In this presentation, Prof. Theo Lynn (DCU) was talking about observations on Multi-disciplinary Challenges in Intelligent Systems Research, at the RECAP consortium meeting in Dublin, Ireland on 06 November 2018.

1. Reliable Capacity Provisioning and Enhanced
Remediation for Distributed Cloud Applications
http://recap-project.eu recap2020
THIS PROJECT HAS RECEIVED FUNDING FROM THE EUROPEAN UNION’S HORIZON 2020
RESEARCH AND INNOVATION PROGRAMME UNDER GRANT AGREEMENT NUMBER 732667
Towards the Intelligent Internet of Everything
Observations on Multi-disciplinary Challenges in Intelligent
Systems Research
Theo Lynn, Irish Centre for Cloud Computing and Commerce
Coloquio de Doctorados 2018: Tecnología, Ciencia y Cultura: Una Visión Global
06 November 2018

2. 2
I got us an expert to
help us evolve our
thinking on the Internet
Of Everything.
Blah, blah, blah cognitive architectures
Blah, blah, blah artificial intelligence
Blah, blah, blah trust
Its as if you’re he thinks he’s
a technologist
and philosopher all in one!!!
Combining the Internet of Everything and Intelligent Systems in 50
minutes is not an easy thing!

3. Agenda
• Conceptualising the Internet of Everything
• Intelligent Systems
⁃Architecture Design Principles
⁃Reasoning and Information Processing
• Some Observation on Research Challenges
⁃Ubiquitous Sensing
⁃Cognitive Architectures
⁃Infrastructure
⁃Trust
• Q&A
3

4. The Internet of Everything presages a society where social structures and activities,
to a greater or lesser extent, are organized around digital information networks that
connect people, processes, things, data and social networks.
Cisco, 2017

5. Cost, performance and form factor breakthroughs in sensors
combined with advances in big data and cloud technologies are
creating new value networks and economic opportunities
Src: Dormon, 2014

6. By 2020, Gartner predicts that the
world will contain more than
20 billion IoT devices, representing
less than 1.4% of all physical
objects worldwide
6
Src: Cisco, 2017

7. The value of the IoE to society is
significant. It is estimated
to generate up to US $19
trillion by 2022
• increased asset utilization
and employee productivity,
• improved supply chain and
logistics,
• optimized customer
experience, and
• accelerated innovation
Public
Sector
$4.6tr
Private
Sector
$14.4tr
Src: Cisco, 2013

8. The Internet of Everything represents a significant research
programme even before we make it smart….

9. 9
What do we mean by
intelligent systems?

10. James Albus
Intelligent systems are systems that act
appropriately in an uncertain
environment, where appropriate action is
that which increases the probability of
success, and success is the achievement
of behavioral subgoals that support the
system’s ultimate goal.
Albus & Meystel, 1997

11. Albus & Meystel (1997) conceptualised intelligent systems as four
functional elements –behaviour generation, sensory perception,
world modelling and value judgment
The planning and control of action
designed to achieve behavioral
goals through agents.
The transformation of data from sensors
into meaningful and useful
representations of the world.
• a) the computation of cost, risk, and
benefit of actions and plans,
• b) the estimation of the importance and
value of objects, events, and situations,
• the assessment of reliability of information,
• the calculation of reward or punishment resulting
from perceived states and events.
• Uses sensory input to construct, update, and
maintain a knowledge database.
• Answers queries from behavior generation
regarding the state of the world.
• Simulates results of possible future plans.
• Generates sensory expectations based on
knowledge in the knowledge database.

12. These are supported by a knowledge database that provides a best
estimate of the state of the world and the processes and
relationships that effect events in the word.
• The knowledge database contains (i) state variables, (ii)
entity frames, (iii) event frames, (iv) rules and
equations, (v) images, (vi) maps, and (vii) task
knowledge.
• The knowledge database has both long (static or slowly
varying) and short (dynamic) memory.
• Entities-of-attention are entities that have either been
specified by the current task, or are particularly
noteworthy entities observed in current memory input.

13. A communications systems manages
the interactions between modules,
agents, and nodes.
Complexity through hierarchical
layering and focussed attention.

14. Self-organization is a dynamical and adaptive process where systems
acquire and maintain structure themselves, without external control
De Wolf and Holvoet (2004).
This implies the absence of external control
or interference from outside the boundaries
of the system..
Autonomy
Self-organization is a process from
dynamism towards order.
Dynamical
A self-organizing system must be capable of
maintaining its organization autonomously in the
presence of changes in its environment. It may
generate different tasks but maintain the
behavioral characteristics of its constituent parts.
Adaptability or robustness with
respect to changes
An increase in order (or
statistical complexity), through
organization, is required from
some form of semi-organized
or random initial conditions to
promote a specific function.
Increase in Order

15. 15
Autonomic Computing and Self* Principles
Components and systems continually
seek opportunities to improve their own
performance and efficiency.
Self Optimisation
Self-protection
Automated configuration of
components and systems
follows high-level policies. Rest
of system adjusts automatically
and seamlessly.
Self Configuration
System automatically detects,
diagnoses, and repairs localised
software and hardware
problems.
Self-healing
System automatically defends against
malicious attacks or cascading failures.
It uses early warning to anticipate and
prevent system-wide failures.

16. 16
Architecture specifies
inputs and outputs of
each module and
protocols for
communication
Multi-agent
Systems
Modules read and alter
a shared memory of
beliefs, goals and short
term structures
Functional processes
that operate on
structures including
performance and
learning mechanisms
Modules communicate
directly with each other
No direct
communication between
modules
Representation and
organization of
structures embedded in
memories
Distinct modules for
different facets of an
intelligent system
Distinct modules for
different facets of an
intelligent system
Short-term and long-
term memories that
store the agent’s beliefs,
goals, and knowledge
Architecture places no
constraints on how each
component operates
A programming
language to construct
knowledge-based
systems that embody
the architecture’s
assumptions
Blackboard
Systems
Cognitive
Architectures
Architectural paradigms in intelligent systems

17. 17
Cognitive architectures reason
about problems across different
domains, develop insights, adapt
to new situations
and reflect on themselves.

18. Cognitive architectures make use of a wide variety of
representation and information processing methods but are
trending towards hybrid appoaches

20. Me!
An Intelligent Internet of Everything as a
system of systems that connects people,
processes, things, data, and social
networks and through intelligent systems
proactively creates new value for
individuals, organizations and society as
a whole

21. Sensors play a vital role as an operational technology in IOT/IOE
that gathers data to enable decision making.
Sensors perform a wide variety of
functions with a range of information
utility
Src: AMR, 2016 Src: Gemelli, 2017

22. Ambient intelligence assumes a digitally-infused environment that
proactively, but sensibly, supports people in their daily lives (Ramos et
al. 2008).

23. Ubiquitous sensing research requires a multi-disciplinary and inter-
disciplinary approach
Ubiquitous
Sensing
New materials and detection
methods
Greater understanding of the
underlying bio-physical
mechanisms, activities and
events.
Computational performance
and interoperability
Sensor Evaluation
and Validation

24. 02
Adequate experimental validation and reproducibility of results
Testing in diverse, challenging and realistic environments, real-world situations, more elaborate
scenarios, and diverse tasks. Fuller technical data and access to software and data.
Some cognitive architecture research
priorities (Kotseruba & Tsotsos, 2016)
03
Human-like learning
More robust and flexible learning mechanisms, knowledge transfer, and
accumulation of knowledge without affecting prior learning.
04
Realistic Perception
Advancements in active vision, localization and tracking, performance under
noise and uncertainty, and the use of context information to improve detection
and localization.
01 Autobiographic memory
Episodic memory and lifelong
memory.
05
Comparative evaluation of cognitive architectures
Combination of (i) objective and extensive evaluation procedures and (ii) theoretical
analysis, software testing techniques, benchmarking, subjective evaluation and
challenges, to every aspect of the cognitive architecture and probing multiple abilities.
06
Natural Communications
Advancements in verbal communications including knowledge bases for generating
dialogues, robustness, detection of emotional response and intentions, personalized
responses as well as performing and detecting other non-verbal human
communications.

25. 25
What kind of research does a cloud research centre do on the
Internet of Everything?

26. 26
Traditional cloud infrastructure is built on commoditised resources
and is not optimised for high throughput/performance computing,
heterogeneous processors and end-devices
Simplified operational model for the cloud
service provider, the developer and the end
user through blueprint-as-a-service and
constrained self-organisation.
Novel scheduler architecture enables multiple
scheduling logics and results in significantly
higher task throughput, computational resource
management and energy efficiency.
Increased extensibility to work with new
heterogeneous hardware through a plug & play
service and new applications through blueprint-
as-a-service.
Supports on-premise, private clouds, public
clouds, hybrid clouds and other federated
computing environments.
Greater scalability through novel self-
organising self-managing approach.

27. Gateway
Service
Self Organizing
Self Management System
Plug & Play
Service
Blueprint
Creator
End User
Services
Catalogue
Blueprint Catalogue Enterprise
Cloud
Operator
Gateway
Service
UI
Heterogeneous Resources
New Hardware
Deploy
Service
Service User
Perspective
Monitor
Request
to join
CL-Resource
Discover
Resource
Extract / Modify
Blueprints
Request
Resource
CL-Resources
Deploy Blueprint
Running
Service
Extract
Blueprint
Get
Services
Create
Blueprints
Get
Status
Resource
Handler

28. 28
CloudLightning uses a
combination of self-
organisation, self-
management, and
separation of concerns to
manage complexity in
cloud infrastructure

29. 29
• Something is wrong on your end
• Something is wrong on the Internet
• Something is wrong on the other end

30. IOE / IOT
EDGE
CORE
CLOUD
DATA
CENTRE
RECAP Use Cases
• Use Case A: Infrastructure and
Network Management
• Use Case B: Big Data Analytics
Engine
• Use Case C: Edge/Fog Computing
for Smart Cities
• Use Case D.1: Virtual Content
Distribution Networks
• Use Case D.2: Network Function
Virtualization

31. IOE / IOT
EDGE
CORE
CLOUD
DATA
CENTRE
RECAP Use Cases
• How many instances? Which
hardware for which application
• What data centre should we use?
• Which parts to offload? Where to
offload to? (When to) move parts
around?

32. RECAP is a 3 year project that seeks to use advanced modelling
and analytics to improve network and cloud deployment and
remediating in IOT/IOE scenarios

33. Scale is a huge problem in the target use scenarios requiring new
approaches to simulation.
• Access to the data
• Experiment model size
• Network topology (Graph)
• Infrastructure (Physical
and Virtual)
• Workload
• Application
• Speed and resource demand
• Balance between accuracy
and granularity

34. RECAP Optimisations
34
Decrease
Cost
Increase
Revenue
Reliability
Competitive
Necessity
Provide superior
products and
services
Achieve
heightened
market
penetration
Develop & deploy
services quickly
ICT infrastructure
quality
Digital service
innovation
Cost-efficient
and agile
Relationship
infrastructure
End-to-end service
chain observability
Anomaly detection
and remediation
Infrastructure and
application
reliability
Improved clarity
of costs
Auto-scale
infrastructure
accurately
Faster
software
releases
Reduced
investment
Accurate planning
and forecasting
Reduced
human ICT
support

35. 35

36. There is a significant trade-off between consumer acceptance of
sensing/surveillance and their exploitation through sensing/surveillance
(Acquisti, 2008; Singh & Lyon, 2013).
An interactive realm
wherein every action and
transaction generates
information about itself
Andrejevic, 2007
The ability to sense what
customers will want next,
knowing what they will ask
before they request it.
Franzak et al. 2001
The
Culture
The
Digital
Enclosure

37. What is trust? The intention to accept vulnerability based upon positive
expectations of the intentions or behavior of another (Rousseau et al.
1998)
Trust
Benevolence
Ability
Integrity
Mayer et al. 1995

38. Different interpersonal cues inform our trust behaviour.
3
2
1 Training, Title,
Reputation, Code of
Ethics
Role
02
03Organisation Norms,
Traditions, Practices,
Semiotics
Rules
01Gender, race,
accent, attire
Identity

39. Bio-chemistry also plays a role. Oxytocin increases trust among
humans (Baumgartner et al. 2008)
Mori, 1970

40. Our emotions towards things as they become to look more human-
like is complicated
Mori, 1970

41. Intelligent systems are based on input and training from humans
and the environment can reflect our biases incl. race, gender and
age (Caliskan et al. 2017).

42. The Internet of Everything introduces a wide range of new security
risks and challenges including using connected end-points as attack
vectors

43. Is trust in technology the same as trust in humans?
Helpfulness
Reliability and
Predictability
Functionality and
Performance
Benevolence
Integrity
Ability
McKnight et al. 2011; Sollner et al. 2013

44. There are a lot of trust issues to be resolved in the Internet of
Everything
1. Choice of law/jurisdiction
2. Data location and transfer to
countries outside of the EEA
3. Data integrity and availability
4. Security of data
5. IP Issues
• Copyright (incl. ownership
of metadata)
• Patents and trade secrets
7. Liability and indemnities
8. Acceptable use requirements
9. Service levels and performance
across a complex service chain
10. Variation of contract terms across
providers
14. Monitoring
15. Backup
16. Termination
• Data / application preservation
• Data transfer
• Data deletion

45. Adding intelligence might manage technical complexity but
introduce additional trust complexity

46. What does assurance and accountability mean in an Intelligent
Internet of Everything?

47. Martin Rees
I don’t but worries about AI expanding and
taking over the universe. Our evolution has
required two things: intelligence and a
certain degree of aggression. There is no
reason why machines that are not
intelligent should also be aggressive
Astronomer Royal, Emeritus Professor of
Cosmology and Astrophysics at the University
of Cambridge

48. Vijay Saraswat,
What does the notion of ethics mean for
a machine that does not care whether it
or those around it continue to exist, that
cannot feel, that cannot suffer, that does
not know what fundamental rights are?
Chief Scientist for IBM Compliance Solutions

49. Joseph Stiglitz
Which is the easier way to make a buck:
figuring out a better way to exploit
somebody, or making a better product?
With the new AI, it looks like the answer
is finding a better way to exploit
somebody.
Nobel Laureate, Former Chief Economist, World Bank

50. 50
cuando?

51. We are decades away from having the building blocks for an
Intelligent Internet of Everything
More than 10 Years
• Biotech – cultured or
artificial tissue
• Artificial General
Intelligence
• 4D Printing
• Human Augmentation
• Brain-Computer Interface

52. The timing
for research
is just right
The Goldilocks Dilemma and Internet of Everything Research

53. THANK YOU
http://recap-project.eu recap2020
RECAP Project ■ H2020 ■ Grant Agreement #732667
Call: H2020-ICT-2016-2017 ■ Topic: ICT-06-2016
THIS PROJECT HAS RECEIVED FUNDING FROM THE EUROPEAN UNION’S HORIZON 2020
RESEARCH AND INNOVATION PROGRAMME UNDER GRANT AGREEMENT NUMBER 732667
Theo Lynn
theo.lynn@dcu.ie