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I have an observation not related to chatgpt but to the debugging skills mentioned in the article: indeed I've always felt that most teaching is done on perfect
by amne 3y ago
I have an observation not related to chatgpt but to the debugging skills mentioned in the article: indeed I've always felt that most teaching is done on perfect working code. I've never seen an exercise for developing debugging skills. For example: "This Djikstra implementation is finding the wrong path." and work with students to pinpoint the off-by-one error. I think it would reveal so much more about the implementation details than just explaining how it works. It could be its own topic to explore race conditions, edge case studies and so on.
- kypro 3y agoDebugging is by far the most difficult thing when working with AI algorithms because the output is rarely deterministic and that makes it hard to tell the difference between actual bugs and a bad selection of hyperparameters. You're also typically working huge arrays of numbers so unless you have some way to visualise what's happening it's also difficult to pin point where the algorithm is doing something it shouldn't be. My main criticism of learning about algorithms using a framework is that they handhold so much with optimisation techniques and accessible APIs that you can know very little about what's actually happening and build something that works. When writing AI algorithms just knowing how to implement in theory is rarely ever enough. The difficulty is always in the debugging and optimisation techniques.