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. - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

1. How Deep Learning Will Make Us More Human Again
Arno Candel, PhD
CTO, H2O.ai
@ArnoCandel
AI Frontiers, Santa Clara
Jan 12, 2017

2. Thanks to the Amazing H2O Team!
We are
hiring!

3. H2O.ai
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 (github.com/h2oai)
H2O is Easy to Use and Deploy
• h2o.ai/download 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
H2O.ai - Makers of H2O

4. https://www.cbinsights.com
H2O.ai - At the Core of AI

5. H2O.ai - Loved By The Best

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. H2O.ai
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. H2O.ai
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. H2O.ai
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. H2O.ai
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. H2O.ai
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. H2O.ai
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. H2O.ai
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. 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. Open-Source - Leverage Community Code, Data and Models
World’s Best Image Classifier (Google + Microsoft, Aug 2016)
https://research.googleblog.com/2016/08/improving-inception-and-image.html
open-source implementation
H2O takes mxnet graph definition as input

16. Build your own models with Deep Water Today!
https://github.com/h2oai/h2o-3/blob/master/h2o-py/tests/testdir_algos/deepwater/pyunit_inception_resnet_v2_deepwater.py
Deep Water - Easiest To Use GPU Deep Learning Ever!

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. Challenges With AI and Deep Learning

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. H2O.ai Stanford Advisors
stankrd pic
Sri/CEO Boyd Hastie Tibshirani
last week

21. Which Open-Source AI Platform to Use?

22. Which Programming Language To Use?
Which one for Development vs Production?

23. Which Hardware To Use?
Which one for Development vs Production?
Analog/Neuromorphic

24. Who Does the Work and on What Infrastructure?
Which one for Development vs Production?
Cloud? Which? On Premise?
Data Lake?
Micro-Services?

25. Which one for Development vs Production?
When is the Model Good Enough?
Crowd sourcing? Trust a Genius? Internal Bake-Off?

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. Gradient Boosting
Machine
Generalized
Linear Modeling
Deep Learning
Distributed
Random Forest
Do you need AI, Deep Learning or just a Simple Model?

28. Future Of AI: Or What’s Left for Humans to Do?
Charlie Chaplin - Modern Times 1936

29. Future Of AI: Or What’s Left for H2O.ai to Do?
H2O.ai is busy working on the next best thing!