The conversations are rather enlightening. >What is the color of water? Water. I'm quite puzzled how it knows "two plus two" is four but "ten minus two" is "seventy-two". I wonder how it parsed one correctly but failed drastically to parse the other.
It did not see enough examples to learn a general addition algorithm. "Two plus two equals four" is a common sentence, "Ten minus two equals eight" isn't. In fact, it's likely that no one learns addition by listening to examples, you're taught to execute a specific algorithm and it takes many people years to master it. In fact, ask a toddler what "ten minus two" is and you might get a similar nonsensical answer.
A Neural Conversational Model [pdf]
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Re: A Neural Conversational Model [pdf]
#22This scares me because all my interactions with google support have been so incredibly bad, it makes me nervous to train a computer on that corpus of bad data. Taking bad support and turning it into inaccurate bad support is a fantastically bad idea.
Re: A Neural Conversational Model [pdf]
#23> Human: what is the purpose of life ? > Machine: to serve the greater good . > Human: what is the purpose of living ? > Machine: to live forever . > Human: what is the purpose of existence ? > Machine: to find out what happens when we get to the planet earth . > Human: where are you now ? > Machine: i ’m in the middle of nowhere . > Human: what is the purpose of dying ? > Machine: to have a life . > Human: what is t…
Something tells me the entirety of BSG was included in the training set.
Re: A Neural Conversational Model [pdf]
#24You can play with it here: http://rare-technologies.com/word2vec-tutorial/ (a bit further down)
Google seems rather heavily invested in NLP, can't blame them as it's a very exciting domain to explore.
Re: A Neural Conversational Model [pdf]
#25Quoc V. Le also helped develop Word2Vec, released as opensource couple of years ago: https://code.google.com/p/word2vec/ You can play with it here: http://rare-technologies.com/word2vec-tutorial/ (a bit further down) Google seems rather heavily invested in NLP, can't blame them as it's a very exciting domain to explore.
Re: A Neural Conversational Model [pdf]
#26Quoc V. Le also helped develop Word2Vec, released as opensource couple of years ago: https://code.google.com/p/word2vec/ You can play with it here: http://rare-technologies.com/word2vec-tutorial/ (a bit further down) Google seems rather heavily invested in NLP, can't blame them as it's a very exciting domain to explore.
Quoc Le is neither an author of the word2vec paper or in the committers list of word2vec software (there's only two committers, mikolov and sutskever). Not sure where you got that inference from.
At Google, he helped develop a system that essentially maps words into vectors. And according to Google, this work would later feed into a system developed largely by a researcher named Tomas Mikolov. Called Word2Vec, the system determines how different words on the web are related, and Google is now using this as a means of strengthening its “knowledge graph”
Re: A Neural Conversational Model [pdf]
#27http://papers.nips.cc/paper/5346-sequence-to-sequence-learni...
Re: A Neural Conversational Model [pdf]
#28Earlier quoted context omitted.
> What is the color of water? Water. Literally true in Japanese. The color of water is 水色 (mizu-iro: lit. "water-color.") The analogy then proceeds in the other direction: light blue things are considered "water-colored", rather than water being "light" "blue".
Technically it is 水の色 (water's color). Often times particles like 「は・が・の」 are dropped when the context allows for such, making 水の色 turn into 水色. Same thing happens with 葉の色 becoming 葉色 (leaf green). The の is implied. In English one might say that something is "sky blue" instead of saying "the color of the sky". So it's not uniquely Japanese in that sense.
Re: A Neural Conversational Model [pdf]
#29Re: A Neural Conversational Model [pdf]
#30> Human: what is the purpose of life ? > Machine: to serve the greater good . > Human: what is the purpose of living ? > Machine: to live forever . > Human: what is the purpose of existence ? > Machine: to find out what happens when we get to the planet earth . > Human: where are you now ? > Machine: i ’m in the middle of nowhere . > Human: what is the purpose of dying ? > Machine: to have a life . > Human: what is t…
> Machine: to find out what happens when we get to the planet earth . Something tells me the entirety of BSG was included in the training set.
> Machine: Kobol .