Companies like Google, Microsoft, Amazon and Facebook are in fierce competition for teams that can build deep-learning applications. Because of deep learning's general usefulness in pattern recognition, those applications are surprisingly diverse, ranging from image recognition to machine translation. This talk will explore deep learning use cases for the major data types -- image, sound, text and time series -- as they're emerging in the private sector. Presented by Chris Nicholson, Co-Founder and CEO at Skymind.

1. DEEP LEARNING
USE CASES
SKYMIND - 2015
@deeplearning4j &
@skymindio

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3. DEEP LEARNING IS
MACHINE PERCEPTION FOR…
TEXT
• CRM
• SEARCH +
• ADS
SOUND
• VOICE SEARCH
• MUSIC GEN.
• TRANSLATION
TIME SERIES
• HEALTH DATA
• SENSORS
• FINANCE
IMAGES
• FACES
• SELF-DRIVING
VEHICLES

4. RECORD-BREAKING ACCURACY
• FACIAL RECOGNITION = 97% accuracy
• GENERAL IMAGE RECOG. = 93%
• SPEECH RECOGNITION = 81%
• VIDEO ACTIVITY RECOG. = 52% - 94%
(Varies by dataset)
• TEXT CLASSIFICATION = 94%

5. NEURAL NETWORKS ARE OLD
1943 – First electrical model of a neural network
1958 – Perceptron (Rosenblatt)
1986 – Backpropagation
1990s – Convolutional networks for images (LeCun)
2006 – Deep-belief networks (Hinton)
>> THEN SOMETHING HAPPENED <<
2013 – Google hires Geoff Hinton
Facebook hires Yann LeCun
2014 – Google buys DeepMind for $600 million
2015 – IBM partners with Yoshua Bengio

6. WHAT HAPPENED? WHY NOW?
DEEP LEARNING
WORKS FOR
ENTERPRISE
MORE
DATA
FASTER
HARDWARE
BETTER
ALGORITHMS

7. Difference
Amount of Data
Performance
Most Learning
Algorithms
Deep
Learning
BIG DATA & DEEP LEARNING

8. "The biggest disruptor that we are sure about is
the arrival of big data and machine
intelligence. This disruption will not only change
every business globally, it will also have an
important impact on the consumer."
Google Chairman Eric Schmidt
THE BIGGEST DISRUPTOR

9. VIRTUOUS CIRCLE OF AI
attracts
generate
improves

10. • “A breakthrough in machine learning would be
worth 10 Microsofts.” - Bill Gates, Microsoft
• “Machine learning is the next Internet.”
- Tony Tether, DARPA Director
• “Web rankings today are mostly a matter of
machine learning.” - Prabhakar Raghavan,
Yahoo Director of Research
• “Machine learning is going to result in a real
revolution.”
- Greg Papadopoulos, Sun CTO
• “Machine learning is todayʼs discontinuity.”
- Jerry Yang, Yahoo
MACHINE LEARNING’S
BREAKTHROUGH IS DEEP…

11. SPEECH IMAGE TEXT SENSORS

12. DEEP LEARNING
WILL TRANSFORM
THE INTERNET

13. AND THE INTERNET OF THINGS

14. VOICE AND IMAGE SEARCH
REQUIRE VOICE AND IMAGE
RECOGNITION

15. IMAGE SEARCH // IMAGE QUERIES

16. IMAGE SEARCH + E-COMMERCE

17. VOICE SEARCH
CRUCIAL IN DEVELOPING NATIONS

18. VOICE + PERSONAL ASSISTANT
CURATED SERVICES
(LIKE GOOGLE NOW)

19. MORE AND MORE MEDIA
WILL BE DELIVERED
IN VIRTUAL CONVERSATION

20. LESS AND LESS INFORMATION
WILL PASS THROUGH WEBSITES

21. MACHINE VISION IS COMING
SAFER CARS, FEWER DRIVERS

22. NETWORKED CARS
COORDINATED TRAFFIC

23. CAR <> ENVIRONMENT
RIDER <> CAR

24. AUTOMOTIVE USE CASES
Car <> Environment (Keep Things Safe And Moving)
• Machine Vision (Visual, Radar, Lidar)
• Geolocation
• Traffic Data -> Rerouting
• Service Location
Rider <> Car (Monitoring Rider State, Meeting Needs)
• Voice Recognition
• Facial Expression Recognition
• Transaction History
• Recommendation Engine // Search

25. COMMERCIAL DRONES

26. WHAT WILL YOU COUNT TODAY?
SURVEYS
• Land use
• Urban development
• Property lines
• Crop growth
SURVEILLANCE
• Large-scale human movements (migrants, commuters)
• Campus and on-site security
• National Parks (poaching)
PAYLOADS
• Goods delivered
• Rescue ops in mountains, at sea

27. SMART HOMES

28. SURVEILLANCE
& SECURITY

29. MEDICAL
IMAGERY
& DIAGNOSTICS
$[possible*tumor]

30. SOUND
MACHINE TRANSCRIPTION

31. SOUND
MACHINE TRANSLATION

32. SOUND
MUSIC GENERATION &
RECOMMENDATION

33. TEXT
SPAM
FILTERING
DISCOVERY

34. TEXT : CUSTOMER RELATIONSHIP
MANAGEMENT

35. TEXT
RECOMMENDATION ENGINES
IMPACT 33% OF SALES @AMAZON

36. TEXT
SENTIMENT ANALYSIS
REPUTATION, BRAND, POLITICS

37. TIME SERIES
PREDICTIVE MAINTENANCE

38. TIME SERIES
ENTERPRISE RESOURCE PLANNING

39. TIME SERIES
HEALTH DATA

40. TIME SERIES : ROBOTICS

41. TIME SERIES
PREDICTIVE FINANCIAL ANALYTICS

42. LONG TERM
THE SINGULARITY
OR NOT…

43. LANDSCAPE FOR DL/ML
MACHINE
LEARNING
• SKYTREE
• BIGML
• IBM ALCHEMY
• H2O
• DATAROBOT
• SCALED
INFERENCE
• WISE.IO
CLOSED DL
• METAMIND
• CLARIFAI
• ENLITIC
OPEN-SOURCE DL
• DEEPLEARNING4J
• THEANO (PYTHON)
• TORCH (LUA)
• CAFFE (C/C++)

44. DEEP LEARNING FOR INDUSTRYAPPLICATIONS
Fraud detection Machine Vision Sentiment Analysis
SKYMIND // DEEP LEARNING
DATASYSTEM
RDBMS
EDW
MPP
SOURCE

45. FEATURES
• All major neural nets
• RBM, ConvNet, LSTM, RNTN, DBN,
SDA, Deep autoencoder
• Composable framework
• Hadoop, Spark, Akka integrations
• Parallelized GPUs and CPUs
• Cross-platform
• Apache 2.0 License

46. CROSS-PLATFORM
• LINUX (SERVERS)
• WINDOWS (DESKTOP)
• OSX
• ANDROID (MOBILE)
• EMBEDDED JAVA (IoT)

47. THE FUTURE OF ENTERPRISE AI
An open-source framework.
Open source dominates OS with Linux,
and big data with Hadoop.
Open source will win AI with
Deeplearning4j:
• Distributed deep learning on GPUs
• Serving 10M Java/Scala programmers

48. QUESTIONS?
HELP@SKYMIND.IO

49. #datapopupseattle
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50. #datapopupseattle
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