AI is mostly about curve fitting (2018)

Post by dr_dshiv » Sat, Nov 23, 2019, 6:02 PM UTC

wayoutthere wrote: post #27

dr_dshiv wrote: post #15

So, one view of AI is that is is machine learning. Another view is that it consists of automated process. So, if expert systems count as AI, or if production rule systems count as AI, then pretty much any handcrafted if-then statement counts as AI. And, so too might any automated process.. This is has the advantage of recognizing how human intelligence can be automated and aggregated into system processes. And, the d…

I too hold the view that artificial intelligence is not reliant on computers. Sufficiently complex business logic is impossible for a single person -- even the CEO -- to have an end-to-end view (much less have a significant influence on). The emergent behavior of large companies fits a definition of an AI today; human "decision makers" are increasingly just reviewing and approving the output of algorithms. I think th…

Post by _0ffh » Sat, Nov 23, 2019, 6:05 PM UTC

manifestsilence wrote: post #87

Veedrac wrote: post #65

The article is less awful than the title. In short, the thesis is that ML seems only able to learn associations, rather than stronger, causal models. It should be fairly obvious that ‘curve fitting’ is a misleading category—these models are clearly learning highly meaningful latent spaces that no prior approaches ever did. But I would agree that the actual high-level ability to make causal inferences seems to be lack…

This is interesting, as this appears to be similar to the difference between Daniel Kahneman's System1 and System2 modes of human thought. ML is perhaps beginning to approach our subconscious, associative intelligence. This shows up in its excellence for things like image processing, which we do instantly and automatically. Perhaps growing the tech equivalent of a prefrontal cortex is what's going to be hard, or requ…

Post by dghughes » Sat, Nov 23, 2019, 6:12 PM UTC

projektfu wrote: post #91

erikpukinskis wrote: post #68

Earlier quoted context omitted.

What’s wrong with anthropomorphizing? I’ve noticed at least as many people under-anthropomorphize as over. People who seem obsessed with human exceptionalism and are personally offended at the idea that plants and animals (and computers!) might have subjective experiences like our own. But to me it seems obvious we are far more alike “lower” species than we are unlike them. I would say the cases of human exceptionali…

Anthropomorphizing is dangerous because it leads to metaphor that can both ascribe too much to the subject and create blind spots in the minds of researchers. Saying, for example, "Dogs want love," is fine for the owner but problematic for a researcher because love, as we understand it, is a human state. We'll never really understand what it means for a dog to feel loved. To the ethologist that is not to say that the…

Post by darksaints » Sat, Nov 23, 2019, 6:20 PM UTC

Veedrac wrote: post #65

The article is less awful than the title. In short, the thesis is that ML seems only able to learn associations, rather than stronger, causal models. It should be fairly obvious that ‘curve fitting’ is a misleading category—these models are clearly learning highly meaningful latent spaces that no prior approaches ever did. But I would agree that the actual high-level ability to make causal inferences seems to be lack…

Post by The_rationalist » Sat, Nov 23, 2019, 6:21 PM UTC