As AI systems start to dominate the marketplace, concerns regarding accuracy and precision are becoming more prevalent. The convenience of these systems is undeniable: They can answer complex questions in minutes, save us time and help us create content. But what if the information you're relying on isn't just wrong—it's completely fabricated? AI models are designed to sound right even when they're shooting from the hip, so they can be extremely convincing. They often present information to justify their position, making it difficult to distinguish fact from fiction. This raises another question: Can you trust AI with complex, high-stakes tasks?
What causes hallucinations?
These errors or—as they're called in the industry, hallucinations—are often attributed to knowledge gaps caused by the parameters and information loaded into the system. What’s often overlooked is the fact that AI is designed to keep you coming back for more by, in short, making you happy.
In the case of knowledge gaps, you can train AI to successfully identify the make and model of a vehicle on vast amounts of images, but it may identify other items as a vehicle because it doesn't have context. In the case of making its users happy, if the user doesn’t point out that the returned information is wrong, the AI will not acknowledge the strength of its results or, in some cases, even deny it made a mistake.
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