ta cr day - smart life (frantisek srupl, google)
TRANSCRIPT
Smart Life 2016
František Štrupl, Google
20.10.2016
01001000 01100101 01101100 01101100 01101111
Hello 01001000 01100101 01101100 01101100 01101111
The World Hello is about :
5 Bytes of DATA
Every day, we produce
2 500 000 000 000 000 000 bytes of DATA * (trillion)
* IBM - What is Big Data
This would make
Four Millions of Millions of Books
(4 000 000 000 000 Books)
All of these books stacked would represent
2x Distance Between Earth & Moon
(384,400 km / 238,800 mi)
What has happened
since I’ve said “Hello” ?
In the last 60 seconds ...
694 rides
Source : Uber
In the last 60 seconds ...
400h of Videos uploaded
Source : Google
In the last 60 seconds ...
2.5 Millions Of requests
Source : Google
In the last 60 seconds ...
77k hours of videos streamed
Source : Netflix
In the last 60 seconds ...
75k Transactions made Online
Source : Statistita
In the last 60 seconds ...
591’000 swaps
Source : Tinder
Confidential & Proprietary
FAST, UNPREDICTABLE CHANGE is an absolute certainty
Confidential & Proprietary
ENTERING A NEW WORLD Of Six Screens (yes, six!)
e.g. Samsung Gear, Google
Glass
[WEARABLE] [MOBILE]
e.g. Project Ara
[TABLET]
e.g. Senseg
[DESKTOP]
e.g. Flutter, Leap
Motions, Thalmic Labs
[IN-CAR]
e.g. HUD Windscreens
[TV]
e.g. Chromecast
Confidential & Proprietary Source: Digital Star Popularity Grows Versus Mainstream Celebrities by Susanne Alt, July 23, 2015
Confidential + Proprietary
THE NEXT 5BN
[100% of Global Population]
Internet Population
Global Population
2014
[39% of Global Population]
2.8 BN
7.2 BN
2020
8 BN
* 8 BN
8 BN
Source: (1) eMarketer, Nov 2013; (2) Eric Schmidt estimate*
Artificial Intelligence / Machine Learning
21
Google’s mission is to organize the world’s information and make it universally accessible and useful.
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one important technology we use is neural networks
OUTPUT INPUT
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neural net models learn from examples
labeled photos
“cat”
“dog”
“car”
“apple”
“flower”
OUTPUT
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neural net models learn from examples Make tiny adjustments to model so output is closer to label for a given image
labeled photos
“cat”
“dog”
“car”
“apple”
“flower”
OUTPUT
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after a model is trained, you can test it
? unlabeled photo
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after a model is trained, you can test it
unlabeled photo
“cat”
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Input Output
“rice”
“restaurants in Seoul”
“hello!”
“A close up of a small child holding a stuffed animal.”
powerful functions that neural nets can learn
안녕하세요
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Android
Apps
Gmail
Maps
Photos
Speech
Search
Translation
YouTube
and many others ...
Used across products:
2012 2013 2014 2015
1500
1000
500
0
Number of directories containing model description files
rapidly accelerating use of deep learning at Google
DeepMind- AlphaGo
Demis Hassabis
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Street name Street number
Street View
Sign
Business facade
Sign Business name
Traffic light
Traffic sign Street number
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Google Photos
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Google Translate
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