Google Photos neural network learns from labeled examples to recognize images
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Neural networks are an important machine learning technology that Google uses to power many of its products. Neural networks learn from large numbers of labeled examples, making tiny adjustments to their models to better match outputs to labels. After training, neural networks can be tested on unlabeled data to produce useful outputs. Google uses neural networks across many products from Search to Translate to Photos. Their use of neural networks is rapidly accelerating to improve these products and tackle new challenges in health, robotics, and more. Google also shares machine learning tools and models to help advance the field.
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Google Photos neural network learns from labeled examples to recognize images
- 1. Machine Intelligence at Google Rob Craft, CloudIntel Lead robcraft@google.com @KindaRobCraft
- 2. 2 Google’s mission is to organize the world’s information and make it universally accessible and useful.
- 3. 3 one important technology we use is neural networks OUTPUTINPUT
- 4. 4 neural net models learn from examples labeled photos “cat” “dog” “car” “apple” “flower” OUTPUT
- 5. 5 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
- 6. 6 after a model is trained, you can test it ? unlabeled photo
- 7. 7 after a model is trained, you can test it unlabeled photo “cat”
- 8. 8 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
- 9. 9 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
- 11. 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
- 12. Breakout
- 14. 14 Street nameStreet number Street View Sign Business facade Sign Business name Traffic light Traffic sign Street number
- 17. 17 Smart reply in Inbox by Gmail 10% of all responses sent on mobile
- 18. 18 signal for Search ranking, out of hundreds improvement to ranking quality in 2+ years #3 #1 Search machine learning for search engines RankBrain: a deep neural network for search ranking
- 19. 19 beyond core products, into areas like health and robotics “Deep Learning for Robots: Learning from Large-Scale Interaction”, Google Research Blog, posted March, 2016
- 20. Sharing our tools with researchers and developers around the world repository for “machine learning” category on GitHub #1 Released in Nov. 2015
- 21. 2121 Machine Learning Use Cases Structured 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
- 22. 2222 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 Alpha Beta GA
- 23. 2323 beyond core products for applications like health and robotics smart platforms to accelerate progress worldwide Google products to create great experiences for users machine intelligence is already making a huge difference and there are many, many more opportunities 23