Large scale neurosimulation.

For those of you who watch the tech field, you've no doubt heard of Ray Kurzweil, the inventor, technologist, and futurist who's been promulgating the "The Singularity is near!" meme since the 1980's. Love him or hate him, he's a brilliant man who's invented some fantastic, practical things. One of the things he talks about a great deal is how strong AI, which many now refer to as Artificial General Intelligence (i.e., human-like intelligence and sapience) is just a few years away, and he cites Moore's Law as evidence of this. Of course, a lot of people think he's pushing jetwash and get on with their lives. So, when he blogs about things like machine learning and AI a lot of people are prone to ignore his observations about what is being done right now, mistaking them for hypotheses (which he's also prone to, make no mistake). So, it came with a groan followed by some surprised Google searches when a post hit one of the Zero State mailing lists about a large scale software simulation of part of a human brain. At first I thought to myself "Yeah, sure, just like that cat brain simulation back in 2009 was only a very small part of a cat's brain, and not the whole thing." I then did some digging on the project called SPAUN (henceforth capitalized because it's an acronym) and found myself amazed. SPAUN (Semantic Pointer Architecture Unified Network) is a software simulation of a network of 2.5 million human-like neurons, each of which includes neurotransmitter concentrations, synaptic connections, and even voltage thresholds in the models. The project is based out of the University of Waterloo, and they've made the model available for download for people to reproduce their results and hack on if they so choose. The massive neural network of SPAUN is actually a network of other neural networks which are tailored for specific tasks, such as identifying shapes, gathering data from a simulated eye, positioning a simulated hand, and drawing conclusions based on data it has in its short term (working) memory. In other words, it is capable of perceiving its environment through a 784 pixel "eye", analyzing what its senses take in, deciding what to do, and writing its answer with a simulated hand, arm, and pencil. The experiments they ran are tasks that a psychologist might ask a human child to carry out as part of an IQ assessment. Things like, "Given these three numbers tell me what the next number in the sequence would be," or "Tell me what the next two elements in this pattern would be." There are three fascinating aspects to the SPAUN experiment that I feel should be emphasized. The first is that between tasks a programmer does not have to go in and rework any of the code. There is no setup time involved in having the neural network run the two tests I described above. The simulation is sufficiently flexible in its capabilities that this is unnecessary. The second is that SPAUN is capable of figuring out which of the tasks that it is to carry out entirely on its own; the researchers don't have to open a terminal window and type (for example), "SPAUN, FIGURE OUT THE FIFTH AND SIXTH ELEMENTS OF THIS PATTERN: 1, 1, 2, 22", they pose the question as-is ("1, 1, 2, 22") and SPAUN does its thing ("3, 333.") without any further interaction with the outside world. The third is that the SPAUN model incorporates simulations of several vastly different areas of the human brain, including the basal ganglia, the visual cortex, the prefrontal cortex, and the thalamus. All in the same model. All running simultaneously. All collaborating with one another, analy