AI News, Chris Manning: How computers are learning to understand language​

Chris Manning: How computers are learning to understand language​

Manning specializes in natural language processing – designing computer algorithms that can understand meaning and sentiment in written and spoken language and respond intelligently.

His work is closely tied to the sort of voice-activated systems found in smartphones and in online applications that translate text between human languages.

He relies on an offshoot of artificial intelligence known as deep learning to design algorithms that can teach themselves to understand meaning and adapt to new or evolving uses of language.

Siebel, a pioneer in numerous areas of information technology and known for his ability to see and understand emerging trends in computer science and beyond, has long held an interest in precisely this kind of work.

Advances in natural language processing change everything about human-computer interaction, and Professor Manning’s innovative natural language processing research is making some of those advancements possible,” said Siebel, CEO of C3 IoT.

If you say something to Google or Siri or Alexa, the speech recognition is incredibly good, but half the time it doesn’t understand what you mean and asks, “Would you like me to do a web search for that?” What got you interested in language?

In the early days, the field involved writing out symbolic rules of grammar – subject, noun, clauses, predicates with a verb, perhaps followed by a noun phrase, perhaps followed by a prepositional phrase and so forth.

It was then that people started to explore statistical methods of analyzing all that data and building probabilistic models of which words are likely to appear together to create meaning and sentiment.

What you learn is that there is a large amount of already translated text lying around that provides context from which we can build probabilistic models to translate new text.

It’s easy to create a chatbot that just repeats random stuff that’s half-connected to what you asked about, but how do you build dialogue agents that can understand and do tasks that people are asking for?

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