Recent article in The New Yorker:
http://www.newyorker.com/online/blogs/newsdesk/2012/11/ibm-brain-simulation-compass.html
Here is the research report from IBM, with the simple title "10^14":
http://www.modha.org/blog/SC12/RJ10502.pdf
It's nothing like a real brain simulation, of course, but illustrates that hardware to do this is getting very close.
There is likely to be quite a long overhang between the hardware and the software...
I think important caveats need to be kept in mind. From the New Yorker article:
I.B.M.’s Compass has more neurons than any system previously built, but it still doesn’t do anything with all those neurons. The short report published on the new system is full of vital statistics—how many neurons, how fast they run—but there’s not a single experiment to test the system’s cognitive capacities. It’s sort of like having the biggest set of Lego blocks in town without a clue of what to make out of them. The real art is not in buying the Legos but in knowing how to put them together. Until we have a deeper understanding of the brain, giant arrays of idealized neurons will tell us less than we might have hoped.
Thanks for this. The latest research report 10^14 already appears to be a significant update on that paper.
IBM now report roughly eight times as many simulated neurons and synapses, while the slow-down has gone from ~400x real-time to ~1500x real time. That works out at a factor > 2 in hardware improvement within a matter of months. They are using a custom hardware architecture and presumably there are still a lot of optimisations to be made. It can't be very long before this can run in real time.
As said in other comments, nobody knows how to program this yet...
It's nothing like a real brain simulation, of course, but illustrates that hardware to do this is getting very close.
They simulate model neurons. Those neurons they are less complex than the real neurons that we have in our head. The way in which real neurons change the amount of ion channels on their membrane for long-term plasticity is neither fully understood nor easy to simulate.
"What do you mean by "strong AI is refuted""
The strong AI hypothesis is that consciousness is the software running on the hardware of the brain. Therefore one does not need to know or understand how brains actually work to construct a living conscious mind. Thus any system that implements the right computer program with the right inputs and outputs has cognition in exactly the same literal sense that human beings have understanding, thought and memory. It was the belief of strong AI proponents such as Marvin Minski at MIT and others that they were literally creating minds when writing their programs. They felt no need to stoop so low as to poke around in actual brains and get their hands dirty.
Computers are syntactical machines. The programs they execute are pure syntax and have no semantic content. Meaning is assigned, it is not intrinsic to symbolic logic. That is it's strength. Since (1) programs are pure syntax and have no semantic content and (2) minds do have semantic content and (3) syntax is neither sufficient for nor constitutive of semantics. It must follow that programs are not by themselves constitutive of, nor sufficient for, minds. The strong AI hypothesis is false.
Which means that IBM is wasting time, energy and money. But.... perhaps their efforts will result in spin off technology so not all is lost.
How would one determine whether a given device/system has this "semantic content"? What kind of evidence should one look at? Inner structure? Only inputs and outputs? Something else?