I hope we can at least see some white papers soon about the architecture--I wonder how programmable it is.
Google is doubling down on hosting as a source of future revenue, and they're doing that by building an ecosystem around Tensorflow.
What I think is interesting is how weak Apple looks. Amazon has the talent and money to be able to compete with Google on this playing field. Microsoft is late, but they can, too.
Where's Apple? In the corner dreaming about mythical self-driving luxury cars?
[1]: http://spectrum.ieee.org/semiconductors/design/the-death-of-...
Where Apple really looks weak is in datacenters, networking, and cloud services.
I get the feeling from today's announcements that Google sees the 2021 version of Google Now as the selling point for their 2021 Nexus line.
I don't think Apple is preparing to compete on that.
Whether that's good or not may be arguable, but it's certainly a selling point for many and I don't see Google or any other company's offerings approaching the same experience, and I suspect that's by design; they have to be more open and support all devices but that kinda dilutes everything. Apple will only get stronger in that aspect IMO.
I’d hope someone somewhere steals the blueprints and posts all of them publicly online.
The whole point of patents was that companies would publish everything, but get 20 years of protection.
But by now, especially companies like Google don’t do so anymore – and everyone loses out.
EDIT: I’ll add the standard disclaimer: If you downvote, please comment why – so an actual discussion can appear, which usually is a lot more useful to everyone.
Re EDIT: Downvotes must be comment-mandatory or not allowed otherwise.
So... just use Google's machine learning cloud thingy.
The software can build the community, where the supercharging is only available when you run it on Google cloud.
(although GPU performance isn't bad either, so you don't have to, thus community)
Machine learning isn't just targeting the AI researcher market though -- it's widely used by a huge number of companies, and of course, by many of Google's most important products. I would argue that those markets combined are larger than gaming.
There isn't much data yet but I'm also guessing they probably have access to much more RAM than NVidia cards and can process much bigger data sets
Look at how much faster ASICs for bitcoin mining are than the GPU... orders of magnitude.
Additional die space on additional functionality might hurt the power envelope (which is where the focus on performance / watt rather than performance kicks in) but it doesn't make your chips slower per se.
Furthermore the fact that ML can be error tolerant means you also get to optimize certain floating point operations for speed or energy efficiency at the cost of accuracy. NVIDIA doesn't get to do this in their linear algebra support.
{(others, ~bottom) (google, ~top)}