5 ms·

Boiling it all down, when prompted, these models just regurgitate a similar sentence to what is observed in the training data for loosely that same input, using

Boiling it all down, when prompted, these models just regurgitate a similar sentence to what is observed in the training data for loosely that same input, using some glorified curve fitting. This does not necessarily imply the model understands the meaning of what it is spitting out. So the uninitiated will be really impressed with this kind of toy. The researchers here appear to have placed particular emphasis on cleaning up what the model is spitting out, but I think it's lipstick on a pig. The area begging for more research is parsing out the meaning of anything but the most simple sentence.

runawaybottle 6y ago
Sounds like Chomsky’s main criticism: https://youtu.be/ndwIZPBs8Y4 https://youtu.be/ndwIZPBs8Y4
AndrewKemendo 6y ago
>Boiling it all down, when prompted, these models just regurgitate a similar sentence to what is observed in the training data for loosely that same input, using some glorified curve fitting This is not that much different than what you do. What criteria would you use to determine if something understands the meaning of a word/phrase/concept that isn't a string of definitions and metaphors? And at what level is sufficient? Attempting to prove that something "understands the meaning" is a fruitless task with no quantifiable criteria - much like proving something is "conscious."
- shermanmccoy 6y ago
  Representing meaning as a number, or more specifically a point or set in some vector space is fine. I'm not suggesting a more sophisticated concept of understanding than the status quo here, basically text in, a number/numbers out, which as I understand it is how most intent analysis works currently. So meaning in this sense is very much quantifiable, yet how far along are we in parsing out even the most basic meanings? Can we build something which discriminates between "I'm moving in to <address>" and "I'm moving in on <date>", using the latest and greatest word embeddings? Not without some extra layers of external rules imposed on top. So the model does not 'understand', even in this limited scope of understanding. Don't be fooled by the sentence recycling is all.
  - mycall 6y ago
    I wish someone fused Cyc with a modern chatbot.

runawaybottle 6y ago

Sounds like Chomsky’s main criticism: https://youtu.be/ndwIZPBs8Y4 https://youtu.be/ndwIZPBs8Y4

AndrewKemendo 6y ago

>Boiling it all down, when prompted, these models just regurgitate a similar sentence to what is observed in the training data for loosely that same input, using some glorified curve fitting This is not that much different than what you do. What criteria would you use to determine if something understands the meaning of a word/phrase/concept that isn't a string of definitions and metaphors? And at what level is sufficient? Attempting to prove that something "understands the meaning" is a fruitless task with no quantifiable criteria - much like proving something is "conscious."

shermanmccoy 6y ago
Representing meaning as a number, or more specifically a point or set in some vector space is fine. I'm not suggesting a more sophisticated concept of understanding than the status quo here, basically text in, a number/numbers out, which as I understand it is how most intent analysis works currently. So meaning in this sense is very much quantifiable, yet how far along are we in parsing out even the most basic meanings? Can we build something which discriminates between "I'm moving in to <address>" and "I'm moving in on <date>", using the latest and greatest word embeddings? Not without some extra layers of external rules imposed on top. So the model does not 'understand', even in this limited scope of understanding. Don't be fooled by the sentence recycling is all.
- mycall 6y ago
  I wish someone fused Cyc with a modern chatbot.