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This is not a problem inherent to machines. If I teach a baby the word "tank" by pointing to a picture of a sunny tank and the word "forest" by pointing to a p

This is not a problem inherent to machines. If I teach a baby the word "tank" by pointing to a picture of a sunny tank and the word "forest" by pointing to a picture of a night forest, the baby could be correct in deducing "tank" meant day and "forest" meant night. This is not even specific to deep neural nets (DNNs). Even something like line fitting (OLS) will have problems with it. Classical statistics has a great way to deal with this, which is that your training data for classifying a label A against B should encompass the entire range of A and B that you want to use this machine on. This means if you want to identify a picture of tanks in a situation x, you better have a training set of tanks that "overlay a neighborhood" of x. But maybe the article isn't talking about the ability to train MLs, or how humans will be better than MLs, but that it's scary to have machines that can decide things and we don't know how they did it (or more precisely, the meaning of how they did it). That's reasonable, but I don't think operationally any different that if you ask a human who misclassified something and she just said "well uh, it just kinda looked like B from this angle / from a glance..."

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.
pron 8y ago
> If I teach a baby the word "tank" by pointing to a picture of a sunny tank and the word "forest" by pointing to a picture of a night forest, the baby could be correct in deducing "tank" meant day and "forest" meant night. I am not at all sure this is the case. It is possible that humans do primitive statistical classification on sensory signals only at very shallow, subconscious, levels of cognition, but not at levels they normally use words for.
- throwawaymath 8y ago
  When you say you're not sure, are you saying you're not sure children could be confused like that? I've witnessed it before. Especially for very young children, it can take several repetitions of mapping a word to different depictions of an object before they realize what you're talking about.
  - pron 8y ago
    They can be confused, but not quite in the same manner as a statistical clustering algorithm. Children (or adults) don't seem to learn by statistical clustering. It's often said that NNs require many samples while humans normally just need one, but a more precise description is that humans need zero. I can teach you to recognize a Chinese character without showing you even a single picture. It is true that this requires some background knowledge that small children lack, but there is little reason to believe that their learning is in any way similar to statistical clustering.
    - throwawaymath 8y ago
      How would you teach me to recognise a Chinese character without showing me a picture?
      - pron 8y ago
        A square, divided into four equal squares, floating symmetrically inside a larger rectangular U whose two vertical sides slightly protrude lower than the horizontal bar, and above the square, a horizontal line, slightly longer than the side of the square and equal in length and alignment to the horizontal line below the square, the base of the U.
throwaway2048 8y ago
I dont think many babies need to see thousands of examples of tanks before they understand what a tank is, and even then sometimes mysteriously classify horses as tanks when one pixel changes.
- cdoxsey 8y ago
  You've nailed. Language is a great examply where our intuition about how one learns is wildly incorrect. Babies are prewired to rapidly learn language, merely by hearing it around them. They do this regardless of the language, with teachers who have no idea what they're doing. It's an incredible process but because it happens so easily it leads us to think teaching a computer these things must not be all that hard. But we're not generic computing devices, and our linguistic developmental skills are innate and incredibly well adapted. We just can't see it so we take it for granted.
- techbio 8y ago
  They understand something simpler earlier: a name/word for something [a noun] is in the set ("manmade object", "animal", "neither of these"). Classifiers built on classifiers referring to classifiers that make them laugh or cry; new knowledge is not built on random initiation weights. It is based on physiological imperatives that start before any of us were born. More data, more generations, more generalization.
- stickfigure 8y ago
  A baby has 100 billion neurons and 100 trillion to 1 quadrillion synapses, so they have a rather large advantage (as of yet). That said, I'm not sure babies are as clever as you suggest. By the time they can verbalize "tank" they've had a vast amount of neural training. Try running this experiment with a 6-month-old; I'll bet on the program.

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.

pron 8y ago

> If I teach a baby the word "tank" by pointing to a picture of a sunny tank and the word "forest" by pointing to a picture of a night forest, the baby could be correct in deducing "tank" meant day and "forest" meant night. I am not at all sure this is the case. It is possible that humans do primitive statistical classification on sensory signals only at very shallow, subconscious, levels of cognition, but not at levels they normally use words for.

throwawaymath 8y ago
When you say you're not sure, are you saying you're not sure children could be confused like that? I've witnessed it before. Especially for very young children, it can take several repetitions of mapping a word to different depictions of an object before they realize what you're talking about.
- pron 8y ago
  They can be confused, but not quite in the same manner as a statistical clustering algorithm. Children (or adults) don't seem to learn by statistical clustering. It's often said that NNs require many samples while humans normally just need one, but a more precise description is that humans need zero. I can teach you to recognize a Chinese character without showing you even a single picture. It is true that this requires some background knowledge that small children lack, but there is little reason to believe that their learning is in any way similar to statistical clustering.
  - throwawaymath 8y ago
    How would you teach me to recognise a Chinese character without showing me a picture?
    - pron 8y ago
      A square, divided into four equal squares, floating symmetrically inside a larger rectangular U whose two vertical sides slightly protrude lower than the horizontal bar, and above the square, a horizontal line, slightly longer than the side of the square and equal in length and alignment to the horizontal line below the square, the base of the U.

throwaway2048 8y ago

I dont think many babies need to see thousands of examples of tanks before they understand what a tank is, and even then sometimes mysteriously classify horses as tanks when one pixel changes.

cdoxsey 8y ago
You've nailed. Language is a great examply where our intuition about how one learns is wildly incorrect. Babies are prewired to rapidly learn language, merely by hearing it around them. They do this regardless of the language, with teachers who have no idea what they're doing. It's an incredible process but because it happens so easily it leads us to think teaching a computer these things must not be all that hard. But we're not generic computing devices, and our linguistic developmental skills are innate and incredibly well adapted. We just can't see it so we take it for granted.
techbio 8y ago
They understand something simpler earlier: a name/word for something [a noun] is in the set ("manmade object", "animal", "neither of these"). Classifiers built on classifiers referring to classifiers that make them laugh or cry; new knowledge is not built on random initiation weights. It is based on physiological imperatives that start before any of us were born. More data, more generations, more generalization.
stickfigure 8y ago
A baby has 100 billion neurons and 100 trillion to 1 quadrillion synapses, so they have a rather large advantage (as of yet). That said, I'm not sure babies are as clever as you suggest. By the time they can verbalize "tank" they've had a vast amount of neural training. Try running this experiment with a 6-month-old; I'll bet on the program.