5 ms·

I'm not sure that's necessary. Early humans didn't know why a lot of things happened, such as why rubbing sticks makes fire; they just learned to use them from

• mindcrime 7y ago
  I suppose that's cause-and-effect in a loose sense, but one doesn't have to view everything as C&E to get similar results. It seems more powerful to think of it as relationships instead of just C&E because then you get a more general relationship processing engine out of it instead of a single-purpose thing. Make C&E a sub-set of relationship processing. If the rest doesn't work, then you still have a C&E engine from it by shutting off some features. I may be wrong (heck, I'm probably wrong), but I can't help but feel that you're abstracting things out too much. Yes, a "cause / effect relationship" IS-A "relationship", but sometimes the distinctions actually matter. I'd argue that a "cause/effect relationship" (and the associated reasoning) is markedly different in at least one important sense, and that is that it includes time in two senses: direction, and duration. There's a difference between knowing that Thing A and Thing B are "somehow" related, and knowing that "Doing Thing A causes Thing B to happen shortly afterwards" or whatever. To may way of thinking, this is something like what Pearl is talking about here: The key, he argues, is to replace reasoning by association with causal reasoning. Instead of the mere ability to correlate fever and malaria, machines need the capacity to reason that malaria causes fever. That said, I do like your idea of trying to build the processing engine in such a way that you can turn features on and off, because I don't necessarily hold that "cause/effect" is the only kind of reasoning we need.
  • tabtab 7y ago
    I'm not against tagging a vector between two or more nodes as "probably causal" (cause related) in some sense (or maybe a "weighted causal"). It just shouldn't be the ONLY tag. Re quote: "The key, he argues, is to replace reasoning by association with causal reasoning. Instead of the mere ability to correlate fever and malaria, machines need the capacity to reason that malaria causes fever." Back to my original point, humans did just fine at "intelligence" before science came along. That's Step 3, we need step 2 first. Find correlations, and if it seems to be that part-1 happens before part-2, then the bot can infer something equivalent to a causal relationship. That may be the time element you are talking about. Perhaps it's a matter of interpreting what "causal" means. I see it as "finding relationships we can take advantage of to obtain our goals". Whether there is physics or chemistry behind a relationship is an unnecessary distraction (without other advances). Re: That said, I do like your idea of trying to build the processing engine in such a way that you can turn features on and off, because I don't necessarily hold that "cause/effect" is the only kind of reasoning we need. So we kind of agree. If it turns out I'm wrong and that we can make most of the bot work right with just causal relationships, then factor out the general purpose "graph processor" for efficiency and make it causal-centric.
• mjfl 7y ago
  They understood cause and effect. They didn't know the causal chain in depth, but they did know that rubbing sticks together caused fire. They also knew that dumping water on the ground did not cause it to rain. Thus they could distinguish between correlation and causation.
  • aeternum 7y ago
    Maybe not on the ground, but dumping water on a cat for example was thought to cause it to rain: https://en.wikipedia.org/wiki/Rainmaking_(ritual)#Thailand https://en.wikipedia.org/wiki/Rainmaking_(ritual)#Thailand
    • tabtab 7y ago
      Stone age version of cat videos = real cats.
  • nradov 7y ago
    Did they really understand cause and effect? Primitive cultures frequently used religious ceremonies (cause) to effect changes in the natural world. It didn't actually work, but somehow they fooled themselves into believing that it did.
    • goatlover 7y ago
      According to Hume, causality is a habit of mind all humans have from experiencing many situations where A always follows B. And according to Kant, causality is one the categories the mind imposes on the world of sensory impression, like space and time, to make sense of the world. Early childhood studies should be able to answer the question as to whether humans are wired to expect causal relationships. My understanding is that all children do develop that expectation, as do animals.
      • cma 7y ago
        People getting umbrellas ready causes it to rain? We take into account models of minds of others etc. into a much more rich understanding of causality than A follows B in time.
• jankotek 7y ago
  I think you are underestimating early humans. Friction creates heat, fire is heat...
  • tabtab 7y ago
    That doesn't tell one directly if heat causes fire or the other way around. One just knows there is some relationship. Recognizing a relationship itself is often enough to trigger experimentation.
    • deleted 7y ago
      [deleted]
• yoquan 7y ago
  To me learning cause-and-effect is a non trivial process, when recognizing relationship often comes first and intrigues "formal" reasoning process afterwards (if any). The more formal in the later process, the closer we reach to the real "knowledge". So your suggestion is quite practical, in the sense that we can start from that and figure out how to push the engine toward the more formal spectrum.