GPGPU in the brain: neuroscientist-approved transhumanism

I haven't been able to find a single neuroscientist publicly agree with Ray Kurzweil that either neuroscience-based AI or brain-computer interfaces are likely to progress far enough this century to lead to a singularity.

Given all this cause for pessimism, and the weight of scientific consensus apparently behind it, I was pleased to read that a prominent neuroscientist -- especially one who's been an outspoken critic of both Ray Kurzweil and IBM's artificial-brain projects -- had recently predicted a transhuman revolution. I was even more pleased when I realized the source of that revolution was analogous to a computing technology I'm studying for my master's thesis: general-purpose computation on graphics-processing units (GPGPU).

Virtual devices and GPGPU's untapped potential: my thesis project

In 2001, Nvidia released the GeForce 3, the first video card with programmable shaders. They intended it to let game programmers invent new visual effects. But experts in high-performance computing started to wonder: How much processing power is on those video cards and now programmable? And why can't we use it to crunch numbers for lab studies and simulations? Research into General-Purpose computing on Graphics Processing Units (GPGPU) began. By the time Oak Ridges National Laboratory ordered 18,000 GPUs from Nvidia last October, one thing was clear: Video cards aren't just for games anymore.

But if Oak Ridges can use video cards to run applications faster, why does yours go unused when you're not playing games? Because of several software problems, one of which I'll attempt to solve in my master's thesis.