Meta's prototype moderation AI only needs a few examples of bad behavior to take action


Moderating content on today's internet is akin to a round of Whack-A-Mole with human moderators continually forced to react in realtime to changing trends, such as vaccine mis- and disinformation or intentional bad actors probing for ways around established personal conduct policies. Machine learning systems can help alleviate some of this burden by automating the policy enforcement process, however modern AI systems often require months of lead time to properly train and deploy (time mostly spent collecting and annotating the thousands, if not millions of, necessary examples). To shorten that response time, at least to a matter of weeks rather than months, Meta's AI research group (formerly FAIR) has developed a more generalized technology that requires just a handful of specific examples in order to respond to new and emerging forms of malicious content, called Few-Shot Learner (FSL). Few-shot learning is a relatively recent development in AI, essentially teaching the system to make accurate predictions based on a limited number of training examples -- quite the opposite of conventional supervised learning methods. For example, if you wanted to train a standard SL model to recognize pictures of rabbits, you feed it a couple hundred thousands of rabbit pictures and then you can present it with two images and ask if they both show the same animal. Thing is, the model doesn't know if the two pictures are of rabbits because it doesn't actually know what a rabbit is.

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