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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 "s

by Nadya 11y ago

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.

biot 11y ago
It's not parsing the phrase into the tokens "10 - 2" and evaluating that. Rather, a corpus of data was used to train the neural net and, for whatever reason, the answer "seventy-two" was the highest probability output. In all likelihood, it doesn't even register that this is a math question.
murbard2 11y ago
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.
- Nadya 11y ago
  I hadn't thought of the "common sentence" approach. Woops. I was thinking semi-intelligent parsing and it recognized "two" and "plus" and created a formula 2+2 then solved it with 4 ("four"). So I had assumed it would have done the same with "ten", "minus", and "two" to create 10-2, solve it, and respond with 8 ("eight"). If you use Google Search and search for "ten minus two" it's intelligent enough to parse the search and give you a calculator with 10-2=8 already inputted. I had assumed a similar parsing approach may have been used for their conversation bot in regards to general mathematical knowledge. But your explanation makes a ton of sense. :)
- vidarh 11y ago
  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").
- reddytowns 11y ago
  I don' think it could ever learn a general addition algorithm, because the algorithm they are using doesn't allow for a multi-step process. For example, to add 28 + 37, a simple approach would be: 1. first, look at the one's column and recall that 8 + 7 is 15. 2. Then take the value from the tens column of the answer and add it to the tens column of the arguments. But given the command : add 28 + 37, it can only execute a single step. It has no way of guessing at and then storing the values for the intermediate command, (take the 1 from the 15 and add it to the 2 from the 28 and the 3 from the 37), to be processed in the next round. Because so it can't ever generalize addition.
  - Smerity 11y ago
    Whilst you are correct that the number of allowed computation steps is limited, LSTMs have still been used with success for such tasks. LSTMs have been used to perform addition with 99% accuracy^ on two 9-digit numbers[1]. The paper even shows that simple Python programs can be evaluated with some degree of accuracy. Remember that even if the number of computation steps is limited, there can be multiple layers ([1] uses 2) and each neuron unit can perform a computation ([1] uses 400 cells per layer). It only needs to learn how to be an ALU. The work is in fact done by one of the people who established the sequence to sequence framework, Sutskever, and is referenced in the Neural Conversational Model paper. ^ They use "teacher forcing" for evaluation which inflates the accuracy to some degree, but it's still quite impressive. [1]: http://arxiv.org/pdf/1410.4615v3.pdf http://arxiv.org/pdf/1410.4615v3.pdf
    - murbard2 11y ago
      Now for the kicker: recurrent network can implement arbitrary algorithms, including a type of GOFAI. It's quite possible that such an algorithm isn't learnable without supervision; that it was discovered by sheer luck at the dawn of humanity and has been passed down by language ever since.
derefr 11y ago
> 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".
- Nadya 11y ago
  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.
  - teraflop 11y ago
    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.)
    - Nadya 11y ago
      It's a word in its own right - but the etymology of it is still important and I feel it was slightly misrepresented. For example, the greeting many people know 「こんにちは」 (konnichiwa) is a word in its own right nowadays, but is really just the beginning of an older greeting: 今日は御機嫌いかがですか
  - paulsutter 11y ago
    Dropping の can create a different meaning. For example, 日本料理 (Japanese cuisine) and 日本の料理　(cooking in Japan, which could well be used to refer to the quality of the italian or chinese food here). The former is truly a single word and the latter two words separated by a particle.

by Nadya 11y ago

biot 11y ago

It's not parsing the phrase into the tokens "10 - 2" and evaluating that. Rather, a corpus of data was used to train the neural net and, for whatever reason, the answer "seventy-two" was the highest probability output. In all likelihood, it doesn't even register that this is a math question.

murbard2 11y ago

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.

Nadya 11y ago
I hadn't thought of the "common sentence" approach. Woops. I was thinking semi-intelligent parsing and it recognized "two" and "plus" and created a formula 2+2 then solved it with 4 ("four"). So I had assumed it would have done the same with "ten", "minus", and "two" to create 10-2, solve it, and respond with 8 ("eight"). If you use Google Search and search for "ten minus two" it's intelligent enough to parse the search and give you a calculator with 10-2=8 already inputted. I had assumed a similar parsing approach may have been used for their conversation bot in regards to general mathematical knowledge. But your explanation makes a ton of sense. :)
vidarh 11y ago
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").
reddytowns 11y ago
I don' think it could ever learn a general addition algorithm, because the algorithm they are using doesn't allow for a multi-step process. For example, to add 28 + 37, a simple approach would be: 1. first, look at the one's column and recall that 8 + 7 is 15. 2. Then take the value from the tens column of the answer and add it to the tens column of the arguments. But given the command : add 28 + 37, it can only execute a single step. It has no way of guessing at and then storing the values for the intermediate command, (take the 1 from the 15 and add it to the 2 from the 28 and the 3 from the 37), to be processed in the next round. Because so it can't ever generalize addition.
- Smerity 11y ago
  Whilst you are correct that the number of allowed computation steps is limited, LSTMs have still been used with success for such tasks. LSTMs have been used to perform addition with 99% accuracy^ on two 9-digit numbers[1]. The paper even shows that simple Python programs can be evaluated with some degree of accuracy. Remember that even if the number of computation steps is limited, there can be multiple layers ([1] uses 2) and each neuron unit can perform a computation ([1] uses 400 cells per layer). It only needs to learn how to be an ALU. The work is in fact done by one of the people who established the sequence to sequence framework, Sutskever, and is referenced in the Neural Conversational Model paper. ^ They use "teacher forcing" for evaluation which inflates the accuracy to some degree, but it's still quite impressive. [1]: http://arxiv.org/pdf/1410.4615v3.pdf http://arxiv.org/pdf/1410.4615v3.pdf
  - murbard2 11y ago
    Now for the kicker: recurrent network can implement arbitrary algorithms, including a type of GOFAI. It's quite possible that such an algorithm isn't learnable without supervision; that it was discovered by sheer luck at the dawn of humanity and has been passed down by language ever since.

derefr 11y ago

> 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".

Nadya 11y ago
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.
- teraflop 11y ago
  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.)
  - Nadya 11y ago
    It's a word in its own right - but the etymology of it is still important and I feel it was slightly misrepresented. For example, the greeting many people know 「こんにちは」 (konnichiwa) is a word in its own right nowadays, but is really just the beginning of an older greeting: 今日は御機嫌いかがですか
- paulsutter 11y ago
  Dropping の can create a different meaning. For example, 日本料理 (Japanese cuisine) and 日本の料理　(cooking in Japan, which could well be used to refer to the quality of the italian or chinese food here). The former is truly a single word and the latter two words separated by a particle.