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AI as a service

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Major themes from the Spring 2017 Thoughtworks Technology Radar. Presented to HUstart accelerator on May 17 2017, Hebrew University, Jerusalem.

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AI as a service

  1. 1. AI as a Service New Major Technology Trend Asher Sterkin asher.sterkin@gmail.com HUstart Meeting May 17, 2017, Jerusalem
  2. 2. About Myself ● Close to 40 years of experience in the software technology field ● VP Technology, Chief Technology Advisor Group, NDS (2010 - 2012) ● Distinguished Engineer, Office of CTO, Cisco Engineering (2012 - 2016) ● Today CTO at IRKI ● Focusing on software product line strategy ○ Lean Startup ○ Wardley Maps ○ (Strategic) Domain-Driven Design ○ Cynefin ○ Promise Theory ○ Serverless Architecture ○ ...
  3. 3. Why Technology Matters?
  4. 4. Equipment Tools Frameworks OS Middleware Services Techniques
  5. 5. Equipment Tools Frameworks OS Middleware Services Techniques This is NOT your core!
  6. 6. Using Technology Radar
  7. 7. Thoughtworks Technology Radar The ThoughtWorks Technology Advisory Board, a group of senior technology leaders in ThoughtWorks, creates the radar. They meet regularly to discuss the global technology strategy for ThoughtWorks and the technology trends that significantly impact our industry.
  8. 8. Themes For the Last Edition ● Conversational UI and natural language processing ● Intelligence as a service ● Developer experience as the new differentiator ● The rise of platforms ● Pervasive Python
  9. 9. Mentioned in this Edition ● Techniques ○ Conversationally aware APIs - ASSESS ■ Amazon Alexa ■ Google Voice ○ Back-end for Front-end - TRIAL ● Tools ○ Apache Airflow - TRIAL ○ Scikit-learn - TRIAL ○ Amazon Recognition - ASSESS ● Platforms ○ api.ai - ASSESS ○ wit.ai - ASSESS ○ Cloud-based Image Comprehension - ASSESS ■ Amazon Recognition ■ Microsoft Computer Vision API ■ Google Cloud Vision API ○ Nuance Mix - ASSESS ○ Voice Platforms - ASSESS ■ Amazon Alexa ■ Google Home ● Languages and Frameworks ○ Caffe - ASSESS ○ DeepLearning.scala - ASSESS ○ Keras - ASSESS ○ Knet.jl - ASSESS
  10. 10. Amazon AI
  11. 11. Azure AI
  12. 12. Google AI
  13. 13. IBM Watson
  14. 14. AWS Open Source AI @ OSCON (Adrian Cockroft)
  15. 15. What’s Your AI Strategy?
  16. 16. ignore AI Irrelevance in a couple of years embrace AI A lot of $$ paid to people who speak language you cannot comprehend and produce models they do not understand themselves
  17. 17. Your Product or Service Value Chain + Evolution (Wardley Maps) Your AI secret sauce AI as a Service G C P U Cloud Platform AI scientific breakthrough Your Customer Needs Infrastructure Optimization
  18. 18. Your Product or Service Value Chain + Evolution + Movement Your AI secret sauce AI as a Service G C P U Cloud Platform AI science breakthrough Your Customer Needs Infrastructure Optimization
  19. 19. Your Product or Service Value Chain + Evolution + Movement Your AI secret sauce AI as a Service G C P U Cloud Platform AI science breakthrough Your Customer Needs Infrastructure Optimization
  20. 20. Your Product or Service Value Chain + Evolution + Movement Your AI secret sauce AI as a Service G C P U Cloud Platform AI science breakthrough Your Customer Needs Infrastructure Optimization
  21. 21. Innovate-Leverage-Commoditize
  22. 22. Strategic Domain-Driven AI Design Generic AI Optimization Pre-trained Models Secret Sauce
  23. 23. DDD AI Architecture Pereferrial Adapters Application Services Dynamic State Machines Conversations Application-level Stimulo Domain Services Command/Query Requests EventsRaw A/V/T Inputs Dynamic Classifications Anomaly Detection Event Processors Feedback and Training Historical Data
  24. 24. ML/DL In Computational Context Computation: Input Data X Policy X Current State → Result X New State value range #ofpaths exponential growth of complexity
  25. 25. ML/DL In Computational Context: Pure Function Input Data → Result Simple Function (if-then-else) Pattern Matching Complex Algorithm Statistical Algorithm Heuristics Machine Trained Function Neural Network Deep Neural Network Convolutional Neural Network Generative Adversarial Networks
  26. 26. ML/DL In Computational Context: Functor Input Data X Policy → Result Higher-Order Function Functor (map) Monoid (fold, reduce) Monad (flatMap) Applicative (apply) Machine Trained Higher-Order Function? … ?
  27. 27. ML/DL In Computational Context: State Machine Input Data X Policy X Current Sate → Result X New State State Monad (FP) Concrete/Abstract Class (OOP) Final State Machine Harel/UML Statechart Recurrent Neural Network Long Short Term Memory Network
  28. 28. Beware of “Black Swan” and “The Turkey Problem” volum e velocity variety Variety is the most time sensitive axis of the V3 Cube edge computing simulation?
  29. 29. Complex Adaptive Systems are not Casual Disorder “Don’t trust us (humans), because we will disappoint you”
  30. 30. AI Trinity
  31. 31. Domain Expert Data Scientist Software Architect
  32. 32. Domain Expert Data Scientist Software Architect
  33. 33. Domain Expert Data Scientist Software Architect
  34. 34. “Consensus is poisonous for innovation” D. Snowden The last slide

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