Wellington Meetup, Focus Technologies, Needs of Society: National strategies for science, technology and innovation

1. Data Science Wellington Meetup
Data Science as a commercial and academic
practice.
• Distributed Intelligence
• Education
• Value Creation
Meetup: https://www.meetup.com/Data-Science-Wellington/
Facebook https://www.facebook.com/groups/559234647748490/
Room AM105, Alan MacDiarmid building Victoria University, Kelburn Campus,
Wellington

2. Plan
• Introduction
• Yuri Anisimov
• Richard Arnold
• Data Science in Victoria University
• A need for Data Science
• Drivers for Technology Innovation, case studies
• Gartner’s Hype Cycle and Trends
• Methodology – Platform Approach
• Focus Technologies
• Organisation and Timeframe
• Discussion

3. Yuri Anisimov
Current:
• Ability Factors Pte. Ltd., Managing Director
• Startmesh, Fintech Director
• RVC, Representative, South-East Asia
Past:
• Hewlett Packard Enterprise – Programme Director, Asia)
• WestLB (Head of Production Engineering, Asia)
• Stecklov Math Institute - Research, Statistical Physics
• Saint-Petersburg EE University – Assistant Professor

4. Richard Arnold
• Current:
• Associate Professor in Statistics
• At VUW since 2001 – research in reliability, clustering, geophysics,
and a variety of applied statistics projects
• Past:
• Researcher in Astronomy
• Statistical epidemiology – environmental risks to health
• Mathematical Statistician at Statistics NZ

5. Data Science at
Victoria
A new undergraduate programme

6. Data Generation
Decision Making
Communication
Presentation
Data storage,
retrieval transmission
transformation
Data Analysis
Legal and Ethical Framework
Applied Science, Computer Science, Engineering, Statistics, Information Systems, Decision
Science
Data Science

7. Training new data scientists
• Data science integrates
• Computer Science
• Statistics
• Decision science
• Subject matter expertise
• Need a set of tools and skill at using them
• A technically strong programme
• Computing fluency in at least two languages + SQL
• Communication skills vital
• Connection of methods & results to context

8. Our students are already asking for this
• We have regular queries from new students about Data Science
• Programmes exist at U of Auckland, U of Canterbury, Massey, AUT
(Analytics), Waikato (Data Analytics)
• Research students everywhere need Data Science
• Genetic data
• Environmental monitoring
• Business transactional data
• Official Statistics
• Linguistics speech data processing
• Image processing in physics
• Economics and econometrics
• …

9. An undergraduate degree
• Three years full time
• 120 points per year – 360 total
• 15 points per course – 8 courses per year
• Room for two majors (each half of the degree)
• Data Science
• Specify 45 points at each level + 15 points from electives
• Designed as a complementary major to …
• Computer Science, Engineering, Statistics, Mathematics, …
• Biology, Psychology, Environmental Science, Geography, …
• Linguistics, Politics, Sociology, Criminology, …
• Economics, Information Management, …
• A major in the Faculties of Science, Commerce and Humanities & Social
Sciences

10. Year Data Science Computing Statistics Electives
1 DATA101
Introduction
Contexts, Data Sources
Modern Data Ecosystems
Principles of information
systems
COMP132
Introductory
programming
(Python)
STAT193
Introductory Applied
Statistics
(Excel, INZight)
• Mathematics
• Computer Science
• Other disciplines
2 DATA201
Communication
Ethical and Legal
framework
Mathematical Tools
Probability
Data simulation,
integration
(Python)
DATA202
Programming and data
management
Data transformation,
cleaning, summary,
display
(R, SQL)
STAT292
Applied Statistics
Regression
Experimental Design
(SAS - EG)
• Philosophy
• Geography
• Economics, Finance
• Maths/CompSci/…
• …
3 DATA301
Communication
Visualisations
Decision modelling
Project assessment
(R Shiny)
COMP309
Computational
techniques for Data
Science
Machine Learning, AI,
Graphical models,
Data mining, Clustering
(Python)
DATA303
Statistical techniques for
Data Science
Binary/Count/Categorical
data
Decision Theory
(R)
• Practicum
• GIS
• Info Management
• …

11. Implementation Timetable
• DATA 202 exists already (SCIE 201 Special Topic)
• COMP132, COMP 309 new in 2018
(STAT193, STAT292 longstanding courses)
• DATA 101, 201 in development in 2018
• Will be offered for the first time in 2019 – first students enrol in the major
• DATA 301, 303 in development in 2019
• Will be offered for the first time in 2020 – first students complete the
major

12. Postgraduate Study
We already offer:
• Honours, PG Diploma in Statistics (a standard year of courses)
• MSc by thesis in Statistics (12 months on top of a year of
courses)
• Master of Applied Statistics (12 months after a BSc)
• Two trimesters of taught courses
• Summer including:
• STAT480 Research Methods
• STAT487 Research Project
• STAT581 Statistics Practicum – (5 week work placement in a data rich work
environment)
• PhD in Statistics

13. Postgraduate Study in Data Science
Not yet decided… possibilities are:
• Honours, PG Diploma in Data Science – Taught courses in
statistics, computing and data science (e.g. COMP 473 Big
Data)
• MSc by thesis in Data Science
• 12 month (180 point) Masters of Data Science
Similar to Master of Applied Statistics (work placement, project)
• Conversion Masters – combination of undergraduate and
postgraduate content, with a possible preparatory boot camp
in computing
Will be developed from 2019 onwards
(PhD study already possible)

14. We’re seeking feedback on our proposed
undergraduate programme
• Any comments you have are welcome
• Richard Arnold
richard.arnold@vuw.ac.nz

15. Why New Zealand needs Data Science?
Not as a hype but to support better living, stronger communities,
and create more opportunities
Challenges for Data Science Companies:
• Consistent Governance
• Establish Dialog between Business Drivers and Academic Ideas
• (Small) Business is not prepared to fund academic research
• Technology Transfer KPIs
• Supporting Infrastructure and Services (telecom, cloud, policies, privacy
laws, IP protection)
Issues:
• Assess practical problems the society is facing
• Establish Research Commercialisation Focused Framework
• Address Global Markets
• Utilise existing skill set and assist in skill development

16. Needs of Society: National strategies for
science, technology and innovation (STI)
OECD countries have used the so-called grand or global
challenges as a means of orienting public investments in STI:
• climate change
• energy security
• Health
• demographic changes
• Brazil, China and India - longer-term economic development
strategies
• Argentina, Colombia and Vietnam - strategies to diversify economies
• France, Italy, Japan and the United States - to re-start economic
growth
• Germany and Korea - new growth areas such as green innovation.

17. Innovation Drivers
Initiatives
• Singapore – Smart Nation Program
• Russia – National Technology Initiative (NTI)
• Japan – PRISM
• China - National High-tech R&D Program (863 Program)
• United States - A STRATEGY FOR AMERICAN INNOVATION (2015)
Demand-side innovation policies:
• Social cohesion
• Business support
• Public support for basic research.
• Human resources

18. Singapore Smart Nation (started in 2013)
“Innovation” itself is defined as solving people’s problems
Pressures – increased urban density and an ageing population,
optimization of existing resources, rather than reliance on new ones
Areas:
• Smart health care
• Transport
• Housing
Smart Nation Platform - nationwide sensor network data analytics
• Connect
• Collect
• Comprehend

19. Singapore: Key Domains and Enablers
5 Key Domains
• Transport;
• Home & environment;
• Business productivity;
• Health and enabled ageing;
• Public sector services.
Enablers
• Facilitating smart solutions
• Culture of experimentation and sustaining innovation
• Building computational capabilities

20. Russia - National Technology Initiative
(NTI) (2014-2035)
NTI - emergence of the companies that would be competitive at the fundamentally new
markets of the future.
• identifying new markets, including the main factors of the demand, key market niches and
possible types of products and services to fill these niches;
• identifying key technologies due to which products and services will be created in the new
markets;
• a set of measures for support and stimulation, including institutional, financial and research
tools that allow for growing national companies – champions in the new markets.
• NTI involves the creation of strategies to develop fundamentally new markets.
Constituencies:
 fast growing technology companies;
 leading universities;
 research centers;
 major business associations;
 expert and professional communities (even informal).

21. National Technology Initiative (NTI)
«Markets» group «Technologies» group
EnergyNet distributed power from personal power to smart
grid and smart city)
Digital design and simulation
FoodNet (system of personal production and food and water
delivery)
New materials
SafeNet (new personal security systems) Additive technologies
HealthNet (personal medicine) Quantum Communications
AeroNet (distributed systems of unmanned aerial vehicles) Sensory
MariNet (distributed systems of unmanned maritime transport) Mechabiotronics
AutoNet (distributed network of unmanned management of
road vehicles)
Bionics
FinNet (decentralized financial systems and currencies) Genomics and synthetic biology
NeuroNet (distributed artificial elements of consciousness and
mentality)
Neurotechnologies
BigData
Artificial intelligence and control systems
New sources of energy
Unit base (including processors)

22. New Zealand STI Outlook
• Excellent entrepreneurial environment
• Ease of doing business
• Small size
• Export-oriented economy, relies heavily on the primary sector
• Room for diversification, the government investing in high-value
manufacturing and services sectors
• Total annual government investment in science and innovation $1.66B by
2021
• 2016 Science and Innovation System Performance Report - Ministry of Business, Innovation & Employment
• New Zealand’s largest inventor network: A glimpse of our innovation ecosystem (2011)
• Understanding Innovation Ecosystems - Catriona Sissons
• Budget 2017 science and innovation funding
• Research, Science, and Technology Act 2010
• National Statement of Science Investment 2015-25
• Most Important Problems facing New Zealand in 2017

23. Innovators are exploiting the density and
diversity of the networks (Catriona Sissons)

24. New Zealand – Issues and Advantages
To discuss: what can be addressed?
• Sustainable living – Smart housing, Sensors, Automation
• Smart Agriculture
• Social Interaction
• Financial Services
• Security
• Energy
• Logistics
• Education
• Government Technology - Public services – not bad
• Economic Development – access to global markets
IoT Report - Accelerating a Connected New Zealand

25. Setting Goals - sustainable pace
What are we prepared to undertake?
• Temporary competitive advantage
• Revenue generation and value creation
Challenges:
• Society (Market) Drivers
• Technology Drivers
• Team Creation
• Interest of stakeholders: decision makers, investors
• Scalability
Roadmap;
• Collaborative effort
• New capabilities – unsolved academic problems,
• Technology transfer
• Platform approach

26. Suggested Focus: The Intelligent Digital Mesh
The Gartner Hype Cycle for Emerging Technologies, 2017
three mega-trends:
• Artificial intelligence (AI) everywhere
• Transparently immersive experiences
• Digital platforms
Gartner Strategic Technology Trends for 2018
• AI for decision making, reinvent business models and ecosystems
• Intelligent Things, IoT Digital Twins
• Disruption in Financial Technology
• Authenticity of Information,
• AI driven counterfeit reality
• Security Adaptive Risk and Trust: fault remediation rather than
protection.

28. Platforms Approach: 3D matrix
Why Platform:
• Platform Revolution, Sangeet Choudary (2016)
• Information Rules A Strategic Guide to the Network Economy (1999) - Carl Shapiro
Needs and trends based on the priority of network technologies
targeting B2C (and B2B?) market:
3D Axis:
• Markets
• Technology
• Services
(Adding another dimension – Participants, bringing together
entrepreneurs, researchers and investors)

29. Platforms Approach: Markets
• Smart Living and Social (communities, government services,
fake news analysis, influencers)
• Resource Management (Energy, Utilities control Systems)
• Food Technology
• Logistics and Transport, including autonomous vehicles
• Health (smart medicine, drug discovery, health monitoring)
• Financial Technology (fraud, credits, trading strategies, market
analitycs, decentralized financial systems)
• Security, Risk Management, Business Analytics and Market
intelligence
• Storytelling

30. Platforms Approach: Technology
• Distributed intelligence, computing and contracts
• Artificial Intelligence, including multi-agent system,
swarming intelligence
• Machine Learning (Supervised, Non-supervised, Neural
Networks)
• Risk Management Approach
• Predictive modelling and Data Analytics, Anomaly
Detection
• Visualisation techniques, brain-computer interfaces, UX

31. Platforms Approach: Services
Data Science as a Service
• Open Data
• Standards
• Intellectual Property
• Privacy Laws
• Education
• Entrepreneurs
• Venture Capital

32. The Group Mission
• We might be able to create a fully functional collaborative
incubator – companies within six months.
• The Team
• Connect people to work on exciting commercial and research
projects
• Discuss opportunities, ideas, trends
• Bring in partnerships with overseas universities and companies
• Formulate academic problems
• Contribute to and utilise open source approach (Standard, Protocols)
• Commercialise research
• Launch startups
• Provide access to venture services (mentorship, capital rising,
intellectual property protection)
• Contribute to ecosystem

33. Roles
Mentor/Adviser
• Entrepreneur
• Innovator
• Investor
Contributor
• Academic Researcher
• Software Developer
• Marketer/Sales

34. Suggested Focus Technologies
Commercial Applications:
• Distributed Artificial Intelligence (Swarming, Multi-Agent Systems) as a
foundation for sustainable living (IoT Security, Logistics)
• Autonomous Vehicles
• Predictive Analytics
• Verified Reality (fake news discovery)
• Crypto-applications
Academic problems:
• Emergence
• Ontology of prediction
• Scalability of Swarming Systems
• Homomorphic encryption
• Risk based security
• Invariant representations

35. Distributed Artificial Intelligence (DAI)
DAI systems - autonomous learning processing nodes
(agents).
• Robust and Elastic
• Loosely coupled
• Adaptive to changes in the problem definition or
underlying data sets
Two types of DAI:
• Multi-agent systems:
• Distributed problem solving

36. Predictive Modelling
Thermodynamics: collective behaviour of connected
nodes creating a system with physical properties: order or
phase transitions
Short range interaction (insect algorithms)
Application:
- Sentiment analytics
- Fake news
- Source verification

37. Next Steps
Next meetup options
1. Smart Nation Singapore
2. National Technology Initiative (Russia)
3. Multi-Agent Technologies
Team Creation
• Expression of interest
• Area of Interest
• Analysing your ability to commit time
• Team Selection

38. Open Discussion
• Questions, suggestions, expression of interest

39. Data Science Related Groups in WLG
1. https://www.meetup.com/machine-learning-data-science-WLG/ - Talks and networking: the
intersection of statistics, machine learning, business analytics, data-based programming, and all
that good
2. https://www.meetup.com/Wellington-Analytics-Freelancers/ - network for those looking into
interesting gigs and projects
3. https://www.meetup.com/Wellington-Data-Scaling-Chats/ - for folks interested in working with
and managing data at scale with open source software.
4. https://www.meetup.com/Data-Driven-Wellington/ - to stay informed and to make those
connections.
5. https://www.meetup.com/Wellington-Data-Management-and-Analytics-Meetup/ - Data
Warehousing, Business Intelligence, Data and Analytics on the Cloud, Big Data, Governance and
Integration. Let us meet and share our knowledge
6. https://www.meetup.com/Data-Without-Borders-NZ/ - gathering a network of non-profits, data
scientists, and eager volunteers to change the world.
7. https://www.meetup.com/Big-Data-Developers-in-Wellington/ - This is an IBM sponsored Meetup
group geared towards developers, data scientists, data engineers
8. https://www.meetup.com/Wellington-Spark-Meetup/ - for those using Apache Spark,
9. https://www.meetup.com/Wellington-R-Users-Group-WRUG/ - promotion of R for its community of
interest
10. https://www.meetup.com/Wellington-Information-Revolution-Meetup/ - to encourage the
discussion of information flow and information transformation

40. Backup Slides

41. Swarming
The Invincible, Stanislaw Lem (1973)
Necroevolution:
A planet inhabited by self-organizing, self-replicating nanites which
aren’t truly conscious but display pseudo-intelligent behaviour as an
emergent phenomenon
Swarms of minuscule, insect-like elements, capable of only very
simple behaviour. When they feel threatened, they can assemble into
huge clouds, able to travel at a high speed and even to climb to the
top of troposphere. These swarms display complex behaviour arising
from self-organization and can incapacitate any intelligent threat -
An evolution winner of selection pressures of "robot wars“

42. Smartdust
• Smart Dust entered the Gartner Hype Cycle on Emerging
Technologies in 2003 and returned in 2013 as the most
speculative entrant.
• Smartdust is a system of many tiny microelectromechanical
systems (MEMS) such as sensors, robots, or other devices
• The concepts for Smart Dust emerged from a workshop at
RAND in 1992 and a series of DARPA ISAT studies in the mid-
1990s due to the potential military applications of the
technology
• A Smart Dust research proposal was presented to DARPA
written by Kristofer S. J. Pister from the University of
California, Berkeley, in 1997. The project led to a working
mote smaller than a grain of rice and larger "COTS Dust"
devices kicked off the TinyOS effort at Berkeley
• Nanoelectronics Research Centre at the University of
Glasgow is developing a related concept: Smart Specks

43. New Zealand – Fact of Life

44. social problems
1. Domestic violence (52% very concerned)
2. Child poverty (46%)
3. Cost of living (44%)
4. Alcohol and drug abuse (43%)
5. Lack of jobs for young people (41%)
6. Pollution of New Zealand lakes and rivers (40%)
7. Level of dependency on social welfare (38%)
8. Cost of housing (37%)
9. Cost of tertiary education (30%)
10. Quality of education provided by state primary & secondary schools (29%)
11. Home burglaries (29%)
12. Problem gambling (26%)
13. Young New Zealanders moving to Australia (19%)
14. Traffic congestion (18%)
15. Public transport (16%)

45. NTI: TECHNOLOGY SUPPORT AND INSTITUTIONAL TRANSFORMATIONS

46. NTI MATRIX LOGIC
46
Aeronet
Marinet
Autonet
Neuronet
Energynet
Foodnet
Healthnet
Safenet
Finnet
Medianet
Basic technological package
Extremums
Olympics
Contests
Coteries
Trajectories
Mentors
Challenges
Careers
Environment
Networks
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Concierge
IP development
Stimulation of
consumption
Fostering special
conditions at the
internal market
(“quasimonopoly”)
Strategic growth
support
“0+E”
Tax system
NTI companies
register
Comfortable
jurisdiction
Marketing and
export promotion
for regional
companies
Technology
standards
development
Technologies **
Services ****
New
markets
*
Talents
***
Big Data
Artificial
intelligence
Distributed
ledger/Blockchain
Quantum
technologies
New and portable
energy sources
New production
technologies
Sensory and robotics
elements
Wireless
communication
technology
Bio objects properties
management technologies
Neurotechnologies and
virtual and augmented
reality technologies
Technological policy priorities Scientific policy priorities
Demand for development institutions
Economic policy priorities
Market
demand
Technology
demand
Educational
policy
priorities
Career
management
* Approved
** Preliminary agreed
*** Ongoing discussion
**** Export promotion services
“Fog” technologies
MNCs of Russian origin
Big
scientific
challeng
es
Mega-
projects
NTI Universities
The logic behind institutional
reforms
S
L
I
М
Т
Т
M
S

47. Trends
Intelligent
• Trend No. 1: AI Foundation
• Trend No. 2: Intelligent Apps
• Trend No. 3: Intelligent Things
• Trend No. 4: Digital Twins
• Trend No. 5: Cloud to the Edge
• Trend No. 6: Conversational
Platforms
• Trend No. 7: Immersive Experience
Mesh
• Trend No. 8: Blockchain
• Trend No. 9: Event-Driven
• Trend No. 10: Adaptive Risk and
Trust