3 ms·

Eh? What does conceptually mean?

red75prime 8y ago
I don't know what "conceptually" means on such a level that I can program it. But it probably has something to do with a network of differing representations we have for a hotdog. A low-level visual cortex representation (probably not too much different from artificial NNs). Representation as parts arranged in particular spacial order. A word. Related representations, like a process of making a hotdog. And so on.
k_sh 8y ago
The AI sees data/markers/patterns that look like something it's seen before, as opposed to actually comprehending that it sees a tube of meat that people call a hot dog. The best metaphor I can think of is the cognitive difference between navigating a transit station that has signs in your native language, and one that you spent a couple of hours learning on Duolingo - with the latter, you aren't really understanding anything, just associating a:b::x:y.
- ben_w 8y ago
  Transit is probably not extreme enough because a:b::x:y is fine. Figures of speech are probably a better example (at least if translated literally, Duolingo teaches the equivalent phrases and is therefore easy to forget it doesn’t teach the meaning). “Der Tropfen, der das Fass zum Überlaufen brachte”. What is the origin story that makes the English equivalent about camels, anyway?
- shawn 8y ago
  This might be another formulation of the "Chinese Room" argument: https://en.wikipedia.org/wiki/Chinese_room https://en.wikipedia.org/wiki/Chinese_room If every action is the same -- that is, if you produce some actions which would have been produced if you "conceptualized" it rather than merely "memorized" it -- isn't that identical? The only thing we can do in life is make decisions. Regardless of how they're derived, if those decisions are identical to yours, isn't that entity "you" in some sense?
  - schiffern 8y ago
    >if those decisions are identical to yours, isn't that entity "you" in some sense? If by "decisions" you mean every single nerve impulse in response to every possible set of stimuli, then that's pretty exacting. Every wobble while standing, every mouth movement answering any possible question, etc. Also, how do you determine if the responses are "identical?" It's not like we can rewind reality and play it back, substituting yourself for an AI. And due to quantum nondeterminism, even if you played it back with no substitution your actions will diverge over time! If you're not considered identical to yourself, how is that a useful definition/test of "identicality"? At the required fidelity, this thought-experiment is problematic both in theory and in practice. It obscures more than it illuminates imo.
- red75prime 8y ago
  Anyone can try to step into neural network shoes on https://rach0012.github.io/humanRL_website/ https://rach0012.github.io/humanRL_website/ It is still much easier for us, because we just need to connect new data to existing concepts.
notahacker 8y ago
A human sees a 3D scene with objects, creatures, texture and lighting (and it evaluates the scene based on these concepts and how they related to each other even if it's never seen green fields, sheep, dry stone walls or fog before). The computer generally sees a set of pixel values, and takes a plenty of training to distinguish between "sheep" and "field the same shade of green as usually found in images containing sheep" because it doesn't have an innate concept of animate objects and habitats, how they relate to each other and which is more important. Whilst the computer's busy seeing a white pile of stones as a false positive for the presence of sheep, the human's looking at the way the stones are piled as possible evidence of human activity and noting the presence of droppings in the foreground might mean sheep were here recently. (of course, it's not entirely impossible for computer vision systems to deal with higher levels of abstraction: autonomous vehicles model the world in 3D and classify objects as vehicles in order to predict their near future behaviour and signals in order to regulate their own behaviour, but that goes well beyond mere learning processes. And of course a pixel-by-pixel understanding of the world has its use cases in spotting changes in colour and texture which are so subtle humans abstract away from, like crop discolouration on satellite images or cracks in rough surfaces) We're much better at abstraction than other animals too: show us a 20,000 year old cave painting and we'll easily grasp that it was produced by humans and the lines represent shapes of animals broadly similar to today's livestock. Same goes for 2000 year old marble bas reliefs. You might well be able to train an algorithm to recognise "paintings of animals" and "carvings of animals", but you'll struggle with a training set consisting purely of photographs of real world livestock
- zby 8y ago
  "A human sees a 3D scene objects, creatures, texture and lighting (and it evaluates the scene based on these concepts and how they related to each other even if it's never seen green fields, sheep, dry stone walls or fog before)." Our eyes are no that different from cameras - they also have a set of pixels that can get some values, they are not that regular and maybe the values are not that discrete but it is not that retina sees objects or textures - there are just some neural layers that do the pixels->objects computation.
  - 8y ago

I don't know what "conceptually" means on such a level that I can program it. But it probably has something to do with a network of differing representations we have for a hotdog. A low-level visual cortex representation (probably not too much different from artificial NNs). Representation as parts arranged in particular spacial order. A word. Related representations, like a process of making a hotdog. And so on.

k_sh 8y ago

The AI sees data/markers/patterns that look like something it's seen before, as opposed to actually comprehending that it sees a tube of meat that people call a hot dog. The best metaphor I can think of is the cognitive difference between navigating a transit station that has signs in your native language, and one that you spent a couple of hours learning on Duolingo - with the latter, you aren't really understanding anything, just associating a:b::x:y.

ben_w 8y ago
Transit is probably not extreme enough because a:b::x:y is fine. Figures of speech are probably a better example (at least if translated literally, Duolingo teaches the equivalent phrases and is therefore easy to forget it doesn’t teach the meaning). “Der Tropfen, der das Fass zum Überlaufen brachte”. What is the origin story that makes the English equivalent about camels, anyway?
shawn 8y ago
This might be another formulation of the "Chinese Room" argument: https://en.wikipedia.org/wiki/Chinese_room https://en.wikipedia.org/wiki/Chinese_room If every action is the same -- that is, if you produce some actions which would have been produced if you "conceptualized" it rather than merely "memorized" it -- isn't that identical? The only thing we can do in life is make decisions. Regardless of how they're derived, if those decisions are identical to yours, isn't that entity "you" in some sense?
- schiffern 8y ago
  >if those decisions are identical to yours, isn't that entity "you" in some sense? If by "decisions" you mean every single nerve impulse in response to every possible set of stimuli, then that's pretty exacting. Every wobble while standing, every mouth movement answering any possible question, etc. Also, how do you determine if the responses are "identical?" It's not like we can rewind reality and play it back, substituting yourself for an AI. And due to quantum nondeterminism, even if you played it back with no substitution your actions will diverge over time! If you're not considered identical to yourself, how is that a useful definition/test of "identicality"? At the required fidelity, this thought-experiment is problematic both in theory and in practice. It obscures more than it illuminates imo.
red75prime 8y ago
Anyone can try to step into neural network shoes on https://rach0012.github.io/humanRL_website/ https://rach0012.github.io/humanRL_website/ It is still much easier for us, because we just need to connect new data to existing concepts.

notahacker 8y ago

A human sees a 3D scene with objects, creatures, texture and lighting (and it evaluates the scene based on these concepts and how they related to each other even if it's never seen green fields, sheep, dry stone walls or fog before). The computer generally sees a set of pixel values, and takes a plenty of training to distinguish between "sheep" and "field the same shade of green as usually found in images containing sheep" because it doesn't have an innate concept of animate objects and habitats, how they relate to each other and which is more important. Whilst the computer's busy seeing a white pile of stones as a false positive for the presence of sheep, the human's looking at the way the stones are piled as possible evidence of human activity and noting the presence of droppings in the foreground might mean sheep were here recently. (of course, it's not entirely impossible for computer vision systems to deal with higher levels of abstraction: autonomous vehicles model the world in 3D and classify objects as vehicles in order to predict their near future behaviour and signals in order to regulate their own behaviour, but that goes well beyond mere learning processes. And of course a pixel-by-pixel understanding of the world has its use cases in spotting changes in colour and texture which are so subtle humans abstract away from, like crop discolouration on satellite images or cracks in rough surfaces) We're much better at abstraction than other animals too: show us a 20,000 year old cave painting and we'll easily grasp that it was produced by humans and the lines represent shapes of animals broadly similar to today's livestock. Same goes for 2000 year old marble bas reliefs. You might well be able to train an algorithm to recognise "paintings of animals" and "carvings of animals", but you'll struggle with a training set consisting purely of photographs of real world livestock

zby 8y ago
"A human sees a 3D scene objects, creatures, texture and lighting (and it evaluates the scene based on these concepts and how they related to each other even if it's never seen green fields, sheep, dry stone walls or fog before)." Our eyes are no that different from cameras - they also have a set of pixels that can get some values, they are not that regular and maybe the values are not that discrete but it is not that retina sees objects or textures - there are just some neural layers that do the pixels->objects computation.
- 8y ago