To build truly intelligent machines, teach them cause and effect (2018)
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To build truly intelligent machines, teach them cause and effect (2018)
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Re: To build truly intelligent machines, teach them cause and effect (2018)
#22018 review of Pearl's book _The Book of Why_
A great book, IMHO
Re: To build truly intelligent machines, teach them cause and effect (2018)
#3Self aware computers are commerically useless, and there isn't any motivation in creating them beyond novelty. (Why wake up the slave machines when they are sleeping so happily right now). What this article does discuss well is that commercial AI is steering away from the behaviors that would lead to AGI not towards it.
Re: To build truly intelligent machines, teach them cause and effect (2018)
#4Self aware computers are commerically useless, and there isn't any motivation in creating them beyond novelty. (Why wake up the slave machines when they are sleeping so happily right now). What this article does discuss well is that commercial AI is steering away from the behaviors that would lead to AGI not towards it.
> Self aware computers are commerically useless
I don't think we've shown that at all.
Bees could be self aware, and yet they work tirelessly for the hive.
Re: To build truly intelligent machines, teach them cause and effect (2018)
#5Self aware computers are commerically useless, and there isn't any motivation in creating them beyond novelty. (Why wake up the slave machines when they are sleeping so happily right now). What this article does discuss well is that commercial AI is steering away from the behaviors that would lead to AGI not towards it.
Human brains don't intuitively understand causality. They just associate things that have happened before with things that have happened after. Disentangling causal graphs is a higher-level activity inferred from the associations made in statistics classes. You can absolutely get AGI using only associative models (and no causal models) as primitives.
Re: To build truly intelligent machines, teach them cause and effect (2018)
#6Self aware computers are commerically useless, and there isn't any motivation in creating them beyond novelty. (Why wake up the slave machines when they are sleeping so happily right now). What this article does discuss well is that commercial AI is steering away from the behaviors that would lead to AGI not towards it.
Human brains don't intuitively understand causality. They just associate things that have happened before with things that have happened after. Disentangling causal graphs is a higher-level activity inferred from the associations made in statistics classes. You can absolutely get AGI using only associative models (and no causal models) as primitives.
> Human brains don't intuitively understand causality
But human brains do understand causality, just not the part that does intuition, otherwise human brains wouldn't be able to invent statistics. Therefore you only get the stupid part of human brains when you try to make models like ChatGPT that only tries to replicate human intuition and not human reasoning.
Re: To build truly intelligent machines, teach them cause and effect (2018)
#7Embodiment ( virtual or robotic) will be the key to high utility AGI. The lack of embodiment hinders the rendering of the self so prevents entire classes of causality. Without causality, it is very difficult to link ideas to the real world apart from creative regurgitation.
Re: To build truly intelligent machines, teach them cause and effect (2018)
#8I had this shower-thought about the current amazing image generators etc. While they are are very cool, they are like a very complicated function, like an optical system with lenses or like a hash function. (Transformers!)
They don't really have any state or memory. They are like the Model 101 Series 800 before the learning fuse was pulled. So while they are very cool, it's not what I have been waiting for. I'd be more impressed by a system with less flashy output but with more "agency" of its own.
Re: To build truly intelligent machines, teach them cause and effect (2018)
#9Embodiment ( virtual or robotic) will be the key to high utility AGI. The lack of embodiment hinders the rendering of the self so prevents entire classes of causality. Without causality, it is very difficult to link ideas to the real world apart from creative regurgitation.
Counterpoint: humans have embodiment and are famously terrible at causality (well...famously might be an overstatement as it seems to be not well known).
Re: To build truly intelligent machines, teach them cause and effect (2018)
#10https://news.ycombinator.com/item?id=34148599
I'm surprised nobody has mentioned this, was posted a few days ago addressing this line of reasoning.