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We have more neurons and, in particular, they are arranged in architecturally interesting ways, rather than just feedforward networks as in most current strateg

We have more neurons and, in particular, they are arranged in architecturally interesting ways, rather than just feedforward networks as in most current strategies for deep learning. There is of course work being done on recurrent networks, but it's basically in its infancy. And what is being done so far is still nowhere near as complex as the human brain, which clearly has many distinct areas with distinct but of course interrelated functions.

maroonblazer 11y ago
I'm not an AI researcher so this is probably a naive question: Do we necessariliy need to mimic the architecture of the human brain to achieve AGI? As someone (Chalmers?) once said about the problem of consciousness: we didn't need to replicate the flapping of wings or the locomotion of sea creatures before taking to the air or underwater. Might it not also be the case with AI that there's some fundamental principle we've yet to discover that just so happens to have expression in the substrate of 1200cc's of fatty tissue, but could possess the same fidelity (and greater) in silicon and looks nothing like the architecture of the human brain?
- shock-value 11y ago
  With respect to needing to mimic biology: not necessarily, though it is certainly true that e.g. a purely feedforward model could NOT achieve AGI, since it can't make predictions over time. Feedforward just means that the outputs of the network don't "feed" back into its inputs -- and as a result the same input will always cause the same output regardless of previous inputs. Humans/animals certainly take time into account when acting -- e.g. something as simple as coordinating muscles for simple movements requires varied output over time, despite most inputs (in the form of tactile senses) being mostly unchanged (at least for some kinds of movement, e.g. waving your hand). Recurrent models (where some outputs are connected back to the inputs) are one possible way to account for time, but the work is still really early on these methods, and it's not clear what architecture (e.g. which and how many inputs and outputs should connect) would be efficacious.
- rdtsc 11y ago
  > of wings or the locomotion of sea creatures before taking to the air or underwater. It is not all or nothing. There are analogies at different abstraction level. Yes we probably shouldn't be plugging in continuous differential equations to mimic chemistry of neuroreceptors, cell sodium channels etc. to replicate it at that level. So in that respect we agree, airplanes are not like birds. Far from it. No flapping. Not composed of cells. Not biological in nature. On the other hand, there is another way to look at systems -- look at higher functional components and how they are connected. So maybe there is a language processing area connecting to memory. And so on. This is called the connectome of the brian as well. Which identifies what parts are connected to what. In this regards airplanes are similar to birds. They both have wings. Fuselage. A tail. They are built with similar structural material contraints -- light and durable. Aluminum, titanium for aircraft, and porous bones for birds. Another way to look at it is in so many decades of AI, we haven't yet come up with another model. So while having to wait for enlightment to hit us one day why not learn from an already existent example.

maroonblazer 11y ago

I'm not an AI researcher so this is probably a naive question: Do we necessariliy need to mimic the architecture of the human brain to achieve AGI? As someone (Chalmers?) once said about the problem of consciousness: we didn't need to replicate the flapping of wings or the locomotion of sea creatures before taking to the air or underwater. Might it not also be the case with AI that there's some fundamental principle we've yet to discover that just so happens to have expression in the substrate of 1200cc's of fatty tissue, but could possess the same fidelity (and greater) in silicon and looks nothing like the architecture of the human brain?

shock-value 11y ago
With respect to needing to mimic biology: not necessarily, though it is certainly true that e.g. a purely feedforward model could NOT achieve AGI, since it can't make predictions over time. Feedforward just means that the outputs of the network don't "feed" back into its inputs -- and as a result the same input will always cause the same output regardless of previous inputs. Humans/animals certainly take time into account when acting -- e.g. something as simple as coordinating muscles for simple movements requires varied output over time, despite most inputs (in the form of tactile senses) being mostly unchanged (at least for some kinds of movement, e.g. waving your hand). Recurrent models (where some outputs are connected back to the inputs) are one possible way to account for time, but the work is still really early on these methods, and it's not clear what architecture (e.g. which and how many inputs and outputs should connect) would be efficacious.
rdtsc 11y ago
> of wings or the locomotion of sea creatures before taking to the air or underwater. It is not all or nothing. There are analogies at different abstraction level. Yes we probably shouldn't be plugging in continuous differential equations to mimic chemistry of neuroreceptors, cell sodium channels etc. to replicate it at that level. So in that respect we agree, airplanes are not like birds. Far from it. No flapping. Not composed of cells. Not biological in nature. On the other hand, there is another way to look at systems -- look at higher functional components and how they are connected. So maybe there is a language processing area connecting to memory. And so on. This is called the connectome of the brian as well. Which identifies what parts are connected to what. In this regards airplanes are similar to birds. They both have wings. Fuselage. A tail. They are built with similar structural material contraints -- light and durable. Aluminum, titanium for aircraft, and porous bones for birds. Another way to look at it is in so many decades of AI, we haven't yet come up with another model. So while having to wait for enlightment to hit us one day why not learn from an already existent example.