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Strata San Jose 2016: Deep Learning is eating your lunch -- and mine


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In recent years, deep learning has taken the lead in predictive accuracy in many fields of machine learning, and companies are struggling to keep up with the speed of innovation. Arno Candel demonstrates how successful enterprises can augment simple statistical models with more accurate data-driven models to gain a competitive edge.

Arno describes how to build smart applications that include data munging, model training and validation, and real-time production deployment—every step is based on open source code (R, Python, Java, Scala, JavaScript, REST) that runs on distributed platforms including Hadoop, Spark, and standard compute clusters. Arno also presents use cases from verticals including insurance, fraud, churn, fintech, and marketing and offers live demos of smart applications on large real-world datasets in distributed clusters.

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Published in: Software

Strata San Jose 2016: Deep Learning is eating your lunch -- and mine

  1. 1.
 Machine Intelligence Afraid of the future?
 You should be. Deep Learning is
 eating your lunch — and mine. San Jose, March 29 2016 Arno Candel, PhD
 Chief Architect
  2. 2.
 Machine Intelligence Who Am I? Arno Candel Chief Architect, Physicist & Hacker at PhD Physics, ETH Zurich 2005 10+ yrs Supercomputing (HPC) 6 yrs at SLAC (Stanford Linear Accelerator) 4+ yrs Machine Learning 2+ yrs at Fortune Magazine Big Data All Star Follow me @ArnoCandel 2 Who am I?
  3. 3.
 Machine Intelligence • What is Deep Learning? • What can it do? • What will it change? • What can you do? 3 Outline
  4. 4.
 Machine Intelligence 4 What is Deep Learning? Machine Learning (ML) Artificial Intelligence (A.I.) Computer Science (CS) Deep Learning (DL)
  5. 5.
 Machine Intelligence 5 What is Deep Learning? That’s the stuff that powers pretty much all of Google. Deep Learning (DL)
  6. 6.
 Machine Intelligence 6 What is Deep Learning? Or, more technically, it’s a computer algorithm that models high-level abstractions in data with multiple layers of non-linear transformations. Deep Learning (DL)
  7. 7.
 Machine Intelligence 7 Example DL Model: Multi-Layer (Deep) Artificial Neural Network income loan amount credit score borrower will
 not default on loan borrower will
 default on loan IN: data OUT: prediction nodes : neuron activations (real numbers) — represent features arrows : connecting weights (real numbers) — learned during training : non-linearity x -> f(x) — adds models complexity mid 1970s
  8. 8.
 Machine Intelligence 8 Deep Learning Pros and Cons • conceptually simple • non linear • highly flexible and configurable • learned features can be extracted • can be fine-tuned with more data • efficient for multi-class problems • world-class at pattern recognition Pros: • hard to interpret • theory not well understood • slow to train and score • overfits, needs regularization • many hyper-parameters • inefficient for categorical variables • very data hungry, learns slowly Cons: Deep Learning got boosted recently by faster computers
  9. 9.
 Machine Intelligence 9 Brief History of A.I., ML and DL John McCarthy
 Princeton, Bell Labs, Dartmouth, later: MIT, Stanford 1955: “A proposal for the Dartmouth summer research project on Artificial Intelligence” with Marvin Minsky (MIT), Claude Shannon 
 (Bell Labs) and Nathaniel Rochester (IBM) A step back: A.I. was coined over 60 years ago
  10. 10.
 Machine Intelligence 10 1955 proposal for the Dartmouth summer research project on A.I. “We propose that a 2-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning and any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for one summer.”
  11. 11.
 Machine Intelligence 11 It took a little longer… McCarthy invents LISP in 1958, influences ALGOL, popularizes timesharing, wins Turing Award, Kyoto Price, National Medal of Science, etc. A.I. takes time
  12. 12.
 Machine Intelligence 12 Almost there! Or not? A few thousand years ago Today: still plowing along latest-news/news-events/industry-news/ robot-helps-farmers-monitor-fertiliser-water- usage We can automate many tasks, general A.I. is still far away.
  13. 13.
 Machine Intelligence 13 Step 1: Great Algorithms + Fast Computers Raw computing power can automate complex tasks! Earlier he said: “No computer will ever beat me.” 1997: Playing Chess (IBM Deep Blue beats Kasparov) Computer Science
 30 custom CPUs, 60 billion moves in 3 mins
  14. 14.
 Machine Intelligence 14 Step 2: More Data + Real-Time Processing 2005: Self-driving Cars
 DARPA Grand Challenge, 132 miles (won by Stanford A.I. lab*) Sensors & Computer Science
 video, radar, laser, GPS, 7 Pentium computers Automating automobiles into autonomous automata! *Established by McCarthy et al. in the early 60s
  15. 15.
 Machine Intelligence 15 Step 3: Big Data + In-Memory Clusters 2011: Jeopardy (IBM Watson) In-Memory Analytics/ML 4 TB of data (incl. wikipedia), 90 servers,
 16 TB RAM, Hadoop, 6 million logic rules Automating question answering and information retrieval! Note: IBM Watson received the question in electronic written form, and was often able to (electronically) press the answer button faster than the competing humans.
  16. 16.
 Machine Intelligence 16 Step 4: Deep Learning 2014: Atari Games (DeepMind) 2016: AlphaGo (Google DeepMind) Deep Learning
 + reinforcement learning, tree search,
 Monte Carlo, GPUs, playing against itself, … Deep Learning + Smart Algorithms = Master Gamer Go board has approx. 200,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000, 000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000 (2E170) possible positions. trained from raw pixel values, no human rules
  17. 17.
 Machine Intelligence 17 Step 5: Improve Training Efficiency 2015: MIT, NYU, Toronto Bayesian Program Learning (NOT Deep Learning) New algorithm learns handwriting of unseen symbols from very few training examples (unlike typical Deep Learning) intelligence-rivals-human-vision-abilities.html
  18. 18.
 Machine Intelligence 18 Microsoft A.I. goes rogue TayandYou/with_replies P.S. Microsoft won the Visual Recognition challenge:
  19. 19.
 Machine Intelligence 19 What ELSE can Deep Learning do? Deep Learning can generate handwriting
  20. 20.
 Machine Intelligence 20 What ELSE can Deep Learning do? Image captioning: “black and white dog jumps over bar.” Deep Learning can generate code, captions, language, etc. DL twitter bot Generated (bogus) math proof:
  21. 21.
 Machine Intelligence 21 Quest Visual (acquired by Google) What ELSE can Deep Learning do? Deep Learning can translate any language
  22. 22.
 Machine Intelligence 22 What ELSE can Deep Learning do? Deep Learning can create masterpieces: Semantic Style Transfer
  23. 23.
 Machine Intelligence 23 State of AI today Today’s A.I. can simulate human behavior, but
 only for the tasks it was programmed for. It’s just very good at some board games. And at driving cars. And at speaking any language. And at handwriting. And at much more, but it doesn’t generalize to arbitrary capabilities. It doesn’t pour itself a scotch (but it could). It doesn’t practice putting (but it could). It doesn’t fall in love (we think).
  24. 24.
 Machine Intelligence 24 What next? Anything that can be automated will be automated. Jobs of the past: assembly line work, teller (ATMs), taxi-firm receptionist Jobs being automated away now: resume matching, driving, language translation, education Jobs being automated away soon: healthcare, arts & crafts, entertainment, design, decoration, software engineering, politics, management, professional gaming, financial planning, auditing, real estate agent Jobs of the future: professional sports, food & wine reviewer
  25. 25.
 Machine Intelligence 25 Maybe we’ll finally get the eating machine? 1936 Modern Times (Charlie Chaplin)
  26. 26.
 Machine Intelligence 26 What will change? Jerry Kaplan, Stanford Future headlines “In a few decades a cheap computer may compute as much as all human brains combined — and everything is going to change; every aspect of civilization is going to be affected and transformed by that.” - Jürgen Schmidhuber (Deep Learning pioneer)
  27. 27.
 Machine Intelligence 27 Technology has always created jobs U.S. workforce in agriculture 1800: 90% 2000: <2% A.I. in software: Today: 1% of all software apps use A.I. 2018: 50% of developers will use A.I. in their software (IDC) “Intelligent software applications will become commonplace. And machine learning will touch every industry.” — Jeff Dean, Google Brain on-to-control-artificial-intelligence-and-techs-future.html
  28. 28.
 Machine Intelligence 28 - Makers of H2O H2O - Software Product • Apache V2 open source ( • Scalable Data Science Platform (AI Engine for Business Transformation) • Distributed Columnar Data Frame and Map/Reduce Backend • Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling, K-Means, PCA, GLRM, … Easy to Train and Operationalize Models • and run anywhere, immediately • R, Python, Java, Scala, REST, GUI client APIs • Spark (cf. Sparkling Water), Hadoop, Standalone • Auto-generated Java scoring code • Vibrant user community
  29. 29.
 Machine Intelligence Client APIs: R, Python, Java, Scala, Flow 29 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 web GUI and Notebook
  30. 30.
 Machine Intelligence 30 Gradient Boosting Tree Model Auto-generated
 Java scoring code to Operationalize Data Science Easily Bring H2O Models into Production READ MORE
  31. 31.
 Machine Intelligence Hear what our customers are saying about H2O… 31
  32. 32.
 Machine Intelligence What Progressive Says About H2O 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.” 32User Based Insurance
  33. 33.
 Machine Intelligence What MarketShare Says About H2O “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 33Digital Marketing
  34. 34.
 Machine Intelligence WATCH NOW WATCH NOW What Nielsen Catalina Says About H2O “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.” 34Consumer Behavior Analytics
  35. 35.
 Machine Intelligence WATCH NOW WATCH NOW What Zurich Says About H2O “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.” 35Risk Analytics
  36. 36.
 Machine Intelligence What Capital One Says About H2O “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 36Financial Services
  37. 37.
 Machine Intelligence 37 H2O Deep Learning Community Quotes READ MORE Kaggle challenge 2nd place winner
 Colin Priest READ MORE “For my final competition submission I used an ensemble of models, including 3 deep learning models built with R and h2o.” “I did really like H2O’s deep learning implementation in R, though - the interface was great, the back end extremely easy to understand, and it was scalable and flexible. Definitely a tool I’ll be going back to.”
  38. 38.
 Machine Intelligence 38 READ MORE WATCH NOW “H2O Deep Learning models outperform other Gleason predicting models.” READ MORE “… combine ADAM and Apache Spark with H2O’s deep learning capabilities to predict an individual’s population group based on his or her genomic data. Our results demonstrate that we can predict these very well, with more than 99% accuracy.” H2O Deep Learning Community Quotes
  39. 39.
 Machine Intelligence 39 Become a Top 1%-er Learn the tricks of the trade (changing fast)!
  40. 40.
 Machine Intelligence 40 Top 13 Tips & Tricks for H2O Deep Learning Know your Math Use Early Stopping Use Checkpointing Use Regularization Use N-fold Cross-Validation Perform HyperParameter Search Know your Data (Sparsity/Categoricals) Tune Communication on Multi-Node Establish Baseline on Holdout DataUnderstand Model Complexity Control Scoring Overhead Do Ensembling Inspect Models in Flow READ MORE WATCH NOW
  41. 41.
 Machine Intelligence 41 Higher Accuracy with Better Algorithms Feature Engineering, Cross-Validation, Hyper-Parameters, Ensembles <— 2 talks today by Erin LeDell!
  42. 42.
 Machine Intelligence 42 H2O Booklets DOWNLOAD Get your booklets at the booth! R Python Deep Learning GLMGBMSparkling Water
  43. 43. H2O OPEN TOUR 
 W W W . O P E N . H 2 O . A I #GOH2O
  44. 44.
 Machine Intelligence Deep Learning is indeed eating
 your lunch — and mine. Keep up by making your own smarter applications and operationalizing your data science with H2O! 44 H2O Google Group @h2oai Key Takeaways Visit our booth #1225 for live demos!