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

1. AI as a Service
New Major Technology Trend
Asher Sterkin
asher.sterkin@gmail.com
HUstart Meeting
May 17, 2017, Jerusalem

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. Why Technology Matters?

6. Equipment
Tools
Frameworks
OS
Middleware
Services
Techniques

7. Equipment
Tools
Frameworks
OS
Middleware
Services
Techniques
This is NOT
your core!

8. Using Technology Radar

9. 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.

13. 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

14. 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

15. Amazon AI

16. Azure AI

17. Google AI

18. IBM Watson

20. AWS Open Source AI @ OSCON (Adrian Cockroft)

22. What’s Your AI Strategy?

23. 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

24. 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

25. 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

26. 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

27. 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

28. Innovate-Leverage-Commoditize

29. Strategic Domain-Driven AI Design
Generic AI
Optimization
Pre-trained Models
Secret Sauce

30. 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

31. ML/DL In Computational Context
Computation: Input Data X Policy X Current State → Result X New State
value range
#ofpaths
exponential
growth of
complexity

32. 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

33. 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?
… ?

34. 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

35. 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?

36. Complex Adaptive Systems are not Casual
Disorder
“Don’t trust us (humans), because
we will disappoint you”

37. AI Trinity

39. Domain Expert
Data Scientist
Software Architect

40. Domain Expert
Data Scientist
Software Architect

41. Domain Expert
Data Scientist
Software Architect

42. “Consensus is poisonous
for innovation”
D. Snowden
The last slide