6 ms·

This isn't learning by any means - this is just similarity. Machines don't understand what the words mean, nor the nuance of how to use them via this method.

ExpiredLink 11y ago
You are going to be downvoted into oblivion by the AI zealots here at HN. But, of course, you are right!
tensor 11y ago
You say this as though you have a definition of what it means for something to "understand" words. It's easy to make a claim, why don't you try to back it up?
- sqrt17 11y ago
  usually, we assume that a meat grinder does not "understand" meat, that a rice cooker does not "understand" food, and that a telephone or a CD player does not "understand" human language. Wiktionary terms "understand" as (i) be aware of the meaning of something (so we've just reduced it to the meaning of awareness and meaning), or (ii) to impute meaning that is not explicitly stated. To put it in more colorful terms, a blind person can understand the difference between green, yellow and brown bananas, but they usually cannot understand the visual aspect of color. By the same rationale, a vector does not understand the word it describes any more than the telephone book understands the people listed in it or the city that they live in.
  - deleted 11y ago
    [deleted]
  - tensor 11y ago
    We are not talking about just a vector, but rather vectors trained in a specific way along with cosine similarity. You could easily argue that this system can tell you that one words has a different meaning from another word, or that two words have similar meaning. Further, it learned this without these relationships being explicitly stated. Once you start to try to define aspects of cognition explicitly, things very quickly get ambiguous. Also, these conversations usually go along the lines of: 1. State a definition. 2. See that computer matches. 3. Decide it's wrong after all and try to change it so that computer can't match it. 4. Repeat until we find a definition that excludes computer. I think it's a fascinating topic, but the above pattern is fairly disappointing.
GolDDranks 11y ago
Well, if you use the term "machine learning", you've got to accept that the "learning" there doesn't have all the same connotations than the word "learning" has in general. But if we just define learning to be getting better at something "autonomously", machine learning has that property. The programmer codes the learning algorithm, but the program gets better at the task itself by example.
sgk284 11y ago
> this is just similarity That may be all that "learning" and "understanding" are.

ExpiredLink 11y ago

You are going to be downvoted into oblivion by the AI zealots here at HN. But, of course, you are right!

tensor 11y ago

You say this as though you have a definition of what it means for something to "understand" words. It's easy to make a claim, why don't you try to back it up?

sqrt17 11y ago
usually, we assume that a meat grinder does not "understand" meat, that a rice cooker does not "understand" food, and that a telephone or a CD player does not "understand" human language. Wiktionary terms "understand" as (i) be aware of the meaning of something (so we've just reduced it to the meaning of awareness and meaning), or (ii) to impute meaning that is not explicitly stated. To put it in more colorful terms, a blind person can understand the difference between green, yellow and brown bananas, but they usually cannot understand the visual aspect of color. By the same rationale, a vector does not understand the word it describes any more than the telephone book understands the people listed in it or the city that they live in.
- deleted 11y ago
  [deleted]
- tensor 11y ago
  We are not talking about just a vector, but rather vectors trained in a specific way along with cosine similarity. You could easily argue that this system can tell you that one words has a different meaning from another word, or that two words have similar meaning. Further, it learned this without these relationships being explicitly stated. Once you start to try to define aspects of cognition explicitly, things very quickly get ambiguous. Also, these conversations usually go along the lines of: 1. State a definition. 2. See that computer matches. 3. Decide it's wrong after all and try to change it so that computer can't match it. 4. Repeat until we find a definition that excludes computer. I think it's a fascinating topic, but the above pattern is fairly disappointing.

GolDDranks 11y ago

Well, if you use the term "machine learning", you've got to accept that the "learning" there doesn't have all the same connotations than the word "learning" has in general. But if we just define learning to be getting better at something "autonomously", machine learning has that property. The programmer codes the learning algorithm, but the program gets better at the task itself by example.

sgk284 11y ago

> this is just similarity That may be all that "learning" and "understanding" are.