This document discusses the challenges of developing cognitive systems through distributed teams. It provides an overview of IBM Research Brazil's work on cognitive computing projects using distributed teams. Key challenges discussed include communication across remote teams and managing complexity and uncertainty in cognitive system development. The document also describes tools and methods used by IBM Research Brazil for distributed development, such as DevOps practices, GitHub for issue tracking, and cloud deployment of systems.

1. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C. © 2016 IBM Corporation
Distributed Development of
Cognitive Systems at IBM Research - Brazil
Alan Braz, MSc, Software Engineer
alanbraz@br.ibm.com, @alanbraz
Maíra Gatti de Bayser (mgdebayser@br.ibm.com), PhD, Research Staff Member, Master Inventor
Claudio Pinhanez (csantosp@br.ibm.com), PhD, Senior Manager

2. © 2016 IBM Corporation
IBM Research Labs
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▪ Visual Analytics & Insights
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▪ Analytics & Intelligence
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▪ Internet of Things
▪ Big Data / Analytics
▪ Enterprise Cloud Services
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▪ Big Data Analytics
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Haifa
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3. © 2016 IBM Corporation
IBM inventors received a record 7,355 U.S.
patents in 2015, marking the 23th consecutive
year it has topped the list of the world’s most
inventive companies. IBM became the first
company to be granted as many as 5,000 U.S.
patents in a single year. It took IBM's inventors
more than 50 years to receive their first 5,000
patents after the company was established in
1911. IBM's first patent: U.S. Patent #998,631
issued July 25, 1911.
23 Consecutive Years of IBM Patent Leadership

4. © 2016 IBM Corporation
IBM Research I Brasil
Mission: To be known for our science and technology
and vital to IBM, Brazil, our partners in the region, and
globally.
IBM @ Tutoia
Street
São Paulo
IBM @ Avenue
Pasteur
Rio de Janeiro
First IBM Research lab in the South America, founded in 2010
Strategic Focus Areas
Smarter natural resources discovery and logistics with
emphasis on NR analytics. Optimization, SW technologies
Cognitive Computing with focus in Social Data Analytics
reinvent large-scale service systems and enterprises.
Industrial Technology & Science with emphasis in sensors
and microfluidics for Health and Natural Resources
Visual analytics & comprehension: methods and
technologies for expert knowledge capturing, modeling,
and representation

5. © 2016 IBM Corporation
IBM Watson: From Jeopardy to Cognitive Solutions and ....
5

6. © 2016 IBM Corporation
... bringing Cognitive Computing to Everyone: Watson Developer Cloud
6
http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/

7. © 2016 IBM Corporation
Platform as a Service
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http://www.bluemix.net

8. © 2016 IBM Corporation
SOCIAL COGNITIVE TECHNOLOGIES
social knowledge
mining
conversational
cognitive agents
2016: Social Data Analytics Research Workstreams
social
knowledge
gatherer
social
knowledge
organizer
cognitive
dialogue
framework
cog agent
interaction
design
CognIA: self-service investment adviser
mission: to make Watson socially skilled
8

9. © 2016 IBM Corporation
Social Cognitive Technologies Research
9
Claudio Pinhanez
Manager,
Social Data Analytics
PhD MIT
Maíra Gatti
PhD PUC-Rio
Ana Appel
PhD USP-SC
Paulo Cavalin
PhD Quebec
Alan Braz
MSc. Unicamp
Heloisa Candello
PhD Brighton
Capabilities
KDD, HCI, Simulation, Machine Learning, Social Media,
Agents, Design, Graph Mining, Computer Vision, Deep
Learning, Large Databases, Agile.
+10 undergraduate/graduate interns
Marcia Ito
PhD USP-Poli
Renan Souza
Msc UFRJ
Flavio Figueiredo
PhD UFMG
Marisa
Vasconcelos
PhD UFMG

10. ©2015 IBM Corporation10 29 August 2016
Creating a new generation of self-service automated systems for investment and wealth management.
Investment Advisors

11. © 2015 IBM Corporation
Conversational Systems, the 4th Interface Paradigm
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1. batch 2. command line 3. point & click 4. conversational
“There are more than 2 billion users of mobile apps”
(The Wall Street Journal, December, 2015)
“Forget Apps, Now The Bots Take Over”
(TechCrunch, Sep 29, 2015 by Beerud Sheth)
https://techcrunch.com/2015/09/29/forget-apps-now-the-bots-take-over/

12. ©2015 IBM Corporation12 IBM Research
Prototype: Direct, Simple, Conversational
● Natural Dialogue System
○ Mixed initiative
● Dialogue Mediation
○ Coordination control
● Investment Options
○ Investment Concepts & Options – Q&A
○ Investment Simulation – Task-oriented
○ Investment Opinion – Hard to answer
● Portuguese
● Brazilian Finance
○ Saving Accounts
○ CD (Certificate of Deposit)

13. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Challenges on Distributed Development
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Picture source: https://www.linkedin.com/pulse/how-organize-efficient-work-distributed-development-team-soloshchuk

14. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Remote working… ?
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15. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
How do distributed teams interact with each other…?
“Meetings-driven development”
“Email-driven task management”
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16. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Issues on Remote Working / Distributed Cognitive Teams
▪ Communication style
▪ Expectations and establishing common ground
▪ Performance control
▪ Interdependence
– Lacks share of information -> less creativity
▪ Communication patterns
– Remote working ≈ Closed door offices
– Social interactions
– Lack of social communication is associated with lower trust and cohesion
▪ High volume of messages can lead to confusion
▪ Need skilled managers
▪ Balance onsite and remote working
▪ Integrate social business technologies
▪ Software integration
▪ Privacy concerns
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17. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Challenges on Cognitive Systems
Development
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Picture source: http://www.forbes.com/sites/ibm/2015/10/06/four-steps-to-get-started-with-cognitive-computing/

18. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Cognitive Computing
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White paper: http://www.research.ibm.com/software/IBMResearch/multimedia/Computing_Cognition_WhitePaper.pdf

19. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Software Engineering & Cognitive Systems Development
▪ Current well established SE practices:
– Agile process
– Extreme Programming (XP)
– Continuous process
– Design improvement
– Shared understanding
– Collective code ownership
– Simple design
19
Waterfall Model
(Royce, W.)
Prototyping Model
(Brooks, F.)
V Model
(Forsberg, K.)
Incremental Dev.
(IBM)
Spiral Model
(Boehm, B.)
1970 1975 1988 1991 2001
Agile Soft Dev.
Kent Beck
XP
Scrum
1996
FDD
RUP
Applicability Degree for Cognitive Systems Development

20. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Software Engineering & Cognitive Systems Development
▪ New challenges:
– Interdisciplinary areas
– Innovation
– Complexity
– Algorithms evolution
– Data management
– Models management
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Standard Waterfall Model
Spiral Model
eXtreme Programming
predictive adaptive
rigid
methodologies
iterative
methodologies
agile
methodologies
Extreme Programming. Kent Beck, 1996

21. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Complexity and Uncertainty
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http://iq3group.blogspot.com.br/2012/12/solving-data-governance-by-scaling.html

22. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Challenges
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White paper: http://www.research.ibm.com/software/IBMResearch/multimedia/Computing_Cognition_WhitePaper.pdf

23. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Methods and Tools for Managing Distributed
Development of Cognitive Systems
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24. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
DevOps
https://www.ibm.com/developerworks/br/devops/principles.html
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25. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
DevOps
▪ Enable collaboration, co-development and reuse (a.k.a. social coding).
▪ Minimize the friction and fuss required to build and deploy your code. This will be a
combination of automating what are often manual or custom processes, in
combination with providing ready-to-go platforms and specific guidance on release
requirements.
▪ Provide ready-to-go tools, platforms and methods. Many of these services are
currently available from various internal and external corporate offerings. We're
working on providing many of these services automatically and without the friction of
concerns about funding, signups, approvals, etc.
▪ Make it easy (or at least, as easy as possible). This is all about providing ready and
useful guidance on using tools, getting help, and learning what you need to know.
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26. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Tools
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27. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.27

28. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Managing tasks with GitHub issues
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29. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
GitHub org structure
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▪ 14 code repos
– Java, Node.js, Python, Go, HTML
▪ 1 planning repo only with Issues
– 81 Open
– 325 Closed (since Jun 2015)
▪ 2 types of Infrastructure
– Softlayer
– Bluemix
▪ 2 Database paradigms
– SQL (DB2 and DashDB)
– NoSQL (Cloudant)

30. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Investment Advisor Team
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Undergrad interns
Senior Manager
PhD
MSc
Software
Engineer
HCI,
Machine Learning
Designer, User
Experience
Database,
Graph
Analytics
Data
Mining
Data Mining
Databases
Software Engineer,
Multi-Agent Systems
Machine
Learning

31. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Architecture - Several Micro-services
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32. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
The Architecture
▪ The Investment Advisor Prototype is built on top of
SABIÁ - the Speech-Act-Based Intelligent Agents framework
▪ SABIÁ is a conversational framework for multi-party chabots built on top of Akka
middleware that enables mixed-initiative dialogues.
– Gatti de Bayser, M.; Cavalin, P.; Programming Conversational Agents with Akka Actors. Tutorial at 18th European
Agent Systems Summer School (EASSS 2016), Sicily, Italy. Available to download.
▪ Akka is a toolkit and runtime that implements the Actor model on the JVM and
was inspired by Erlang in many ways.
▪ Akka can be used to build highly concurrent, distributed, and resilient message-
driven applications. A large ecosystem of more than 250 Github projects has
grown on Akka, in programming languages like Scala, Java, Groovy or JRuby.
Akka.io. Akka is Open Source and available under the Apache 2 License.
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33. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Actor Model
▪ Originated in 1973
▪ The actor model in computer science is a mathematical model of concurrent
computation that treats "actors" as the universal primitives of concurrent
computation.
▪ In response to a message that it receives, an actor can:
– make local decisions,
– create more actors,
– send more messages, and
– determine how to respond to the next message received.
▪ Actors may modify private state, but can only affect each other through
messages (avoiding the need for any locks).
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Carl Hewitt; Peter Bishop; Richard Steiger (1973). "A Universal Modular Actor Formalism for Artificial Intelligence". IJCAI.

34. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Cloud Deployment
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35. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Main back-end project - Akka agents
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▪ Technologies
– Java runtime
– Akka toolkit (http://akka.io/)
– Socket.io
▪ 11 team members contributed
▪ Average of 50 commits/member
▪ 2 main developers
– 80+ commits
▪ Automated unit and integration tests
▪ Continuous deploy
▪ Performance profiling
▪ Stress tests

36. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Main front-end project - Socket.io Web/mobile chat
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▪ Technologies
– Node.js runtime
– Ionic framework (Angular.js)
– Socket.io
▪ 6 team members contributed
▪ Average of 29 commits/member
▪ 2 main developers
– 60+ commits
▪ Continuous deploy
▪ Mobile and web interfaces

37. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Evolution
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▪ Sort of a 2-week Scrum Sprint called Step
▪ Review and Planning at the same meeting (2h)
▪ Weekly status meeting (like a “Daily Scrum”)
▪ Not fully self-organized team
Akkaagents
Chat

38. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Conclusions
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39. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
Conclusions
▪ It is not trivial to manage distributed teams on the
development of cognitive applications
▪ Several communication tools and methods are needed
▪ Software engineering practices and DevOps can provide
methods and tools to help tackling some challenges
▪ There is still a need on improving the process so it can
become more mature while with space to innovation and
creativity.
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40. © 2016 IBM CorporationGatti de Bayser, M.; Braz, A.; Pinhanez, C.
IBM Research – Brazil
http://www.research.ibm.com/brazil/
alanbraz@br.ibm.com, @alanbraz