3 ms·

> But this is where the problem is in NLU: machines don’t know what we leave out, because they don’t know what we all know. What the machine doesn’t have that

> But this is where the problem is in NLU: machines don’t know what we leave out, because they don’t know what we all know. What the machine doesn’t have that we have is an understanding of culture and the feedback of emotions. What’s elusive about culture is that much of it is tacit. Consider all the things you don’t do in order to be a respected guest at a dinner party (a) for someone you don’t know well; vs (b) your best friend’s mother; vs (c) your new boss after the takeover. Most of the decisions you make are the result of tacit feedback (or lack thereof) that a machine can’t access. Consider these lines from an Eagles song: City girls just seem to find out early How to open doors with just a smile How’s a machine to learn that knowledge, when the substantive information never passes through a keyboard or a camera, and only exists in response to biological stimuli?

nonameiguess 5y ago
There's no reason in principle a machine can't learn this kind of thing (humans are still machines, after all), but I think this certainly points to why humans learn language so much faster than machine models. We can synthesize language understanding with a much richer, sensory-based model of the world and recognize that words correspond to clusters of sensations like specific sights, sounds, tastes, feelings, and smells. A logical next step in AI research is figuring out some kind of way to get these various systems that do one thing and one well to interoperate the way all the parts of a brain do, so Alpha Go, some object recognizer trained on Go boards, and a language model trained on sentences about the game of Go can recognize they're all modeling the same thing and augment each other with crosstalk and competitive hypothesizing in roughly the way animal reasoning seems to work.
- wombatmobile 5y ago
  > (humans are still machines, after all) Good point. > but I think this certainly points to why humans learn language so much faster than machine models. We can synthesize language understanding with a much richer, sensory-based model of the world and recognize that words correspond to clusters of sensations like specific sights, sounds, tastes, feelings, and smells. Yes, because unlike computers, we have bodies that are in the world. > A logical next step in AI research is figuring out some kind of way to get these various systems that do one thing and one well to interoperate the way all the parts of a brain do Do you think it’s fair to conclude that until computers have bodies that enable them to have the same situational presence and sensory awareness that humans have, computers will fall short of being able to parse natural language the way humans do? Are a (human like) body and senses sufficient or might more be required? I’m thinking of extinct animals - just because we can restore their bodies from their genomic sequence doesn’t mean we can ever restore their culture. Humans learn culture through a lifelong process that necessarily involves an early stage of childhood dependency, then growth, and maturation.
- silent_cal 5y ago
  > humans are still machines, after all Bold claim...

nonameiguess 5y ago

There's no reason in principle a machine can't learn this kind of thing (humans are still machines, after all), but I think this certainly points to why humans learn language so much faster than machine models. We can synthesize language understanding with a much richer, sensory-based model of the world and recognize that words correspond to clusters of sensations like specific sights, sounds, tastes, feelings, and smells. A logical next step in AI research is figuring out some kind of way to get these various systems that do one thing and one well to interoperate the way all the parts of a brain do, so Alpha Go, some object recognizer trained on Go boards, and a language model trained on sentences about the game of Go can recognize they're all modeling the same thing and augment each other with crosstalk and competitive hypothesizing in roughly the way animal reasoning seems to work.

wombatmobile 5y ago
> (humans are still machines, after all) Good point. > but I think this certainly points to why humans learn language so much faster than machine models. We can synthesize language understanding with a much richer, sensory-based model of the world and recognize that words correspond to clusters of sensations like specific sights, sounds, tastes, feelings, and smells. Yes, because unlike computers, we have bodies that are in the world. > A logical next step in AI research is figuring out some kind of way to get these various systems that do one thing and one well to interoperate the way all the parts of a brain do Do you think it’s fair to conclude that until computers have bodies that enable them to have the same situational presence and sensory awareness that humans have, computers will fall short of being able to parse natural language the way humans do? Are a (human like) body and senses sufficient or might more be required? I’m thinking of extinct animals - just because we can restore their bodies from their genomic sequence doesn’t mean we can ever restore their culture. Humans learn culture through a lifelong process that necessarily involves an early stage of childhood dependency, then growth, and maturation.
silent_cal 5y ago
> humans are still machines, after all Bold claim...