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Bit of an AI novice here, I did Norvig's course a few years ago and never worked in the field, but how can a machine take a "first pass" at labelling without be
by kareemsabri 7y ago
Bit of an AI novice here, I did Norvig's course a few years ago and never worked in the field, but how can a machine take a "first pass" at labelling without being trained? What information is it using to apply labels to the first set of data? How does this approach differ from a conventional classifier? Would the initial guesses essentially be random?
- plinkplonk 7y agoThe business model is, initially, selling human labeling services to owners of data (like Uber or Google), using third world cheap labor to keep costs low. This is very much like a call center service. Once a sufficiently large corpus of human labeled data is available (across clients and datasets probably), that labeled data is used to train a 'first pass' labeling system. It then becomes a virtuous cycle. Now the labeling is done in two phases. The first pass system makes its best guess, which is then reviewed by the existing human work force. Over time the first pass labeler gets better and better, till only very tricky/borderline cases need human intervention. The end game is anyone's guess. Pretty clever biz model hack.
- vadym909 7y agolike Google Search using human raters to verify their updated algorithms improve results
- streetcat1 7y agoSo they have a set of pretrained model that they are running as first pass and either verify the result with humans, or filter out the low confidence results and give them to humans. Note that this creates a positive feedback loop. I.e. as they get more results they can improve the initial stage.