The network, through iterated adjustment of the elements of the vector based on errors detected on comparison with the text corpora, produces the values in continuous space that best reflect the contextual data given. Most dictionaries will offer a direct or indirect connection through "king" to "ruler" or "sovereign" and "male" and through "queen" to "ruler" or "sovereign" and "female," as: These definitions2 show gender can be "factored out," and in common usage the gender aspect of sovereigns is notable. As we understand the high degree of contextual dependency of word meanings in a language, any representation of word meaning to a significant degree will reflect context, where context is its interassociation with other words. The word vectors produced by the method of training on a huge natural text dataset, in which words are given distributed vector representations refined through associations present in the input context, reflect the cross-referential semantic compositionality of a dictionary.
As I gaze into the palantir afforded me as a co-host of "The EdTech Situation Room" each week with Jason Neiffer (@techsavvyteach), this is part of the future I see for our students, our society, and ourselves in the coming decades. This 98 second video, which I titled "EdTech Situation Room Promo Trailer," is the result of my thinking about this question tonight. This question of what an emerging "artificial intelligence first" rather than "mobile first" worldview (which Google announced at Google IO 2017) should mean for schools is something I discussed on The EdTech Situation Room back on May 17, 2017, with Jason Neiffer (@techsavvyteach) and Ben Wilkoff (@bhwilkoff). Check out the "Narrated Slideshow – Screencast" and "Digital Storytelling" pages of ShowWithMedia.com for additional resources and examples related to these media project types.
Artificial intelligence and machine learning are ushering in the rise of smart machines that will be able to carry out many of the complex cognitive tasks that once seemed exclusive to middle class work in the knowledge economy. Already, doctors are using deeper learning to help diagnose illnesses, entry-level lawyers are finding themselves out-analyzed by machines that can harvest case history faster than any human, artificial intelligence is writing news stories and robots are staffing restaurants. This is just the beginning: one Oxford University study suggests that as many as 47% of current middle-class American jobs could get displaced or change significantly over the next two decades due to automation.