Re:TensorFlow machine learning now optimized for the Snapdra...
by tsomctl on Thursday January 12, 2017 @02:47AM
(#13380546)
Maybe they are unused because the implementation was poor. And the implementation was poor because a Pentium 2 processor is shit and deep learning has only been practical in the last couple of years.
More specifically, I had a Palm Pilot, and you had to write using weird letter shapes for it to work.
Re:TensorFlow machine learning now optimized for the Snapdra...
by lightedman on Thursday January 12, 2017 @11:06AM
(#13383312)
The implementation worked great. You walked up to your computer, turned it on, and looked at the camera. Bam! You were logged in, assuming you had enough proper light on your face for the software to make out your facial shape.
It was just bloody annoying because you had to wait about 15 seconds for it to figure everything out. It was far faster to just use the keyboard.
Re:TensorFlow machine learning now optimized for the Snapdra...
by dharma1 on Thursday January 12, 2017 @02:51AM
(#13380562)
High accuracy voice recognition done fully on the mobile client, instead of on the cloud, would be pretty big, and useful. I think we'll get there in 1-2 years
Re:TensorFlow machine learning now optimized for the Snapdra...
by lightedman on Thursday January 12, 2017 @11:05AM
(#13383296)
We had that with Dragon Naturally Speaking and had it working on Pentium-4 Class hardware.
Re:TensorFlow machine learning now optimized for the Snapdra...
by dharma1 on Thursday January 12, 2017 @01:40PM
(#13384880)
HMM based speech recognition works ok locally today (and has worked for a while as you say) but there is a large difference in word error rate, handling noisy environments etc when compared to SOTA recurrent neural network models. Those don't (yet) run realtime locally on mobile hardware, but we are not far off.
The difference between 8x% and 97% accurate speech recognition in terms of user experience is pretty drastic
Re:TensorFlow machine learning now optimized for the Snapdra...
by cwyers on Thursday January 12, 2017 @01:39PM
(#13384866)
I remember Dragon Naturally Speaking. You had to do a lot of training to your specific voice to get it to a level of accuracy I would describe as "not great." You'd have to slow down your speaking to get anything I'd consider acceptable output out of it. Modern systems are much better.
Re:TensorFlow machine learning now optimized for the Snapdra...
by orf on Thursday January 12, 2017 @03:57AM
(#13380849)
> Windows 98 on a 180MHz Evergreen Overclocked processor and 48MB RAM did that just fine.
No it didn't
Re:TensorFlow machine learning now optimized for the Snapdra...
by pricechild on Thursday January 12, 2017 @04:44AM
(#13381049)
http://www.prnewswire.com/news-releases/visionics-introduces...
Systems Requirements for FaceIt PC
-- Microsoft Windows 95.
-- 90 MHz Intel Pentium compatible or higher.
-- 16 MB RAM/10 MB free hard disc space.
-- VGA or higher video display adapter.
-- CD-ROM drive.
-- Microsoft Video for Windows (VFW) compatible video capture system with
resolution 320 x 240, depth: 15 bit RGB, capture rate to memory:
5 frames/sec.
Re:TensorFlow machine learning now optimized for the Snapdra...
by unlikelymordant on Thursday January 12, 2017 @04:55AM
(#13381106)
there is a big difference in recognition accuracy between that and current state of the art systems. This isnt just a bit of incremental improvement. Todays systems are way better, but they also require a bit more processing.
Re:TensorFlow machine learning now optimized for the Snapdra...
by orf on Thursday January 12, 2017 @04:13PM
(#13386167)
Ahh well how can we forget FaceIt: the cutting edge of facial recognition. 20 years behind the curve but at least it can run on Windows 95, so that proves your point? Damn these kids with their bloated software, we got terrible results using rubbish software with 16mb of ram processing at 5 FPS and so should they!
/sarcasm
I think nobody's bothered to code that stuff in, because, well, despite trying time and time again to make these hot-shit features for a couple of decades, these features end up unused. Those that pay attention to history see this, and figure "Probably not worth trying, even in this day and age."