Having an ambitious stretch objective enables the design of a new architecture that can address a massive data processing project. It will be interesting what spin-offs emerge. Once done, data mining all of accumulated human knowledge would seem to be a modest challenge in comparison.
Actually I believe the current standard model is Inflation Model, a variation of Big Bang Model. There are experimentally testable predictions made by these models, such cosmic microwave background radiation. So people are following scientific methods. I do know what other scientific methods you are referred to.
Anyway, what is wrong with spending money on investigate "Something happened a long time ago and we don't really know what it is was".
I am pretty certain @daleste that IBM is seeing potential to make big money on this technology eventually...otherwise they will simply not pursue it...water-cooled 3D technology is one of the few technologies that can deliver exabyte computing as the power dissipation is the most limiting factor...Kris
"...notably the Big Bang from which the known universe originated."
Question: Why is it that theories have become commonly accepted as something more than theories? What ever happened to the scientific method? Or is "Big Bang" a euphamism for 'Something happened a long time ago and we don't really know what it is was. But we need to keep the research dollars flowing, so we're going to call it by this cool alliterative name and hope that nobody notices.'?
Other than that, go Big Blue!
3D chips are the future of computing. This gives a good overview: http://www.jilp.org/vol9/v9paper9.pdf Within a few years, we will have the computing power of today's desktop in a package the size of an Aruduino, with what was spread out in 2 dimensions stacked up in 3.
I believe that the water-cooled 3D chips IBM develops to process exabytes of data daily will have myriad commercial applications as the Internet-of-Things begins streaming sensor data from every corner of the Earth up to cloud computers. The more analytics that can be performed by network edge-devices with these 3D chips, the less congestion there will be on the Internet as a result.
What are the engineering and design challenges in creating successful IoT devices? These devices are usually small, resource-constrained electronics designed to sense, collect, send, and/or interpret data. Some of the devices need to be smart enough to act upon data in real time, 24/7. Are the design challenges the same as with embedded systems, but with a little developer- and IT-skills added in? What do engineers need to know? Rick Merritt talks with two experts about the tools and best options for designing IoT devices in 2016. Specifically the guests will discuss sensors, security, and lessons from IoT deployments.