The document discusses key concepts in artificial intelligence, machine learning, and big data, highlighting their social and environmental implications. It emphasizes the challenges faced by megacities, including environmental stressors, infrastructure needs, and governance issues, while also advocating for crowdsourcing and collective awareness initiatives to foster sustainability. Additionally, it explores the potential benefits and ethical considerations of pervasive data collection and sharing in society.

1. AI, Machine Learning, Big Data:
Social and Environmental Impact
Katina Michael
University of Wollongong
katinamichael.com/seminars
September 26, 2017
UTS Energy Lab

2. Basic Definitions
• Artificial Intelligence: a branch of computer science dealing with the simulation of intelligent
behavior in computers. 2 :the capability of a machine to imitate intelligent human behavior.
• Machine Learning: is an application of artificial intelligence (AI) that provides systems the
ability to automatically learn and improve from experience without being explicitly
programmed. Machine learning focuses on the development of computer programs that can
access data and use it learn for themselves.
• Big Data: extremely large data sets that may be analysed computationally to reveal patterns,
trends, and associations, especially relating to human behaviour and interactions.
• Crowdsourcing: the practice of obtaining information or input into a task or project by
enlisting the services of a large number of people, either paid or unpaid, typically via the
Internet.
• Open Data Institute: (ODI) equip, connect and inspire people around the world to innovate
with data.
• Planetary skin: Institute (PSI) is a global non-profit research and development organization
that aims to improve the lives of millions of people around the world.
• Collective Awareness: Platforms for Sustainability and Social Innovation (CAPS) initiative
pioneers new models to create awareness of emerging sustainability challenges and of the
role that each and every one of us can play to ease them through collective action.

3. Mega Cities = Mega Problems
Toward Sustainable Development
• Environmental Stresses
– Air pollution
– Traffic management
– Climate change
• Infrastructure
– Energy
– Water
– Transportation
– Health care
• Mobility/ migration
– Economic development
– Food supplies
• Power consumption
– Cont. to build out
infrastructure and use
them more efficiently
– Proactively manage
demand
• Governance/
coordination
• Financing
• Public-private
partnerships (PPP)
• Natural vs human-made
disasters

5. Argus-IS (DARPA) for the World
https://www.youtube.com/watch?v=QGxNyaXfJsA

9. The Thermostat Made Me Do It

11. Dropcam works with NEST - Convenient
https://www.dropcam.com/

12. The Internet of Things

14. The Open Data Movement
• Internet of Everything
• Nothing should be private
• Look at the great things big data will herald
• The end of starving children in Africa
• The end of criminal activity by underground
networks
• No more corruption, only transparency
• Nowhere to hide
• Sensors everywhere

16. Crowd-sourcing Platform for Self Coordination

17. The Collective Awareness Movement
• Equitable access to energy
• Sharing energy toward sustainability
• Smart grids, smart homes, smart meters, smart
cars, smart phones, smart people
– What’s wrong with this model?
• I know who you are, where you live, what
condition you are in because of the energy you
draw
• If you have something your neighbour does not,
why not share it? Redistribution is great!

21. Tagging our livestock…
Source: https://www.youtube.com/watch?v=oRqCiD4bIGc

22. Tagging our wildlife…

24. Tagging our trees and plants…

25. Tagging our food…
Source: http://www.smartag.my/wp-content/uploads/2014/03/Meat-Traceability1.jpg

26. Source: Committee on Radio Frequency Identification Technologies, Computer Science
and Telecommunications Board, Division on Engineering and Physical Sciences, National
Research Council (2004), Radio Frequency Identification Technologies:: A Workshop
Summary, p. 28, footnote 18.
Stick a chip in every person, of course

27. Source: Derek Clements-Croome (2004), Intelligent Buildings: Design, Management and Operation, p. 10

28. Utopia
• Automatic ID
– Who
• Location Tracking
– Where
• Condition Monitoring
– What
• Points of View
– How
• Evidence-on-demand
• Validation
• Risk minimisation
• Control Applications
– Security-related
• Care Applications
– Wander-alerts
• Convenience Applications
– Key-less access
• Efficiencies
• Effectiveness
• Resource optimisation