diving into machine learning with rob craft, group product manager at google!
TRANSCRIPT
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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
OUTPUTINPUT
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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 examplesMake 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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AndroidAppsGmailMapsPhotosSpeechSearchTranslationYouTubeand 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
Atari 2600 testbed: 100+ Atari games from the 70/80s
Agents just get raw pixels as inputs (~30K)
Wired up to action buttons but NOT told what they do
Goal is simply to maximize score
Everything learnt from scratch, ZERO pre-programmed knowledge
ONE agent with ONE set of parameters to play ALL the different games
THINK OF A ROBOT in the arcade watching the screen controlling the joystick
DQN Deep Q-network- Atari testbed
Image: Gnome Enterprises
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Street nameStreet number
Street View
Sign
Business facade
SignBusiness name
Traffic light
Traffic signStreet number
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[glacier]
Google Photos
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Google Translate
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Smart reply in Inbox by Gmail
10%of all responses sent on mobile
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signalfor Search ranking,
out of hundreds
improvementto ranking quality
in 2+ years
#3 #1
Search
machine learning for search engines
RankBrain: a deep neural network for search ranking
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beyond core products, into areas like health and robotics
“Deep Learning for Robots: Learning from Large-Scale Interaction”, Google Research Blog, posted March, 2016
Sharing our tools with researchers and developers around the world
repositoryfor “machine
learning”category on GitHub
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Released in Nov.
2015
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Machine Learning Use CasesStructured Data
Classification/ Regression● Customer Churn Analysis● Product Diagnostics● Forecasting
Recommendation● Content Personalization● Product X-Sells/Up-sells
Anomaly Detection● Fraud Detection● Asset Sensor Diagnostics● Log Metric Anomalies
Unstructured Data
Image Analytics● Identify damaged shipments● Explicit Content Classification● Identify “styles” in images
Text Analytics● Call Center log analysis● Language Identification● Topic Classification● Sentiment Analysis
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Ready to use Machine Learning models
Use your own data to train models
Cloud Vision API
Cloud Speech API
Cloud Translate API
Cloud Machine Learning
Develop - Model - Test
Google BigQuery
Stay Tuned….
Cloud Storage
Cloud Datalab
NEW
Alpha
GA BetaGA
AlphaBeta
GA
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beyond core productsfor applications like health and robotics
smart platformsto accelerate
progress worldwide
Google products to create great
experiences for users
machine intelligence is already making a huge differenceand there are many, many more opportunities
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Questions?(thanks)
@KindaRobCraft