...trillion synapses [the connections between neurons] between the static pages on the Web. The human brain has about 100 times that number—but brains are not doubling in size every few years. The Machine is."
Models of the human mind are being fed with information harvested from the real thing. Advances in studies on live, functioning brains through techniques such as neuroimaging using positron emission technology (PET) and functional magnetic resonance imaging (FMRI) show neural activity in real time, to increasingly high precision. FMRI is particularly exciting, as it is non-invasive and completely harmless: it works by inducing hydrogen molecules in the brain to emit radio waves through a very strong magnetic field. With it, the interrelationships between precisely defined areas of the brain can be studied in action again and again in the same subject — individual brains can be wired quite differently — revealing subtle details of processing.
For example, we now know that even while performing specific tasks, the brain activates areas not directly connected with the business in hand. Something involving the right hand which would directly involve the left hemisphere also activates the right. This is thought to allow continuous monitoring and learning through feedback from the task, while being prepared to react to unexpected events.
Quantum neural networks
Nanotechnology is constantly producing new possibilities for computation beyond silicon: semiconducting nanotubes of carbon, optical circuits switching at many terahertz and quantum computer devices. One particular strand of quantum computing is creating particular interest, the quantum neural network (QNN). The characteristics of quantum devices can be described to match those of neural networks quite closely — a neural network takes a wide variety of inputs and sees if they match a condition it had previously learned. It does this by having a lot of processors linked up in a parallel architecture similar to the way neurons are linked in animal brains.
So far, neural networks have been built out of standard processors programmed to emulate neurons and synapses, or specialised circuits which do that emulation in hardware. Stanford professor Kwabena Boahen says that he is "trying to do now is build chips with something like 100,000 neurons, and then build a multiple-chip network that gets up to about one million neurons. With a network of that size, you can model what the different cortical areas are doing and how they are talking to each other".
QNN replaces the neurons with a molecular model, with the synapses replaced by the mathematical interactions of the atomic bonds. There are massive practical problems with this, not least that quantum computers are very sensitive to their environment and hard to program, as well as deep suspicion in the research community that 'quantum consciousness' is a cover for arm-waving new-age pseudoscience. Nevertheless, the mathematics indicates that QNNs may be among the most efficient ways of modelling neural activity.
AIs can build better AIs
Perhaps the most way-out prediction of AI is the singularity, a science fiction concept first proposed as a legitimate event by writer and mathematician Vernor Vinge. In this, the development of AI reaches a point where it exceeds human capabilities and can consequently design ever more powerful versions of itself. This feedback effect would quickly lead to an intelligence far beyond our understanding or prediction, with a variety of results depending on the optimism or otherwise of whoever's doing the prediction.
One of the chief proponents of this is inventor Ray Kurzweil, who has proposed an enhanced version of Moore's Law called the Law of Accelerating Returns. This purports to show that technology in general improves exponentially, with new ideas turning up whenever a barrier is reached. The exact timing of the Singularity has not been agreed, although by extrapolating current trends in computer capabilities a date between 2025 and 2045 is most likely. Against that possibility, it must be noted that with no AI is at present capable of the intelligence of even the more primitive mammals. We'll have to fit in the engineering equivalent of 150 million years of evolution in a couple of decades.
