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On paradoxes, robots and
autonomous systems -
conceptual model or losing
control?
Speaker: Opher Etzion
Asimov coined the axioms for robotics. These are the assertions that every robot must obey
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The Autonomous Car Paradox
) based on article in Scientific American)
OUTLINE:
1. On Paradox, contradiction and logic
2. On Autonomous Systems
3. On dillemas
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1. On Paradoxes2. On Autonomous Systems3. On Dilemmas
OUTLINE:
OUTLINE:
1. On Paradox, contradiction and logic
2. On Autonomous Systems
3. On dillemas
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On Paradoxes
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Paradoxes: Let’s go to the basics…
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Russell’s paradox: The Barber version
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Bibliographies that quote themselves
Bibliographies that don’t quote themselves
Russell’s paradox: The Librarian version
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These are the assertions that every model must obey
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The Heap Paradox
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Zenon’s Paradox
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The man from Mars allegory
Nuel Belnap
What is the color of this desk?
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Is the desk RED ?
Sure...
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Not really. This
desk is YELLOW
Is the desk RED ?
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I don’t have a clue
Is the desk RED ?
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This desk is GREEN
This desk is RED
Is the desk RED ?
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Nuel Belnap
The man from Mars allegory
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Fuzzy Logic
On Autonomous Systems
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Autonomous systems – analogy to human thinking
Sensing
Making sense of what we sense
Real Time Decision Making
Acting
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IoT and robotics
Robots serve as intelligent actuators – and are becoming autonomous
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Healthcare robotics
Rehabilitation robots: enhancing patients with motoric and cognitive skills
Assistive robots: Robots for independent living of disabled persons
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Some future healthcare
robotics applications
Automated assistance of monitored patients
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Some future healthcare
robotics applications
Help in sit-to-stand and sit-down actions for people with motor disabilities
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Some future healthcare
robotics applications
Autonomous moving of drugs and medical equipment within the hospital
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Some future healthcare
robotics applications
Support of medical staff in various activities
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Some future healthcare
robotics applications
People movement and movement monitoring
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Some future healthcare
robotics applications
People assistance in panic and danger situations
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I(
The classical use of robots are for industrial purposes: production, machinery control, product design…
Industrial Robots
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I(
Autonomic management and coordination of production activities among multiple robots
Industrial Robots and IoT
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I(
Autonomous management of equipment and instruments
Industrial Robots and IoT
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I(
Immediate reaction to critical situations such as: high temperature, harmful chemicals in the air
Industrial Robots and IoT
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I(
Autonomic control of electrical and energy plants
Industrial Robots and IoT
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Robotics for defense
Robots are used for unmanned tools (ground and air) for transport and intelligence , threat detection and combat
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Robotics for defense and IoT
Autonomous and smart detection of harmful chemicals and biological weapons
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Robotics for defense and IoT
Autonomic control of land vehicles and aircrafts
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Robotics for defense and IoT
Identification and access prevention of suspicious people intruding to sensitive places
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Robotics for defense and IoT
Rescue trapped people
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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
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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/pancreas-presentation
Dilemmas
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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….
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Causal inference
How the knowledge about causality is being acquired?
Expert knowledge
Statistical inference
Inference using semantic or association net
Necessity? and relevance?
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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.
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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)
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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).
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
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Sensing can be noisy
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 accuracyof 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 validityof a forecasting pattern
Uncertain about the quality of the decision about traffic lights setting
Sources of uncertainty
Uncertaininput data/
Events
SourceMalfunction
ThermometerHuman error
MaliciousSource
Fake tweet
Sensor disrupter
Projectionof temporalanomalies
Wrong hourly sales summary
SourceInaccuracy
Samplingor
approximation
Propagationof
uncertainty
Visual data
Rumor
Wrong trend
Inference based on uncertain value
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Defense Robots
Unmanned aerial vehicle – Robots that arebeing employed for logistics, intelligence,and combat
In reality –Asimov’s axioms were not adopted
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What would you
do?
BACK TO:
The Autonomous Car Paradox
) based on article in Scientific American)
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Deep Learning is takingover autonomous
systems
Autonomous systems are equipped withself-learning capabilities. This creates asituation where the actual algorithmbehind their activities is not transparent
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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
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The Singularity Dillema
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Who will move to the next phase of evolution?
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Implications on socialequality
Will the majority of humanity stay behind?
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The Solaria Paradox
||autonomous systems|| >>||human beings||
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Progress vs. Employability
What will be the purpose in life? Howshould the time be spent?
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Maybe Asimov was rightand the galaxy should becontrolled by a smartrobot?
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