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May 2, 2012

How Will We Re-Build A Human Brain?




 Artificial Brain
George Dvorky has written a great piece for iO9 outlining a significant element to singularity theory:  creation of an artificial brain.  Dvorky writes that two approaches are being taken to work towards the goal: rules-based artificial intelligence and whole brain emulation.
Guest writing at i09 this week, George Dvorky of Sentient Developments asks the question, "How will build an artificial human brain?"  Almost daily at 33rd Square, and other sources announce breakthroughs in neuroscience, scanning, artificial intelligence and other areas that are promising to bring us closer to the realization of a substrate independent mind.

Assuming that our brains are essentially a biological form of a computer, what Dvorky calls, the theory of cognitive functionalism, reverse engineering the circuit diagram and software of a brain is not entirely out of the realm of technical possibility.

Dvorky writes that there two approaches from two relatively different disciplines progressing towards an artificial brain: cognitive science and neuroscience. One side wants to build a brain with code, while the other wants to recreate all the brain's important functions by duplicating it on a computer and "It's anyone's guess at this point in time as to who will succeed and get there first, if either of them."

Computational functionalism writes Dvorky, goes back to the Church-Turing thesis which states that a Turing machine can emulate any other Turing machine. Essentially, this means that every physically computable function can be computed by a Turing machine. And 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.

Refering to artificial intelligence, Dvorky continues,
One very promising strategy for building brains is the rules-based approach. The basic idea is that scientists don't need to mimic the human brain in its entirety. Instead, they just have to figure out how the "software" parts of the brain work; they need to figure out the algorithms of intelligence and the ways that they're intricately intertwined. Consequently, it's this approach that excites the cognitive scientists. 

Many computer theorists insist that the rules-based approach will get us to the brain-making finish line first. Ben Goertzel is one such example. 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.
Essentially, cognitive scientists like Goertzel are confident that the hard-coding of artificial general intelligence (AGI) is a more elegant and direct approach. It'll simply be a matter of identifying and developing the requisite algorithms sufficient for the emergence of the traits they're looking for in an AGI. They define intelligence in this context as the ability to detect patterns in the world, including in itself.
To that end, Goertzel and other AI theorists have highlighted the importance of developing effective learning algorithms. A new mind comes into the world as a blank slate, they argue, and it spends years learning, developing, and evolving. Intelligence is subject to both genetic and epigenetic factors, and just as importantly, environmental factors. It is unreasonable, say the cognitive scientists, to presume that a brain could suddenly emerge and be full of intelligence and wisdom without any actual experience.
On the other hand Dvorky points out that many neuroscientists are not convinced of the AI approach to artificial brains.  Their path is to reverse engineer the brain.

Whole brain emulation (WBE), the idea of reverse engineering the human brain, makes both intuitive and practical sense. Unlike the rules-based, AI approach, WBE works off a tried-and-true working model writes Dvorky. Natural selection, through excruciatingly 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. 

Dvorky clarifies:
Emulation refers to a 1-to-1 model where all relevant properties of a system exist. This doesn't mean re-creating the human brain in exactly 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. 
Moreover, emulation is not simulation. Neuroscientists are not looking to give the appearance of human-equivalent cognition. A simulation implies that not all properties of a model are present. Again, it's a complete 1:1 emulation that they're after.
Work toward whole brain emulation will require considerable advances in neuroscience, genetics, scanning and computer science, but all of these technologies are currently experiencing exponential and greater than exponential growth at the moment.

Dvorky concludes that Ben Goertzel and Ray Kurzweil's predicted time-frames for an artificial brain is overly optimistic, however he is relatively certain that it will take place.

Moreover, he cites Singularity Institute computer theorist Eliezer Yudkowsky's claim that, because of the brain's particular architecture, we may be able to accelerate its processing speed by a factor of a million relatively easily. Consequently, predictions as to when we may achieve greater-than-human machine intelligence will likely co-incide with the advent of a fully emulated human brain.

"It's worth noting that, given the capacity to re-create a human brain in digital substrate, we won't be too far off from creating considerably greater-than-human intelligence."

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