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This is a good point. Two operational differences I can think of between humans and ML/CV systems: 1) Humans can explain themselves. Many ML systems are not d

by antcas 8y ago

This is a good point. Two operational differences I can think of between humans and ML/CV systems: 1) Humans can explain themselves. Many ML systems are not designed to provide any explanation, even poor ones. 2) Humans are much more limited in the number of decisions they can make per second. A ML system could make millions of mistakes per seconds. If a system is capable of making many orders of magnitude more decisions than a similarly tasked human, then I think it's fair to push for a higher standard of accuracy and error logging than the human would be capable of. The point made, that both biological and artificial systems are susceptible to some of the same errors and bound by some of the same constraints, is well taken.

yorwba 8y ago
> Humans can explain themselves. Many ML systems are not designed to provide any explanation, even poor ones. Humans are good at generating sentences that appear to explain their decisions, but whether those are their actual reasons is unverifiable. Unless the decision is based on some predecided verbal rule, most such "explanations" are probably pretty useless. While I agree that most ML systems are not set up to explain themselves any better, the ability to get an explanation is something that can be bolted on after the fact. By providing the model with slightly modified inputs, it's possible to determine which kinds of input would have changed the decision if they had been different, i.e. the value of those inputs being as observed is the reason for the output. One possible implementation of that general idea is "Anchors: High-Precision Model-Agnostic Explanations" https://homes.cs.washington.edu/%7Emarcotcr/aaai18.pdf https://homes.cs.washington.edu/%7Emarcotcr/aaai18.pdf
throwawaymath 8y ago
> Humans can explain themselves. Many ML systems are not designed to provide any explanation, even poor ones. In my opinion, the vast majority of self-reported reasons given by humans to explain their behavior are extremely poor, inconsistent and low-fidelity.

by antcas 8y ago

yorwba 8y ago

> Humans can explain themselves. Many ML systems are not designed to provide any explanation, even poor ones. Humans are good at generating sentences that appear to explain their decisions, but whether those are their actual reasons is unverifiable. Unless the decision is based on some predecided verbal rule, most such "explanations" are probably pretty useless. While I agree that most ML systems are not set up to explain themselves any better, the ability to get an explanation is something that can be bolted on after the fact. By providing the model with slightly modified inputs, it's possible to determine which kinds of input would have changed the decision if they had been different, i.e. the value of those inputs being as observed is the reason for the output. One possible implementation of that general idea is "Anchors: High-Precision Model-Agnostic Explanations" https://homes.cs.washington.edu/%7Emarcotcr/aaai18.pdf https://homes.cs.washington.edu/%7Emarcotcr/aaai18.pdf

throwawaymath 8y ago

> Humans can explain themselves. Many ML systems are not designed to provide any explanation, even poor ones. In my opinion, the vast majority of self-reported reasons given by humans to explain their behavior are extremely poor, inconsistent and low-fidelity.