distributed development of cognitive systems at ibm research - brazil - cbsoft2016

© 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 [email protected], @alanbrazMaíra Gatti de Bayser ([email protected]), PhD, Research Staff Member, Master Inventor Claudio Pinhanez ([email protected]), PhD, Senior Manager

mailto:[email protected]




© 2016 IBM Corporation

IBM Research Labs IBM Research – Openings in 2011 IBM Research – Openings in 2012

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WatsonAlmaden

Austin

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India

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Australia Brazil

Africa▪ Next Gen Public Sector ▪Water & transportation▪ Human Capacity Development

▪ Natural Resources▪ Disaster management▪ Healthcare/Life Sciences

▪ Natural Resources▪ Social Data Analytics (FSS)▪ Visual Analytics & Insights▪ Industrial S&T

▪ Analytics & Intelligence▪ Systems &Software▪ Industry research

▪ Internet of Things▪ Big Data / Analytics▪ Enterprise Cloud Services▪ Energy, Commerce, Traffic

▪ Big Data Analytics▪ HW & SW Quality▪ Cloud▪ Mobile

Haifa

▪ Smarter Cities▪ Analytics

▪ Services ▪ Big Data Analytics▪ Front Office Digitization

▪ Semiconductors▪ Systems▪ Software▪ Services▪ Analytics

▪ Semiconductors▪ Processors

▪ Analytics▪ Storage ▪ Nanotech

Healthcare

▪ Nanotech▪ Security▪ Business

Analytics▪ Systems

Industry Solution Lab

IBM Research: 12 Labs Worldwide in 10 Countries - 3,000 employees


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


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 AreasSmarter 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


IBM Watson: From Jeopardy to Cognitive Solutions and ....

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... bringing Cognitive Computing to Everyone: Watson Developer Cloud

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http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/


Platform as a Service

7http://www.bluemix.net


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

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Social Cognitive Technologies Research

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Claudio PinhanezManager,Social Data AnalyticsPhD MIT

Maíra GattiPhD PUC-Rio

Ana AppelPhD USP-SC

Paulo CavalinPhD Quebec

Alan BrazMSc. Unicamp

Heloisa CandelloPhD Brighton

CapabilitiesKDD, HCI, Simulation, Machine Learning, Social Media, Agents, Design, Graph Mining, Computer Vision, Deep Learning, Large Databases, Agile.

+10 undergraduate/graduate interns

Marcia ItoPhD USP-Poli

Renan SouzaMsc UFRJ

Flavio FigueiredoPhD UFMG

Marisa VasconcelosPhD UFMG

©2015 IBM Corporation10 29 August 2016

Creating a new generation of self-service automated systems for investment and wealth management.

Investment Advisors


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/

©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)

■ CDB (Certificado de Depósito Bancário)

© 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


Remote working… ?

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How do distributed teams interact with each other…?

“Meetings-driven development”“Email-driven task management”

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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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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/


Cognitive Computing

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White paper: http://www.research.ibm.com/software/IBMResearch/multimedia/Computing_Cognition_WhitePaper.pdf


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

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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


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

rigidmethodologies

iterativemethodologies

agilemethodologies

Extreme Programming. Kent Beck, 1996


Complexity and Uncertainty

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http://iq3group.blogspot.com.br/2012/12/solving-data-governance-by-scaling.html


Challenges

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White paper: http://www.research.ibm.com/software/IBMResearch/multimedia/Computing_Cognition_WhitePaper.pdf


Methods and Tools for Managing Distributed Development of Cognitive Systems

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DevOps

https://www.ibm.com/developerworks/br/devops/principles.html

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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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Tools

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Managing tasks with GitHub issues

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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)


Investment Advisor Team

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Undergrad interns

Senior Manager

PhD

MSc

SoftwareEngineer

HCI, Machine Learning

Designer, UserExperience

Database,Graph Analytics

DataMining

Data Mining

Databases

Software Engineer,Multi-Agent Systems

Machine Learning


Architecture - Several Micro-services

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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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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.


Cloud Deployment

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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


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


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

Akk

a ag

ents

Cha

t


Conclusions

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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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IBM Research – Brazilhttp://www.research.ibm.com/brazil/[email protected], @alanbraz

http://www.research.ibm.com/brazil/



distributed development of cognitive systems at ibm research - brazil - cbsoft2016

Leadership & Management