photoshop" model which predicts "what will the output of pressing these buttons be". If you'd like a second example: any simple pocket calculator going back to 1970 is a system that "predicts the result of its operations and operands". The cost function is the happiness of the engineers who designed it, and a human is involved in the iterative method of training the model, whose cost function is the human's happiness with it. Kind of an absurd way of thinking about the system. So while these (and anything else with an output) can be formulated as "predictions", my examples going back to 1970 aren't machine learning, since a human is involved in this training loop. So this sense of "prediction" is kind of specious. Sure, you can call them all predictions but you don't gain any insight by doing so. And you lose OP's point, which I thought was insightful.
- robertk 8y ago
  Ok that is clever. I take your point!

logicallee 8y ago

True but vacuously so. Any system that has output can be rephrased the way you just phrased it: * Photoshop predicts the output of a given a set of buttons, filters, UI states, etc. * Your car's steering system predicts the wheel outputs given steering wheel inputs. * The abs( ) operator predicts the absolute value of a number. -- If we want to make a funny analogy with ML using my first example: this model -- i.e. the entire Photoshop software -- is trained in a slow and manual (not automated) iterative process against the cost function "whether Photoshop engineers and managers will ship it as the next version of Photoshop". It's repeatedly tested against it (or through whatever training algorithm its designers want, including waterfall. The training algorithm doesn't have to be good - but anything they use to design the software by definition is the training algorithm for the model - under this stretched analogy / way of thinking about it.) I just mention this to show the absurdity of this way of thinking about it - like the entire Adobe campus is just one giant training algorithm for the "next version of Photoshop" model which predicts "what will the output of pressing these buttons be". If you'd like a second example: any simple pocket calculator going back to 1970 is a system that "predicts the result of its operations and operands". The cost function is the happiness of the engineers who designed it, and a human is involved in the iterative method of training the model, whose cost function is the human's happiness with it. Kind of an absurd way of thinking about the system. So while these (and anything else with an output) can be formulated as "predictions", my examples going back to 1970 aren't machine learning, since a human is involved in this training loop. So this sense of "prediction" is kind of specious. Sure, you can call them all predictions but you don't gain any insight by doing so. And you lose OP's point, which I thought was insightful.

robertk 8y ago
Ok that is clever. I take your point!