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Missouri University of Science and Technology Given the choice of riding in an Uber driven by a human or a self-driving version, which would you choose?
Despite these recent incidents, Siau sees a strong future for AI, but one fraught with trust issues that must be resolved. In their article, Siau and Wang examine prevailing concepts of trust in general and in the context of AI applications and human-computer interaction. They discuss the three types of characteristics that determine trust in this area — human, environment and technology — and outline ways to engender trust in AI applications.
Siau and Wang point to five areas that can help build initial trust in artificial intelligence systems: The more "human" a technology is, the more likely humans are to trust it.
Perhaps first-generation autonomous vehicles should have a humanoid "chauffeur" behind the wheel to help ease concerns. Science fiction books and movies have given AI a bad image, Siau says.
Reviews from other users. People tend to rely on online product reviews, and "a positive review leads to greater initial trust.
The ability to test a new AI application before being asked to adapt it leads to greater acceptance, Siau says. How to maintain trust in AI Beyond developing initial trust, however, creators of AI also must work to maintain that trust. Siau and Wang suggest seven ways of "developing continuous trust" beyond the initial phases of product development: AI "should be designed to operate easily and intuitively," Siau and Wang write.
AI developers want to create systems that perform autonomously, without human involvement. Developers must focus on creating AI applications that smoothly and easily collaborate and communicate with humans. Building social activities into AI applications is one way to strengthen trust.
A robotic dog that can recognize its owner and show affection is one example, Siau and Wang write.
Security and privacy protection. AI applications rely on large data sets, so ensuring privacy and security will be crucial to establishing trust in the applications. Just as transparency is instrumental in building initial trust, interpretability — or the ability for a machine to explain its conclusions or actions — will help sustain trust.
As concerns about AI replacing humans on the job continue to grow, policies must be put in place to provide retraining and education to those affected by this trend.
But in this unsettling environment, higher education can play a significant role.I'm using Delphi and Windows 10, with all current updates.
My application uses and re-uses a large number of dynamic arrays, 50 or more, each having up to 5, elements. During execution, t. April 20, in stoner folklore, is a day of celebration—as well as a 12 percent increase in fatal car crashes.
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Topics range from number theory to relativity to how to study calculus. There are few situations more fluid and dynamic driving a car in traffic. If you lose SA on the LA Freeway you are headed for trouble.
In closing I must reiterate my opinion that most accidents, though preventable and tragic, are still unintentional therefore the term accident seems appropriate to me. But I have a problem with the dynamic array I don't understand. First problem: As soon as I try to enter a "big" value for n (for example ), the program crashes, it doesn't even allocate the memory.