This document discusses how much artificial intelligence can be trusted. It begins with an introduction that defines artificial intelligence and discusses major techniques like machine learning. It then reviews literature on incidents where AI systems caused harm, like a Tesla crash or a robot killing a Volkswagen employee. The document considers how much AI systems can be trusted given that they are based on algorithms and can have security problems or unintended biases. It concludes that while AI offers benefits, its future is unknown and work is needed to develop trusted algorithms and ensure new technologies are developed safely.

1. How Much to Trust
Artificial Intelligence?
Guided by:
Ms. AMALA RAJAN
Assistant Professor
ECE dept, CCET
Presented by:
ANAS .M
(COAOEEC001)

2. INTRODUCTION
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What is artificial intelligence (AI)?
Can machine be intelligent?
Dark sides of AI.
We are victims of AI.

3. ARTIFICIAL INTELLIGENCE
Using computers to solve problems or make
automated decisions.
Major artificial intelligence techniques:-
-Rules and logic based approaches.
-Machine learning based approaches.
-Hybrid systems.
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4. WARNINGS!
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5. LITERATURE REVIEW
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Tesla’s Self-Driving System Cleared in Deadly
Crash.
Robot killed volkswagen employee.
Facebook shuts down robots after they invent
their own language.
 Robot became a Saudi Arabian citizen.

6. LITERATURE REVIEW (cont.)
 Non-player characters in a video game crafted
weapons beyond creator's plans.
 Russian robot makes break for freedom.
 Apple's Face ID fails to distinguish between
users.
 “I will destroy humans”- Sophia
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7. HOW MUCH TO TRUST AI?
AI works based on various algorithms.
Required efficient and accurate algorithms.
Testing and practices.
Security problems.
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8. MACHINE LEARNING UPS THE
TRUST ANTE
AI is a high-order construct
Tester must establish the basis for multiple
factors, such as:-
– Programmatic consistency
– Repeatability
– Applied path tracing
– Identifiable systemic failure modes.
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9. BEYOND THE CODE
Extensive datasets are required to “train” the
AI.
Intentional or unintentional cultural or personal
bias into AI code.
Noises and interferences.
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10. THE THIRD WAVE
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Complex Adaptive Systems (CAS).
Controlling of individual parts.
There Are Better Ways of Judging AI.

11. FUTURE DIRECTIONS
Future of AI is unknown!!!
Self programmed robots.
Cyborgs and other intelligent robots.
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12. ADVANTAGES
• Reduces human work force.
• Capability to create smart world.
• Error and difficulty Reduction.
• Fast response.
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13. CHALLENGES
Un employment.
Unpredictable outcomes.
Responsibility of system.
Hacking !!!
Biased than humans.
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14. CONCLUSION
There are benefits and dark sides to every
disruptive technology.
Needed trusted algorithms for AI.
Robustness of an MLA’s capabilities.
The robots are coming. Let’s make sure they
come in ‘peace’.
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15. REFERENCE
[1]George Hurlburt, STEMCorp- “How much to trust artificial intelligence”,
Published by the IEEE Computer Society, 1520-9202/17/$33.00 © 2017 IEEE
[2]N.E. Boudette, “Tesla’s Self-Driving System Cleared in Deadly Crash,” New
York Times, 19 Jan. 2017.
[3]N. Cardozo, P. Eckersley, and J. Gillula, “Does DARPA’s Cyber Grand
Challenge Need a Safety Protocol?” Electronic Frontier Foundation,4 Aug.
2016; bit.ly/2aPxRXc.
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