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How Deep Learning Will Make Us More Human Again

While deep learning is taking over the AI space, most of us are struggling to keep up with the pace of innovation. Arno Candel shares success stories and challenges in training and deploying state-of-the-art machine learning models on real-world datasets. He will also share his insights into what the future of machine learning and deep learning might look like, and how to best prepare for it.

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  1. 1. How Deep Learning Will Make Us More Human Again Arno Candel, PhD CTO, @ArnoCandel
 AI Frontiers, Santa Clara Jan 12, 2017
  2. 2. Thanks to the Amazing H2O Team! We are hiring!
  3. 3.
 Machine Intelligence 3 Software Product: H2O - AI for Business Transformation • Scalable and Distributed Data Science and Machine Learning:
 Deep Learning, Gradient Boosting, Random Forest,
 Generalized Linear Modeling, K-Means Clustering, PCA, GLRM, … • Apache v2 Open Source ( 
 H2O is Easy to Use and Deploy • and run anywhere, immediately • Client APIs: R, Python, Java, Scala, REST, Flow GUI • Spark (cf. Sparkling Water), Hadoop, Standalone • Auto-generated Java/C++ Scoring Code - Makers of H2O
  4. 4. - At the Core of AI
  5. 5. - Loved By The Best
  6. 6. Powerful, Scalable Techniques for Deep Learning and AI Win your copy at our booth! Dec 2016 - brand new! H2O Book - Written by the Community
  7. 7.
 Machine Intelligence User Based Insurance WATCH NOW WATCH NOW “H2O is an enabler in how people are thinking about data.” “We have many plans to use H2O across the different business units.” 7
  8. 8.
 Machine Intelligence Digital Marketing - Campaigns “H2O gave us the capability to do Big Modeling. There is no limit to scaling in H2O.” “Working with the H2O team has been amazing.” “The business value that we have gained from advanced analytics is enormous.” WATCH NOW WATCH NOW 8
  9. 9.
 Machine Intelligence WATCH NOW WATCH NOW Matching TV Watching Behavior with Buying Behavior “Unlike other systems where I had to buy the whole package and just use 10-20%, I can customize H2O to suit my needs.” “I am a big fan of open source. H2O is the best fit in terms of cost as well as ease of use and scalability and usability.” 9
  10. 10.
 Machine Intelligence WATCH NOW WATCH NOW Insurance - Risk Assessment “Predictive analytics is the differentiator for insurance companies going forward in the next couple of decades.” “Advanced analytics was one of the key investments that we decided to make.” 10
  11. 11.
 Machine Intelligence Fintech - Fraud/Risk/Churn/etc. “H2O is a great solution because it's designed to be enterprise ready and can operate on very large datasets.” ”H2O has been a one-stop shop that helps us do all our modeling in one framework.” ”H2O is the best solution to be able to iterate very quickly on large datasets and produce meaningful models.” WATCH NOW WATCH NOW 11
  12. 12.
 Machine Intelligence 12 High Level Architecture of H2O HDFS S3 NFS Distributed In-Memory Parallel Parser Lossless Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model
 Evaluation & Selection Predict Data & Model
 Storage Model Export:
 Standalone Scoring Code C/C++/Java
 R/Py/etc. Data Prep Export: Plain Old Java Object Local SQL LDAP Kerberos SSL HTTPS HTTP
  13. 13.
 Machine Intelligence Native APIs: Java, Scala — REST APIs: R, Python, Flow, JavaScript, Java 13 library(h2o) h2o.init() h2o.deeplearning(x=1:4,y=5,as.h2o(iris)) import h2o from h2o.estimators.deeplearning import H2ODeepLearningEstimator h2o.init() dl = H2ODeepLearningEstimator() dl.train(x=list(range(1,4)), y="Species", training_frame=iris.hex) import _root_.hex.deeplearning.DeepLearning import _root_.hex.deeplearning.DeepLearningParameters val dlParams = new DeepLearningParameters() dlParams._train = iris.hex dlParams._response_column = ‘Species val dl = new DeepLearning(dlParams) val dlModel = dl.trainModel.get All heavy lifting is done by the backend! Built-in interactive GUI and notebook - no coding needed!
  14. 14. Deep Water Brings State-Of-The-Art Deep Learning on GPUs to H2O H2O Deep Learning: simple multi-layer networks, CPUs H2O Deep Water:
 arbitrary networks, CPUs or GPUs Limited to business analytics, statistical models (CSV data) Large networks for big data (e.g. image 1000x1000x3 -> 3m inputs per observation) 1-5 layers MBs/GBs of data 1-1000 layers GBs/TBs of data
  15. 15. Open-Source - Leverage Community Code, Data and Models World’s Best Image Classifier (Google + Microsoft, Aug 2016) open-source implementation H2O takes mxnet graph definition as input
  16. 16. Build your own models with Deep Water Today! Deep Water - Easiest To Use GPU Deep Learning Ever!
  17. 17. Yesterday: Small Data (<GB) Today: Big Data (TeraBytes, ExaBytes) Data + Skills
 are good for business Data + Machine Learning ARE the business Things are Changing Quickly
  18. 18. Challenges With AI and Deep Learning
  19. 19. CEO: “We will transform our business with AI” Management: “Hire someone to give us AI” Senior Data Scientist: “I should look into AI” Junior Data Scientist: “I use TensorFlow all the time” High School Kid: “I did my internship on Deep Learning” Average Joe: “I want a self-driving car (and keep my job)” Stanford Professors: “focus on interpretability, start with simple models!” The Hype and Reality of AI
  20. 20. Stanford Advisors stankrd pic Sri/CEO Boyd Hastie Tibshirani last week
  21. 21. Which Open-Source AI Platform to Use?
  22. 22. Which Programming Language To Use? Which one for Development vs Production?
  23. 23. Which Hardware To Use? Which one for Development vs Production? Analog/Neuromorphic
  24. 24. Who Does the Work and on What Infrastructure? Which one for Development vs Production? Cloud? Which? On Premise? Data Lake? Micro-Services?
  25. 25. Which one for Development vs Production? When is the Model Good Enough? Crowd sourcing? Trust a Genius? Internal Bake-Off?
  26. 26. What problem are you solving in the first place? What problem should/could you be solving instead? What can you learn from the model? How can you improve the models? More, better data? How can you characterize the model? Do you need AI, Deep Learning or just a simple model? Back to the Drawing Board!
  27. 27. Gradient Boosting
 Machine Generalized
 Linear Modeling Deep Learning Distributed
 Random Forest Do you need AI, Deep Learning or just a Simple Model?
  28. 28. Future Of AI: Or What’s Left for Humans to Do? Charlie Chaplin - Modern Times 1936
  29. 29. Future Of AI: Or What’s Left for to Do? is busy working on the next best thing!