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What are the implications for ML used in real-world situations with no training set (or a very limited one) that could have life or death consequences - passeng
by ilamont 7y ago
What are the implications for ML used in real-world situations with no training set (or a very limited one) that could have life or death consequences - passenger vehicles, industrial use, military, etc.?
Or is the consensus that it is a matter of time before compute and algorithms make these situations "safe enough," even for edge cases?
- The_rationalist 7y agoIt's clearly delusional to think that computer vision will go from soft computing (erroneous) to hard computing in less than a decade, at the rate of current incremental improvements. We will soon hit an accuracy wall that only breakthrough research will allow to beat. The problem being: there's too much research exploring the search space in the same direction and not enough foundational research.
- etaioinshrdlu 7y agoI disagree. There is a huge amount of research on adversarially robust classifiers and detectors going on. One can also programmatically test a neural network on real data, synthetically damaged data, fully synthetic data, and adversarial data, and everything in between. You can statistically ensure you get any desired accuracy level on those tests. While that's not a hard proof of anything, it can allow you to be very confident in the nets abilities.
- ElFitz 7y agoWell, anyway it doesn't seem worse than how we test our biological neural networks before letting them drive huge chunks of metal around
- mirekrusin 7y agoIs there an end here? Just google "optical illusion photos" or something similar.