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A cynic in me says this is one of the reasons large companies like deep learning so much. Unlike some other branches of AI, deep learning is not something that

by gambler 10y ago

A cynic in me says this is one of the reasons large companies like deep learning so much. Unlike some other branches of AI, deep learning is not something that can be easily used by individuals or smaller companies. It does not scale down very well if at all. So it makes a great basis for some expensive cloud service. They can develop it without fear that someone new will use it to disrupt their business.

tossaway322 10y ago
This. Why the broadspread interest in a technology so unpredictable, whose payoff is so little and that requires so much investment in hardware/developer time? Wouldn't you be better off learning tools you can understand and that can be used to build reliable and predictable programs? Why not warn graduate students: "You'll be working on this project for about two years. We don't know what to tell you about how to solve the problems involved. We don't have any good general guidelines; this field is changing all the time; nobody knows how these things work except in very broad terms (there is no explanatory power). Sometimes it takes years to find something useful. Luck is the key - if you're unlucky, you're screwed." "Nothing useful will come of your work when you're done. At completion you'll be dumber than when you started, because you will not have learned anything useful except possibly 'patience', a trait not valued in our field and sometimes viewed as equivalent to 'stupidity' or 'stubbornness'. You will, as a result of working with deep learning/NN, forget that sometimes you must cut your losses and quit exploring a particular solution path. At the end, you _may_ get a Master's degree, if you can show you made a good effort or even some progress. Three months after graduation, everything you have done will be obsolete and no firm will hire you: those familiar with NN because your knowledge is obsolete; those not in NN because they see no particular value in your training."

by gambler 10y ago

tossaway322 10y ago

This. Why the broadspread interest in a technology so unpredictable, whose payoff is so little and that requires so much investment in hardware/developer time? Wouldn't you be better off learning tools you can understand and that can be used to build reliable and predictable programs? Why not warn graduate students: "You'll be working on this project for about two years. We don't know what to tell you about how to solve the problems involved. We don't have any good general guidelines; this field is changing all the time; nobody knows how these things work except in very broad terms (there is no explanatory power). Sometimes it takes years to find something useful. Luck is the key - if you're unlucky, you're screwed." "Nothing useful will come of your work when you're done. At completion you'll be dumber than when you started, because you will not have learned anything useful except possibly 'patience', a trait not valued in our field and sometimes viewed as equivalent to 'stupidity' or 'stubbornness'. You will, as a result of working with deep learning/NN, forget that sometimes you must cut your losses and quit exploring a particular solution path. At the end, you _may_ get a Master's degree, if you can show you made a good effort or even some progress. Three months after graduation, everything you have done will be obsolete and no firm will hire you: those familiar with NN because your knowledge is obsolete; those not in NN because they see no particular value in your training."