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How will we build anartificial human brain?

There's an ongoing debate among

neuroscientists, cognitive scientists, and

even philosophers as to whether or not we

could ever construct or reverse engineer the

human brain. Some suggest it's not

possible, others argue about the best way to

do it, and still others have already begun

 working on it.

Regardless, it's fair to say that ongoing breakthroughs in brain science are steadily paving the

 way to the day when an artificial brain can be constructed from scratch. nd if we assumethat cognitive functionalism holds true as a theory ! the idea that our brains are a kind of 

computer ! there are two very promising approaches worth pursuing.

"nterestingly, the two approaches come from two relatively different disciplines# cognitive

science and neuroscience. $ne side wants to build a brain with code, while the other wants to

recreate all the brain's important functions by emulating it on a computer. "t's anyone's guess

at this point in time as to who wil l succeed and get there first, if either of them.

%efore we take a deeper look into these two approaches, however, it's worth reviewing what

our friend lan Turing had to say about brains.

The Church-Turing Hypothesis

&iven that scientists are looking to model the human brain in digital substrate i.e. acomputer(, they're having to work in accordance to a rather fundamental assumption#

computational functionalism. This goes back to the )hurch*Turing thesis which states that a

Turing machine can emulate any other Turing machine. +ssentially, this means that every 

physically computable function can be computed by a Turing machine. nd if brain activity is

regarded as a function that is physically computed by brains, then it should be possible to

compute it on a Turing machine, namely a computer.

So, if you believe that there's something mystical or vital about human cognition you're

probably not going to put too much credence into these two approaches.

$r, if you believe that there's something inherently uniue about intelligence that can't be

translated into the digital realm, you've got your work cut out for you to e-plain what that is

e-actly ! keeping in mind that any informational process is computational, including those

 brought about by electrical and chemical reactions. inds are what brains do, so it's not too

implausible to suggest that minds are what computers can do, too.

Rules-based artificial intelligence

$ne very promising strategy for building brains is the rules*based approach. The basic idea is

MAY 2, 2012 8:05 AMBY GEORGE DVORSKY

 

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that scientists don't need to mimic the human brain in its entirety. "nstead, they they need to figure out the algorithms of 

intelligence and the ways that they're intricately intertwined. )onseuently, it's this approach

that e-cites the cognitive scientists.

Some computer theorists insist that the rules*based approach will get us to the brain*making

finish line first. %en &oertel is one such theorist. His basic argument is that other

approaches over*complicate and muddle the issue. He likens the approach to building

airplanes# we didn't have to reverse engineer the bird to learn how to fly.

+ssentially, cognitive scientists like &oertel are confident that the hard*coding of artificial

general intelligence &"( is a more elegant and direct approach. "t'll simply be a matter of 

identifying and developing the reuisite algorithms sufficient for the emergence of the traits

they're looking for in an &". They define intelligence in this conte-t as the ability to detect

patterns in the world, including in itself.

To that end, &oertel and other " theorists have highlighted the importance of developing

effective learning algorithms. new mind comes into the world as a blank slate, they argue,

and it spends years learning, developing, and evolving. "ntelligence is sub neuroscientists are not having to re*invent the wheel. 0atural

selection, through e-cruciatingly tedious trial*and*error, created the human brain ! and all

 without a preconceived design. They say there's no reason to believe that we can't model this

structure ourselves. "f the brain could come about through autonomous processes, argue

neuroscientists, then it can most certainly come about through the diligent work of intelligent

researchers.

 4hen talking about 4%+ it's important to distinguish between emulation and simulation.

+mulation refers to a A*to*A model where all relevant properties of a system e-ist. This doesn't

mean re*creating the human brain in e-actly the same way as it resides inside our skulls.

Rather, it implies the re*creation of all its properties in an alternative substrate, namely a

computer system.

oreover, emulation is not simulation. 0euroscientists are not looking to give the

a""earance of human*euivalent cognition. simulation implies that not all properties of a

model are present. gain, it's a complete A#A emulation that they're after.

  number of critics point out that we'll never completely emulate the human brain on account

of the chaos and comple-ity inherent in such a system. $thers disagree. s researchers from

$-ford @niversity have pointed out, we will not need to understand the whole system in order

to emulate it. 4hat's reuired is a functional understanding of all necessary low*level

information about the brain and knowledge of the local update rules that change brain states

from moment to moment. 4hat is meant by low*level at this point is an open uestion, but itlikely won't involve a molecule*by*molecule understanding of cognition. nd as Ray 2urweil

has revealed, the brain contains masterful arrays of redundancy> it's not as complicated as we

currently think.

"n order to gain this =low*level functional understanding= of the human brain, neuroscientists

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w will we build an artificial human brain? http://io9.com/5906945/how-will-we-build-an-artificial-human-brain

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