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A Neural Conversational Model [pdf]

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Re: A Neural Conversational Model [pdf]

#21
post #5
post #2

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.

Just working through maths problems with my 6 year old son on a daily basis now, and it's oh-so-hard not to face palm in front of him. So many things that seems incredibly obvious are hard learned lessons. One of the fascinating things of helping him practice is that it's helping me recognise the sheer number of simple little shortcuts I apply to even something as basic as addition (and incredibly hard to hold back and give him the chance to practice the basics before I show him the "cheats").

Re: A Neural Conversational Model [pdf]

#22
post #17

This 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.

Some might suggest that Google support has been run by AI's for years already, given just how awful some of the responses are...

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…

> 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.

Re: A Neural Conversational Model [pdf]

#24
Quoc 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]

#25
post #24

Quoc 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.

Re: A Neural Conversational Model [pdf]

#26
post #25
post #24

Quoc 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.

http://www.wired.com/2014/12/googlers-quest-teach-machines-u...

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]

#28
post #13
post #11

Earlier 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.

Technically, 水色 appears in both Japanese and Japanese-English dictionaries, so I'd say it's a word by any reasonable definition. (Disclaimer: not a native speaker.)

Re: A Neural Conversational Model [pdf]

#30
post #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…

> 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.

> Human: where are you now ?

> Machine: Kobol .

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