11:11 Have a great GTC everyone.
11:11 Jensen wraps up. Titan X, the world’s fastest GPU, DIGITIS DevBox GPU deep learning platform, Pascal – 10x Maxwell for deep learning, Drive PX deep learning platform for self-driving cars.
11:04 Jensen – I get excited every time I get an [Tesla] OTA
10:59 10-50 MPH in urban environments is the most challenging part of autonomous driving, but we know how to solve it and will be there in a few years
10:58 Elon – Tegra will be a real enabler for autonomous driving
10:55 2B cars on the road, about 100M manufactured each year, 20Y replacement cycle, we won’t have 100% self-driving or 100% electric cars for a long time
10:52 Self driving cars will be like elevators (elevators used to have human operators)
10:50 Applause welcomes Elon Musk on stage
10:49 Drive PX DevKit, $10,000, May 2015
10:47: AlexNet on Drive PX, 184 frames per second
10:34 Jensen shifts discussion to ADAS, I think his special desk must be coming on stage soon
10:31 Key Pascal Features
10:28: Coming next year, Pascal, mixed precision, 3D memory, NVLink, 10X faster than Maxwell for deep learning, *very rough estimates
10:21 DIGITS DEVBOX
10:19 DIGITS Deep GPU Training System for Data Scientists. Process data, configure DNN, monitor progress, visualize layers.
10:15 Walking through some automated image captioning results. Thanks to all those Amazon Mechanical Turks who helped caption the original training data and to Julie B. for countless hours of reviewing results to pick out the most interesting examples
10:12 Andrej Karpathy describes how ConvNets and Recurrent Nets can be joined together for automated image captioning.
10:07 Deep learning revolutionizing medical research, predicting toxicity of new drugs, understanding gene mutation to prevent disease, detecting mitosis in breast cancer cells, groundbreaking work
10:06 Very glad to see EyeEm listed as one of the start-ups doing GPU-accelerated deep learning.
10:04 Volumetric rendering being done by Nvidia IndeX
9:58 Mike Houston joins Jensen to help explain how deep neural networks, specifically AlexNet, are processed on a GPU. No rabbits were harmed in the processing (have to watch the video).
Titan X with cuDNN2
9:40: #gtc15 on Twitter. You don’t have to be here to know that the press tables are full
9:39: Titan X, $999. Clapping.
9:36 Titan X for Deep Learning, training AlexNet, 43 days on 16-core Xeon, 2.5 days on Titan X + cuDNN.
9:31 Epic Games trailer – kite, need to watch the video, words don’t capture the beauty, 100 square miles (about the size of Silicon Valley), running in realtime on a single Titan X
9:30 Titax X. World’s fastest GPU. 8B transistors, 3072 CUDA cores, 7 TFLOPS SP, 12GB memory, based on Maxwell
9:28 Rolling Titan X video
9:27 Part of our promise is access to the platform. By putting CUDA in every GPU we make it easy for every developer in the world to access
9:25 Our promise is to accelerate your code, striking a balance between ease of programming and speed.
9:24 54,000 GPU teraflops around the world today, 3 million CUDA downloads, 60,000 academic papers
9:15 4 things to talk about 1) A new GPU and deep learning, 2) A very fast box and deep learning 3) Roadmap reveal and deep learning 4) Self driving cars and deep learning
9:13 Jen-Hsun comes on stage. GTC is about developers, about sharing ideas, about being inspired
9:11 Opening video starts, “do you remember the future … its here”. Going to be a lot of discussion of deep learning.
9:09 Tune into the Livesteam to watch.
9:08 Packed house for the #gtc15 keynote. Announcer asking everyone to take their seats