NVIDIA Hopes To Sell More Chips By Bringing AI Programming To the Masses
jfruh writes: Artificial intelligence typically requires heavy computing power, which can only help manufacturers of specialized chip manufacturers like NVIDIA. That's why the company is pushing its Digits software, which helps users design and experiment with neural networks. Version 2 of digits moves out of the command line and comes with a GUI interface in an attempt to move interest beyond the current academic market; it also makes programming for multichip configurations possible.
Crunch all you want, we'll make more. Sounds artificially intelligent.
We welcome our new multichip overlords
All Hail Digits 2.0
...with ARTIFICIAL neural networks. ANNs have a teeny weeny bit in common with neural networks, about which we STILL know rather fucking little when it comes to creation of intelligence.
This is Nvidia. Don't buy into this based on any promises of what is to come, no matter how reasonable they seem.
Last summer I bought a Nvidia Tegra Note 7 tablet based on promises that Android 5 (Lollipop) was coming out for it "real soon". They even stated that it was easy to port Lollipop on the Tegra Note 7 since it was basically a stock Android design with little or on deviation from the standard design. That "real soon" slipped to February of 2015 and when February 2015 came and went Nvidia became strangely mute on the subject, ignoring customers' inquiries.
A claimed Nvidia employee even posted here as an AC that it was a shame what happened to the Tegra Note 7 customers, but explained that the U.S.A. developers wanted to work on the new stuff and the Tegra Note 7 project was shipped overseas, where no one wanted to work on it either (and apparently did not).
My Tegra Note 7 tablet is the last thing that Nvidia will ever sell me. If you chose to do business with them then I may not be able to talk you out of it, but do so based on what they deliver today, not on promises of things that will never come.
I'm an American. I love this country and the freedoms that we used to have.
From TFA there was no pic of the UI, nor any mention of tech specs aside from a lot of nebulous details. From nVidia's website ...
* https://developer.nvidia.com/d...
They are really trying to get people on board about how much better / faster their GPU solutions are ...
* http://www.nvidia.com/object/m...
The problem is that there are lot of "niche" use cases. If your problem domain maps to the GPU then yeah, mjaor speedup. If not, well, then you're SOL running on "slow" CPUs.
NV open sourced CUDA in 2011, but I don't believe there are any other implementations out there. The rest of the world continues adopting OpenCL and now the whole Khronos supergroup is super hyper for Vulkan (NV even giving a solid thumbs up), with Apple and NV being the two rogue vendors pushing proprietary wares (Metal and CUDA). Even with NVidia doing really *really* well in the GPGPU market, even with a really great dev env, the extreme proprietary-ness of CUDA makes it really hard to sell to the alpha techies.
Cuda has a lot of traction in academic and applied fields, but the technical industry doesn't take it seriously, isn't comfortable saddling themselves to a one-trick-horse offering from NVidia. This ridiculously powerful box, and it's cool software with cool visibility into a neat problem, but it's really a pipeline play, to get you into NVidia's world. For some, going full in on NVidia is ok, but I don't think it's unlike going full in as a MS Developer or iOS developer- you're picking up, putting on the blinders, and all you'll be able to do is sprint towards a fixed, not too far away point.
The company that doesn't support open programming hope to support open programming !
I'm skeptical.
Ceci n'est pas une Signature !
If nvidia hopes to sell me anything, they better start by not voluntarily crippling their own software the moment it detect some competitor hardware.
If I want to put both an AMD and an NVidia graphic card in my computer, I should be able to use the NVidia card to its full extent. Instead, they disable some of their proprietary technology in this situation (namely PhysX), with the official answer being "you can't have both running at the same time" and then never answering back.
Guess what? Yes I can have PhysX running on the NVidia GPU while plugging my display on the AMD one. A small unofficial patch makes this work perfectly fine. And even if that wasn't perfectly fine, a disclaimer saying that this configuration is not supported would do no harm.
With this in mind, I'm not going to trust anything they say or buy anything they were seriously involved with. They want people like me to buy their hardware? They should stop screwing their current customers first.
Neural networks do benefit from parallelism, and i'm sure GPUs will help them run a bit faster, but it's not enough...
I'm convinced by Simon Knowle's analysis of learning and inference compute patterns: which if you accept - focusing on making NNs run faster on CPUs and GPUs appears to be an approach with severely limited potential. This isn't about some basic hardware optimisations gained by turning something into an ASIC, it's because design features of CPUs and GPUs actively work against NN compute patterns.
These issues are explained well in his keynote at 18:30, he goes on to explain how inference compute patterns (quite literally in the case of computer vision) want to do the complete opposite of what a GPU is designed to do in a deterministic way. Here is a list of the pattern characteristics:
Thanks a lot for bringing us THAT MUCH CLOSER to the singularity!
but is it a graphical GUI interface?
.. anyone like to share a simple out-of-the-box neural network trainer/demonstrator to play around with neural nets on a novice-to-intermediate level?
Nvidia uses the concept of neural networks to promote its oncoming Pascal GPU : the new features are massive internal and external bandwith (the attached stacked memory is a big deal, and there's an interconnect ; well, I suppose internal buses are wider/faster) and FP16 (half precision floats) carried over from mobile GPU.
The half floats are meant to save bandwith and power.
So nvidia goes and say, well we can use that for NN. It's kind of marketing spin but at least using a GPU is cheap (next to designing your own hardware or say, using a mainframe)
I'm glad the "Masses" now have $15,000 to spend on projects like this.
"The DIGITS DevBox Access Program is available for purchase to qualified deep learning researchers in the United States and will be priced at $15,000. Lead time is 8-10 weeks from payment confirmation."
http://info.nvidianews.com/early_access_nvidia_3_15.html