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Understanding Unstructured Data With Language Models
Much of our machine learning capabilities come from structured data, but the real payload lies in the messy, unstructured data underneath. If we want to gain practical insights, machines have to learn to parse things like social media posts filled with misspellings or sarcasm or handwritten doctor’s notes with illegible lettering.
So how do machines do this? Alex Peattie, the co-founder of PEG, has thoughts on where we’ve been with language models in the past and how they may help machines decipher these difficulties.
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Origins of Language Models
Roughly 80 years ago, Alan Turing and a group of brilliant minds gathered to create the newest iteration of the enigma machine, which helped win the war and began the journey to unlocking one of our most persistent problems, how to define a language with data.
