What are AI hallucinations? Computer expert breaks down why it happens, how to avoid it
More internet users are starting to replace popular search engines with advanced chatbots from artificial intelligence platforms. However, the more powerful they become, the more mistakes they're making, the New York Times reported. These mistakes are referred to as hallucinations.
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Hallucinations have even been at the centre of a recent case in Canada involving a lawyer accused of using AI and fake cases to make legal arguments. A Ontario Superior Court judge said the lawyer's factum, or statement of facts about the case, included what the judge believed to be 'possibly artificial intelligence hallucinations.'
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There's also been a surge in the amount of Canadians using AI recently, new data released by Leger on Wednesday revealed. Nearly half of the Canadians surveyed (47 per cent) in March said they've used AI tools, compared to only a quarter saying the same in February 2023.
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Canadians are more likely to trust AI tools when it comes to tasks around the home, answering product questions via chat, or for using facial recognition for access. Canadians are much less trusting when it comes to using AI for driverless transport, teaching children or getting help to find a life partner. Canadians were split on whether AI is good (32 per cent) or bad (35 per cent) for society.
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What are AI hallucinations?
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An AI hallucination is when a chatbot presents a response as true, but it is not correct.
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This can occur because AI chatbots are not 'explicitly programmed,' said University of Toronto professor David Lie from the department of electrical and computer engineering in a phone interview with National Post on Tuesday. Lie is also the Canada Research Chair in Secure and Reliable Systems.
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'The programmer beforehand doesn't think of every possible question and every possible response that the AI could face while you're using it,' he said. Therefore, the chatbots rely on inferences from the training data. Those inferences can be incorrect for a multitude of reasons. The training data may be incomplete or the training method leads it to the wrong shortcuts to arrive at the answer.
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He compared how the current generation of artificial intelligences are modelled to the human brain.
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'The way they're trained is, you give a bunch of examples … trillions of them. And from that, it learns how to mimic, very much like how you would teach a human child,' said Lie. 'When we learn things, we often make mistakes, too, even after lots and lots of learning. We may come to the wrong conclusions about things and have to be corrected.'
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