"Thought Vectors" May Help Software 'Understand'
Originally shared by Gideon Rosenblatt
"Thought Vectors" May Help Software 'Understand'
The underlying idea is that by ascribing every word a set of numbers (or vector), a computer can be trained to understand the actual meaning of these words.
...when I ask Google the question, “Who was the first president of the United States?”, it spits back a short bit of text containing the correct answer. Doesn’t it understand what I am saying? The answer is no. The current state of the art has taught computers to understand human language much the way a trained dog understands it when squatting down in response to the command “sit.” The dog doesn’t understand the actual meaning of the words, and has only been conditioned to give a response to a certain stimulus. If you were to ask the dog, “sit is to chair as blank is to bed,” it would have no idea what you’re getting at.
Thought vectors provide a means to change that: actually teaching the computer to understand language much the way we do. The difference between thought vectors and the previous methods used in AI is in some ways merely one of degree. While a dog maps the word sit to a single behavior, using thought vectors, that word could be mapped to thousands of sentences containing “sit” in them. The result would be the computer arriving at a meaning for the word more closely resembling our own.
http://www.extremetech.com/extreme/206521-thought-vectors-could-revolutionize-artificial-intelligence
"Thought Vectors" May Help Software 'Understand'
The underlying idea is that by ascribing every word a set of numbers (or vector), a computer can be trained to understand the actual meaning of these words.
...when I ask Google the question, “Who was the first president of the United States?”, it spits back a short bit of text containing the correct answer. Doesn’t it understand what I am saying? The answer is no. The current state of the art has taught computers to understand human language much the way a trained dog understands it when squatting down in response to the command “sit.” The dog doesn’t understand the actual meaning of the words, and has only been conditioned to give a response to a certain stimulus. If you were to ask the dog, “sit is to chair as blank is to bed,” it would have no idea what you’re getting at.
Thought vectors provide a means to change that: actually teaching the computer to understand language much the way we do. The difference between thought vectors and the previous methods used in AI is in some ways merely one of degree. While a dog maps the word sit to a single behavior, using thought vectors, that word could be mapped to thousands of sentences containing “sit” in them. The result would be the computer arriving at a meaning for the word more closely resembling our own.
http://www.extremetech.com/extreme/206521-thought-vectors-could-revolutionize-artificial-intelligence
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