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Can a Robot Learn a Language the Way a Child Does? (zdnet.com)
MIT researchers have devised a way to train semantic parsers by mimicking the way a child learns language. "The system observes captioned videos and associates the words with recorded actions and objects," ZDNet reports, citing the paper presented this week. "It could make it easier to train parsers, and it could potentially improve human interactions with robots." From the report: To train their parser, the researchers combined a semantic parser with a computer vision component trained in object, human and activity recognition in video. Next, they compiled a dataset of about 400 videos depicting people carrying out actions such as picking up an object or walking toward an object. Participants on the crowdsourcing platform Mechanical Turk to wrote 1,200 captions for those videos, 840 of which were set aside for training and tuning. The rest were used for testing. By associating the words with the actions and objects in a video, the parser learns how sentences are structured. With that training, it can accurately predict the meaning of a sentence without a video.
Chomsky famously called it a "black box inside their heads."
Noam Chomsky was being a bit modest. He did more than anyone to figure out what is going on inside that black box, and what innate language learning ability children are born with, which is far more than the "tabla rasa" theory pushed by behaviorists. Chomsky learned that all human languages, even those invented by isolated groups of children, have nouns, verbs, adjectives, and adverbs. All of them have words for discussing hypotheticals, and situations separated in both time and place from the here-and-now.
Chomsky wasn't right about everything. He believed that all human languages were based on recursive grammars, and for decades this was thought to be correct. But recently it was reported that the Pirahã language does not allow recursive phrases.