This document discusses paradoxes, autonomous systems, and the dilemmas they present. It covers paradoxes like Russell's paradox and Zeno's paradox to illustrate logical contradictions. It then outlines how autonomous systems work by sensing their environment, making sense of inputs, decision-making, and acting. Examples of current and future applications of healthcare, industrial, and military robotics are provided. However, it also notes the dangers of causal inference from correlation and sources of uncertainty. Deep learning allows autonomous systems to self-learn but lacks transparency. This could enable remote killing and raises issues around social equality if advanced systems surpass human

1. On paradoxes, robots and
autonomous systems -
conceptual model or losing
control?
Speaker: Opher Etzion

2. Asimov coined the
axioms for robotics.
These are the assertions
that every robot must
obey

3. 3

4. 4

5. 5
The Autonomous Car
Paradox
) based on article in
Scientific American)

6. OUTLINE:
1. On Paradox, contradiction and logic
2. On Autonomous Systems
3. On dillemas
6
1. On Paradoxes
2. On Autonomous Systems
3. On Dilemmas
OUTLINE:

7. OUTLINE:
1. On Paradox, contradiction and logic
2. On Autonomous Systems
3. On dillemas
7
On Paradoxes

8. 8
Paradoxes: Let’s go
to the basics…

9. 9
Russell’s paradox:
The Barber
version

10. 10
Bibliographies
that quote
themselves
Bibliographies
that don’t quote
themselves
Russell’s paradox:
The Librarian
version

11. 11
These are the assertions
that every model must
obey

12. 12

13. 13

14. 14
The Heap
Paradox

15. 15
Zenon’s Paradox

16. 16
The man from
Mars allegory
Nuel Belnap
What is the color
of this desk?

17. 17
Is the desk RED ?
Sure...

18. 18
Not really.
This
desk is
YELLOW
Is the desk RED ?

19. 19
I don’t
have a
clue
Is the desk RED ?

20. 20
This desk
is
GREEN
This desk
is RED
Is the desk RED ?

21. 21
Nuel Belnap
The man from
Mars allegory

22. 22
Fuzzy Logic
On Autonomous
Systems

23. 23
Autonomous systems – analogy to human thinking
Sensing
Making
sense of
what we
sense
Real Time
Decision
Making
Acting

24. 24
IoT and robotics
Robots serve as intelligent actuators – and
are becoming autonomous

25. 25
Healthcare robotics
Rehabilitation robots: enhancing patients
with motoric and cognitive skills
Assistive robots: Robots for independent
living of disabled persons

26. 26
Some future healthcare
robotics applications
Automated assistance of monitored
patients

27. 27
Some future healthcare
robotics applications
Help in sit-to-stand and sit-down actions
for people with motor disabilities

28. 28
Some future healthcare
robotics applications
Autonomous moving of drugs and medical
equipment within the hospital

29. 29
Some future healthcare
robotics applications
Support of medical staff in various
activities

30. 30
Some future healthcare
robotics applications
People movement and movement monitoring

31. 31
Some future healthcare
robotics applications
People assistance in panic and danger
situations

32. 32
I(
The classical use of robots are for
industrial purposes: production,
machinery control, product design…
Industrial Robots

33. 33
I(
Autonomic management and coordination of
production activities among multiple
robots
Industrial Robots and
IoT

34. 34
I(
Autonomous management of equipment and
instruments
Industrial Robots and
IoT

35. 35
I(
Immediate reaction to critical situations
such as: high temperature, harmful
chemicals in the air
Industrial Robots and
IoT

36. 36
I(
Autonomic control of electrical and energy
plants
Industrial Robots and
IoT

37. 37
Robotics for defense
Robots are used for unmanned tools
(ground and air) for transport and
intelligence , threat detection and combat

38. 38
Robotics for defense and IoT
Autonomous and smart detection of
harmful chemicals and biological weapons

39. 39
Robotics for defense and IoT
Autonomic control of land vehicles and
aircrafts

40. 40
Robotics for defense and IoT
Identification and access prevention of
suspicious people intruding to sensitive
places

41. 41
Robotics for defense and IoT
Rescue trapped people

42. 42
Smart pacemaker
A pacemaker is a
small device
that's placed in
the chest or
abdomen to
help control
abnormal heart
rhythms. This
device uses
electrical
pulses to
prompt the
heart to beat
at a normal
rate.
Implants for cardiovascular diseases
source:
https://www.cambridgeconsultants.com/media
/press-releases/setting-pace

43. 43
Sensors and
actuators –
technology still
under
development
Goal:
to improve insulin
replacement
therapy until
glycemic
control is
practically
normal, and to
ease the
burden of
therapy for the
insulin-
dependent.
Implants for diabetes patients
Source:
https://www.slideshare.net/energexsystems/p
ancreas-presentation

44. Dilemmas
44

45. 45
Causality
In order to derive conclusions from facts
and events there is a need to identify
causalities.
Statistical methods can infer correlations.
Causality inference is more tricky….

46. 46
Causal inference
How the knowledge about causality is being
acquired?
Expert knowledge
Statistical inference
Inference using semantic or association net
Necessity? and relevance?

47. 47
Dangers of using correlation as causality
indicator
Correlation between A and B:
1. A causes B
2. B causes A
3. There is C which causes both A and B
4. A combination of all three
interpretations
The faster windmills are observed to
rotate, the more wind is observed to be.
Therefore wind is caused by the rotation of
windmills.

48. 48
Dangers of using correlation as causality
indicator
Correlation between A and B:
1. A causes B
2. B causes A
3. There is C which causes both A and B
4. A combination of all three
interpretations
Sleeping with one's shoes on is strongly
correlated with waking up with a
headache.
Therefore, sleeping with one's shoes on
causes headache.
(correct answer: going to bad drunk causes
both)

49. 49
Dangers of using correlation as causality
indicator
Correlation between A and B:
1. A causes B
2. B causes A
3. There is C which causes both A and B
4. A combination of all three
interpretations
As ice cream sales increase, the rate of
drowning deaths increases sharply.
Therefore, ice cream consumption causes
drowning. (real answer: they are both in
the same context – summer).

50. False positives and negatives
False positive:
The pattern is matched;
The real-world situation
does not occur
False negative:
The pattern is not matched;
The real-world situation
occurs
Learning
from
experience

51. 51
Sensing can be noisy

52. What are we uncertain of?
Uncertain whether a reported
event has occurred (e.g.
accident)
Uncertain what really
happened. What is the type
and magnitude of the
accident (vehicles involved,
casualties)
Uncertain when an event
occurred (will occur): timing
of forecasted congestion
Uncertain where an event
occurred (will occur):
location of forecasted
congestion
Uncertain about the level of
causality between a car
heading towards highway
and a car getting into the
highway
Uncertain about the accuracy
of a sensor input: count of
cars, velocity of cars…
The pattern: more
than 100 cars
approach an area
within 5 minutes after
an accident derives a
congestion forecasting
Uncertain about the validity
of a forecasting pattern
Uncertain about the quality
of the decision about traffic
lights setting

53. Sources of uncertainty
Uncertain
input data/
Events
Source
Malfunction
Thermomete
r
Human error
Malicious
Source
Fake tweet
Sensor
disrupter
Projection
of temporal
anomalies
Wrong hourly
sales summary
Source
Inaccuracy
Sampling
or
approximation
Propagation
of
uncertainty
Visual data
Rumor
Wrong trend
Inference based
on uncertain
value

54. 54
Defense Robots
Unmanned aerial vehicle – Robots that are
being employed for logistics, intelligence,
and combat
In reality –
Asimov’s axioms
were not
adopted

55. 55
What
would you
do?
BACK TO:
The Autonomous Car
Paradox
) based on article in
Scientific American)

56. 56
Deep Learning is taking
over autonomous
systems
Autonomous systems are equipped with
self-learning capabilities. This creates a
situation where the actual algorithm
behind their activities is not transparent

57. 57
Murder by the Internet
“With so many devices being Internet connected, it makes murdering
people remotely relatively simple, at least from a technical
perspective. That’s horrifying,” said IID president and CTO Rod
Rasmussen. “Killings can be carried out with a significantly lower
chance of getting caught, much less convicted, and if human history
shows us anything, if you can find a new way to kill, it will be
eventually be used.”
EXAMPLES: Turn off pacemakers, Shutdown car systems while
driving, stop IV drip from functioning
Safe vs. connected

58. 58
The Singularity
Dillema

59. 59
Who will move to the next phase of evolution?

60. 60
Implications on social
equality
Will the majority of humanity stay
behind?

61. 61
The Solaria Paradox
||autonomous systems|| >>
||human beings||

62. 62
Progress vs.
Employability
What will be the purpose in life? How
should the time be spent?

63. 63
Maybe Asimov was right
and the galaxy should be
controlled by a smart
robot?