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Still other people are disappointed that companies they invested in went bankrupt Basic Questions, The main thing that people appear to be afraid of is that AI may take over decisions that they think should be made by human beings, for example driving cars or powered wheelchairs and aiming and firing missiles.

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These can be life-and-death decisions, and they are engineering as well as ethical problems. If an AI system makes a decision that we regret, then we change its algorithms.

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If AI systems make decisions that our society or our laws do not approve of, then we will modify the principles that govern them or create better ones. Of course, human engineers make mistakes and intelligent machines will make mistakes too, even big mistakes. Like humans, we need to keep monitoring, teaching, and correcting them. There will be a lot of scrutiny on the actions of artificial systems, but a wider problem is that we do not have a consensus on what is appropriate. There is a distinction to be made between machine intelligence and machine decision making.

We should not be afraid of intelligent machines but of machines making decisions that they do not have the intelligence to make. As for human beings, it is machine stupidity that is dangerous and not machine intelligence Bishop, A problem is that intelligent control algorithms can make very many good decisions and then one day make a very foolish decision and fail spectacularly because of some event that never appeared in the training data. That is the problem with bounded intelligence. We should fear our own stupidity far more than the hypothetical brilliance or stupidity of algorithms yet to be invented.

There is no metric for intelligence or benchmark for particular kinds of learning and smartness, and so it is difficult to know if we are improving Kelly, We definitely do not have any ruler to measure the continuum of intelligence.

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AI does appear to be becoming more useful in control engineering though, partly through trial and error and removing control algorithms and machines that do not work. As AI systems make mistakes, we can decide what is acceptable. Since AI systems are assuming some tasks that humans do, we have much to teach them.

Without this teaching and guidance they would be frightening as can many engineering systems , but as control engineers, we can provide that teaching and guidance. As humans, we know the physical world only through a neurologically generated virtual model that we consider to be reality. Even our life history and memory is a neurological construct.

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Our brains generate the narratives that we live by. These narratives are imprecise, but good enough for us to blunder along. Although we may be bested on specific tasks, overall, we tend to fare well in competition against machines. Machines are very far from simulating our flexibility, cunning, deception, anger, fear, revenge, aggression, and teamwork Brockman, While respecting the narrow, chess-playing prowess of Deep Blue, we should not be intimidated by it.

In fact, intelligent machines have helped human beings to become better chess players. As AI develops, we might have to engineer ways to prevent consciousness in them just as we engineer control systems to be safe.

After all, even with Watson or Deep Blue, anyone can pull its plug and beat it into rubble with a hammer Provine, …or can we? Hoske, content manager, Control Engineering , mhoske cfemedia. More frequent control software upgrades may be more advantageous as programs use more useful AI principles. This page approximate paper equivalent online article is summarized in the Control Engineering February issue.

See below for related links and reference, including a December article by Sanders, "Artificial intelligence tools can aid sensor systems. Artificial intelligence tools can aid sensor systems. At least seven artificial intelligence AI tools can be useful when applied to sensor systems: knowledge-based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case-based reasoning, and ambient-intelligence. See diagrams. Artificial intelligence: Fuzzy logic explained. Fuzzy logic is a rule-based system that can rely on the practical experience of an operator, particularly useful to capture experienced operator knowledge.

Bar-Cohen Y Actuation of biologically inspired intelligent robotics using artificial muscles. Industrial Robot: An International Journal. Basic Questions Basic Questions-What Is Artificial intelligence? Stanford University. Accessed 15 January Bellman R Bellman RE Dynamic Programming. Intelligent browser-based systems to assist Internet users. IEEE Transactions 48 4 , pp GOLD: A framework for developing intelligent-vehicle vision applications. Bishop M Fear artificial stupidity, not artificial intelligence.

New Scientist. Bouganis A, and Shanahan M A vision-based intelligent system for packing 2-D irregular shapes. Brackenbury I, Ravin Y Machine intelligence and the Turing Test.

IBM Systems Journal. Brockman J Brooks R Cannon W Wisdom of the Body. United States: W. ISBN Cannon WB Bodily changes in pain, hunger, fear, and rage. New York: Appleton-Century-Crofts. Real time generation of humanoid robot optimal gait for going upstairs using intelligent algorithms. Towards an intelligent medical system for the aesthetic evaluation of breast cancer conservative treatment.

AI in Medicine. Integrated Principles of Zoology: Fourteenth Edition.

Crevier D Dingle N Control Engineering. Dyson G Space Hippo.

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Feigenbaum E Gegov A Application of computational intelligence methods for intelligent modelling of buildings. Applications and science in soft computing. Intelligent assembly modelling and simulation, Assembly Automation. ISSN: , Vol. Assembly Automation. ISSN: Aspects of an expert design system for the wastewater treatment industry. Journal of Systems Architecture 43 : Hurwitz Ingham R and Mollard P Science Magazine Kandel ER Appleton and Lange: McGraw Hill, pp Kelly K Intelligent robot software architecture.

Published in lecture notes in control and information sciences, Vol. Kress M Intelligent Internet knowledge networks: Processing of concepts and wisdom. Kucera V Lanier J Legg S, Hutter, M Universal intelligence: A definition of machine intelligence.

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Minds and Machines. Lighthill, James Science Research Council. Masi CG Fuzzy Neural Control Systems-Explained. Maxwell From the Proceedings of the Royal Society, No.