A review of the article written by Dr. David Parnas. Reference "Parnas, D.L., 2017. The real risks of artificial intelligence. Communications of the ACM, 60(10), pp.27-31. "

1. Review: The Real Risks of
Artificial Intelligence
Udaka Ayas Manawadu
1
Parnas, D.L., 2017. The real risks of artificial intelligence. Communications of the ACM, 60(10), pp.27-31.

2. Contents
2
ABOUT THE AUTHOR NOTABLE HIGHLIGHTS
OF AI AS DAVID
PARNAS
REAL RISKS OF AI CONCLUSIONS

3. About the Author :
David Lorge Parnas
• Experience in Computer science field
since 1950s
• Known for publishing widely cited
papers.
• Professor Emeritus at McMaster
University in Hamilton Canada, and at
the University of Limerick Ireland.
3References: https://www.computer.org/profiles/david-parnas

4. AI as David Parnas sees
• “Artificial intelligence” remains a buzzword, a word that
many think they understand but nobody can define.
• Application of AI methods can lead to devices and
systems that are untrustworthy and sometimes
dangerous.
• Can heuristic programming use to solve AI problems?
4

5. Notable Highlights of AI as David Parnas
5
ALAN TURING
ABOUT AI
JOSEPH
WEIZENBAUM’S
ELIZA
ROBERT DUPCHAK’S
PENNY-MATCHER
CHARACTER/ IMAGE
RECOGNITION
PROBLEMS
ARTIFICIAL NEURAL
NETWORKS
MACHINE LEARNING

6. 6
Alan Turing about AI Joseph Weizenbaum’s Eliza
• He understood that science requires agreement
on how to measure the properties for machine
intelligence.
• Most of Turing’s paper was not about either
machine intelligence or thinking; it discussed
how to test whether or not a machine had some
well-specified property
• Considered as the First chatbot of the world.
• Added psychology to the chats.
• Some “patients” believed they were dealing
with a person.
Notable Highlights of AI as David Parnas

7. 7
Robert Dupchak’s Penny-Matcher Character/ Image Recognition
Problems
• The machine that only remembered past moves
by its opponent and assumed that patterns
would repeat. (Heads or tail)
• The goal was to write programs that could
identify hand-drawn or printed characters
• Most humans can read a text, in a new font
without studying its characteristics, but
machines often cannot.
Notable Highlights of AI as David Parnas

8. 8
Artificial Neural Networks Machine Learning
• Researchers try to produce AI by imitating the
structure of a brain.
• Construct programs that have minimal initial
capability but improve their performance during
use
• Machine-learning algorithms are heuristic and
may fail in unusual situations
Notable Highlights of AI as David Parnas

9. The real Risks?
• We cannot trust a device unless we know how it works.
• Some AI programs almost always work and are dangerous because we
learn to depend on them.
• Modern computer systems use powerful sensors and remote
actuators than Human.
• We need machines that do things that people can’t do, won’t do, or
don’t do well
• Instead of asking “Can a computer win Turing’s imitation game?” we
should be studying more specific questions such as “Can a computer
system safely control the speed of a car when following another car?”
9

10. Conclusions of the paper.
• A Clear Definition on AI should be implemented.
• The real Question is “Can Machines perform specific task under a
well-defined way?” not “Can machines think?”
• It is questionable whether AIs will treat us ethically.
10

11. Thank You. 11
Even if we could keep the machines in a subservient position, for instance by turning off the power at
strategic moments, we should, as a species, feel greatly humbled.
Alan Turing, 1951