We all know that robots are the future, but just how reliable are they? Autonomous cars may soon replace human drivers, but they’re not perfect. A setback occurred in March 2018, when one of Uber’s self-driving cars accidentally hit and killed a pedestrian. After this, the company suspended testing on the technology. Regardless of how accurate the robots are, consumers and industry leaders alike want assurance that they won’t get hurt.
Some people are concerned that AI systems are too complex to explain. For example, the process of credit decision-making involves thousands of variables. However, the algorithm operators may be liable under product liability rules. The penalties could range from civil fines to jail time for major harm. The fatality in Arizona may be a key test case for this question. Arizona actively recruited Uber to test its autonomous vehicles and granted it considerable latitude for road testing.
Reliability is one of the most valuable use cases for enterprise AI. The benefits of AI-based reliability applications are measurable. For example, modern AI machine-learning techniques are capable of learning the behavior of a wide variety of asset types and environments. They can also build models for failure and alert operators of preventive maintenance. Once trained, the model will continually learn how to predict asset failure and act accordingly. It can even learn the type of failure that will occur next.
The newest developments in AI technology include the use of facial recognition technology and the development of artificial intelligence. AI-powered robots can now interpret video feeds from drones and understand customer service queries. They can coordinate with other intelligent systems and even diagnose tumors. They can also recognize inappropriate content online, detect wear and tear in elevators, and create 3D models of the world. It’s not clear if AI technology will replace humans shortly, but it is a major step in that direction.
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