4 ms·

the biggest problem is that ML/AI has no conception of time, context, continuity, or cause and effect.

by novia 6y ago

NateEag 6y ago
Which is why some of us avoid calling these systems "AI". Yes, the term has become accepted, but it's a horribly irresponsible label because it massively misleads laypeople about how these systems work and how trustworthy they are.
- gentleman11 6y ago
  What’s a better catch all term? Data modelling system? Prediction system?
  - Reelin 6y ago
    ... machine learning? Technically AI is correct, since AI doesn't necessarily refer to human level AGI. Unfortunately it's highly misleading because that distinction seems to be lost on the average person. Machine learning doesn't seem to carry the same science fiction implications for whatever reason.
- joveian 6y ago
  I wouldn't say "has become" since the term was already in widspread use in games by the early 90s at least if not earlier. I would guess many people are first exposed to the term in games and I would be surprised if the expectation you describe is actually widespread at all. Games or not, it seems like the first exposure anyone has to anything called AI would cure them of any such misconceptions.
  - NateEag 6y ago
    I meant it had become widespread in describing ML / DL systems, specifically.
Reelin 6y ago
> ML/AI has no conception of Time: False. Time can be included as an input. Previous system state can also be communicated forward in various ways (ex the LSTM architecture). Conceptual context: I'll agree, since that seems like it would require general abstract reasoning ability (ie strong AI). Similarly, robust treatment in the general case of physical continuity and cause and effect both seem like special cases of conceptual context to me. That being said, bear in mind that we already have examples of not so robust treatment for special cases of physical continuity as well as cause and effect. (Example: https://news.developer.nvidia.com/transforming-standard-video-into-slow-motion-with-ai/ https://news.developer.nvidia.com/transforming-standard-vide...) Who knows how close (or far) we might be from a general solution?
- novia 6y ago
  > Time: False. Time can be included as an input. I mean that they don't understand time as something that's unidirectional. (To the best of my knowledge! Would be happy to be proven wrong.)
  - Reelin 6y ago
    Well I guess without abstract reasoning ability (and thus no conceptual context in the human sense) "understanding" in the way the word is typically used is presumably impossible. But I don't see any reason you couldn't train a model to incorporate the underlying assumption that the flow of time is unidirectional. I expect you would just need inputs (ie training data) reflecting that fact coupled with an appropriate loss function. (Aren't networks that predict future physical states, such as motion, more or less doing this?)
ad404b8a372f2b9 6y ago
Maybe not the model they used but AI in general does. A simple model like P(ball is in position y at time t1 | ball is in position x at time t0) accounts for all of that.

the biggest problem is that ML/AI has no conception of time, context, continuity, or cause and effect.

by novia 6y ago

the biggest problem is that ML/AI has no conception of time, context, continuity, or cause and effect.

Which is why some of us avoid calling these systems "AI". Yes, the term has become accepted, but it's a horribly irresponsible label because it massively misleads laypeople about how these systems work and how trustworthy they are.

gentleman11 6y ago
What’s a better catch all term? Data modelling system? Prediction system?
- Reelin 6y ago
  ... machine learning? Technically AI is correct, since AI doesn't necessarily refer to human level AGI. Unfortunately it's highly misleading because that distinction seems to be lost on the average person. Machine learning doesn't seem to carry the same science fiction implications for whatever reason.
joveian 6y ago
I wouldn't say "has become" since the term was already in widspread use in games by the early 90s at least if not earlier. I would guess many people are first exposed to the term in games and I would be surprised if the expectation you describe is actually widespread at all. Games or not, it seems like the first exposure anyone has to anything called AI would cure them of any such misconceptions.
- NateEag 6y ago
  I meant it had become widespread in describing ML / DL systems, specifically.

Reelin 6y ago

> ML/AI has no conception of Time: False. Time can be included as an input. Previous system state can also be communicated forward in various ways (ex the LSTM architecture). Conceptual context: I'll agree, since that seems like it would require general abstract reasoning ability (ie strong AI). Similarly, robust treatment in the general case of physical continuity and cause and effect both seem like special cases of conceptual context to me. That being said, bear in mind that we already have examples of not so robust treatment for special cases of physical continuity as well as cause and effect. (Example: https://news.developer.nvidia.com/transforming-standard-video-into-slow-motion-with-ai/ https://news.developer.nvidia.com/transforming-standard-vide...) Who knows how close (or far) we might be from a general solution?

novia 6y ago
> Time: False. Time can be included as an input. I mean that they don't understand time as something that's unidirectional. (To the best of my knowledge! Would be happy to be proven wrong.)
- Reelin 6y ago
  Well I guess without abstract reasoning ability (and thus no conceptual context in the human sense) "understanding" in the way the word is typically used is presumably impossible. But I don't see any reason you couldn't train a model to incorporate the underlying assumption that the flow of time is unidirectional. I expect you would just need inputs (ie training data) reflecting that fact coupled with an appropriate loss function. (Aren't networks that predict future physical states, such as motion, more or less doing this?)

ad404b8a372f2b9 6y ago

Maybe not the model they used but AI in general does. A simple model like P(ball is in position y at time t1 | ball is in position x at time t0) accounts for all of that.