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The human brain contains about 10^11 neurons. Each neuron has about 510^3 synapses, and signals are transmitted along these synapses at an average frequency of

by themgt 11y ago

The human brain contains about 10^11 neurons. Each neuron has about 510^3 synapses, and signals are transmitted along these synapses at an average frequency of about 10^2 Hz. Each signal contains, say, 5 bits. This equals 10^17 ops.* This kind of nonsense is why no one should take Bostrom seriously. We did not then and do not know even begin to know how to write software to "simulate" a human brain, or whether such a task is even possible with modern day tools. Multiplying random neurobiology stats times 0.5 bits pulled out of your ass == AI in 2004? We have "AI" that can drive a car or a copter, play Chess or Go, translate speech to text, do image recognition ... but what we mean by human intelligence is something different. And I see no evidence anyone has made much progress developing a truly biological-like AI even at the level of say, a mouse. Which according to Bostrom's math ought to be doable in a 2U chassis by now, right? If someone does succeed in writing mouse-AI or dog-AI, I'd believe that could be scaled up to human-level intelligence very rapidly. But it's clear to me there's (at least one) fundamental breakthrough missing from the current approach, because my dog can't play chess or drive a car, but he has a degree of sentience and awareness (and self-awareness) that no 2016 AI even approaches.

davmre 11y ago
I don't know where in the article you see Bostrom predicting AI in 2004. That's a lower bound on when we might achieve the needed hardware capacity. Bostrom's predictions about hardware advancements have more or less come to pass (closer to the upper than lower bounds, granted). I think it's a common opinion among AI researchers that the level of computing power available today is plausibly sufficient for human-level AI, if only we knew how to build the software. The closest Bostrom gets to a prediction on software is that the "the required neuroscientific knowledge" to do brain simulation "might be obtained" by 2012. Which has turned out to be optimistic, but is very far from the strawman position of "hardware capability implies software capability" that you seem to be painting him with.
zardo 11y ago
http://www.openworm.org/ http://www.openworm.org/ We are on the cusp of revolutionary general nematode level intelligence. Soon we'll be able to upload nematode minds to the cloud and they can live forever in the nemamatrix.
- pnut 11y ago
  "You get used to it. I…I don’t even see the code. All I see is blue squiggle, green squiggle, translucent squiggle."
osterbit2 11y ago
there seems to be a large group of cs folks who are very out of touch with the fact that we currently are able to build computational systems that replicate regions of cortex (visual cortex, auditory cortex). It's not general intelligence or superintelligence--really dumb animals can see and hear. And there's a lot more to cognition than simply converting external signals into meaningful representations. But I think it's pretty arguable that deep neural nets (or maybe networks of DNNs) DO represent an architecture sufficiently powerful for strong/general AI to develop. And if you can venture far enough to imagine computational systems on non-von-neumann architectures, you might conclude that the level of abstraction that Bostrom is operating around to describe computation isn't total nonsense
eli_gottlieb 11y ago
>We have "AI" that can drive a car or a copter, play Chess or Go, translate speech to text, do image recognition ... but what we mean by human intelligence is something different. Says who? Who says that "general" intelligence doesn't just mean doing the same glorified statistics with larger search spaces, leading to greater difficulty weighting-and-searching those spaces with statistical data, leading to a greater difficulty writing learning systems that perform well without discovering new statistical principles?

by themgt 11y ago

davmre 11y ago

I don't know where in the article you see Bostrom predicting AI in 2004. That's a lower bound on when we might achieve the needed hardware capacity. Bostrom's predictions about hardware advancements have more or less come to pass (closer to the upper than lower bounds, granted). I think it's a common opinion among AI researchers that the level of computing power available today is plausibly sufficient for human-level AI, if only we knew how to build the software. The closest Bostrom gets to a prediction on software is that the "the required neuroscientific knowledge" to do brain simulation "might be obtained" by 2012. Which has turned out to be optimistic, but is very far from the strawman position of "hardware capability implies software capability" that you seem to be painting him with.

zardo 11y ago

http://www.openworm.org/ http://www.openworm.org/ We are on the cusp of revolutionary general nematode level intelligence. Soon we'll be able to upload nematode minds to the cloud and they can live forever in the nemamatrix.

pnut 11y ago
"You get used to it. I…I don’t even see the code. All I see is blue squiggle, green squiggle, translucent squiggle."

osterbit2 11y ago

there seems to be a large group of cs folks who are very out of touch with the fact that we currently are able to build computational systems that replicate regions of cortex (visual cortex, auditory cortex). It's not general intelligence or superintelligence--really dumb animals can see and hear. And there's a lot more to cognition than simply converting external signals into meaningful representations. But I think it's pretty arguable that deep neural nets (or maybe networks of DNNs) DO represent an architecture sufficiently powerful for strong/general AI to develop. And if you can venture far enough to imagine computational systems on non-von-neumann architectures, you might conclude that the level of abstraction that Bostrom is operating around to describe computation isn't total nonsense

eli_gottlieb 11y ago

>We have "AI" that can drive a car or a copter, play Chess or Go, translate speech to text, do image recognition ... but what we mean by human intelligence is something different. Says who? Who says that "general" intelligence doesn't just mean doing the same glorified statistics with larger search spaces, leading to greater difficulty weighting-and-searching those spaces with statistical data, leading to a greater difficulty writing learning systems that perform well without discovering new statistical principles?