AI Hallucinations: The Challenges and Risks of Artificial Intelligence's Misinformation Problem

What are AI hallucinations? Why AIs sometimes make things up

When someone sees something that isn't there, people often refer to the experience as a hallucination. Hallucinations occur when your sensory perception does not correspond to external stimuli. Technologies that rely on artificial intelligence can have hallucinations, too. When an algorithmic system generates information that seems plausible but is actually inaccurate or misleading, computer scientists call it an AI hallucination. Researchers have found these behaviors in different types of AI systems, from chatbots such as ChatGPT to image generators such as Dall-E to autonomous vehicles. We are information science researchers who have studied hallucinations in AI speech recognition systems. Wherever AI systems are used in daily life, their hallucinations can pose risks. Some may be minor - when a chatbot gives the wrong answer to a simple question, the user may end up ill-informed. But in other cases, the stakes are much higher. From courtrooms where AI software is used to make sentencing decisions to health insurance companies that use algorithms to determine a patient's eligibility for coverage, AI hallucinations can have life-altering consequences. They can even be life-threatening: Autonomous vehicles use AI to detect obstacles, other vehicles and pedestrians. Making it up Hallucinations and their effects depend on the type of AI system. With large language models - the underlying technology of AI chatbots - hallucinations are pieces of information that sound convincing but are incorrect, made up or irrelevant. An AI chatbot might create a reference to a scientific article that doesn't exist or provide a historical fact that is simply wrong, yet make it sound believable. In a 2023 court case, for example, a New York attorney submitted a legal brief that he had written with the help of ChatGPT. A discerning judge later noticed that the brief cited a case that ChatGPT had made up. This could lead to different outcomes in courtrooms if humans were not able to detect the hallucinated piece of information. With AI tools that can recognize objects in images, hallucinations occur when the AI generates captions that are not faithful to the provided image. Imagine asking a system to list objects in an image that only includes a woman from the chest up talking on a phone and receiving a response that says a woman talking on a phone while sitting on a bench. This inaccurate information could lead to different consequences in contexts where accuracy is critical. What causes hallucinations Engineers build AI systems by gathering massive amounts of data and feeding it into a computational system that detects patterns in the data. The system develops methods for responding to questions or performing tasks based on those patterns. Supply an AI system with 1,000 photos of different breeds of dogs, labeled accordingly, and the system will soon learn to detect the difference between a poodle and a golden retriever. But feed it a photo of a blueberry muffin and, as machine learning researchers have shown, it may tell you that the muffin is a chihuahua. When a system doesn't understand the question or the information that it is presented with, it may hallucinate. Hallucinations often occur when the model fills in gaps based on similar contexts from its training data, or when it is built using biased or incomplete training data. This leads to incorrect guesses, as in the case of the mislabeled blueberry muffin. It's important to distinguish between AI hallucinations and intentionally creative AI outputs. When an AI system is asked to be creative - like when writing a story or generating artistic images - its novel outputs are expected and desired. Hallucinations, on the other hand, occur when an AI system is asked to provide factual information or perform specific tasks but instead generates incorrect or misleading content while presenting it as accurate. The key difference lies in the context and purpose: Creativity is appropriate for artistic tasks, while hallucinations are problematic when accuracy and reliability are required. To address these issues, companies have suggested using high-quality training data and limiting AI responses to follow certain guidelines. Nevertheless, these issues may persist in popular AI tools. What's at risk The impact of an output such as calling a blueberry muffin a chihuahua may seem trivial, but consider the different kinds of technologies that use image recognition systems: An autonomous vehicle that fails to identify objects could lead to a fatal traffic accident. An autonomous military drone that misidentifies a target could put civilians' lives in danger. For AI tools that provide automatic speech recognition, hallucinations are AI transcriptions that include words or phrases that were never actually spoken. This is more likely to occur in noisy environments, where an AI system may end up adding new or irrelevant words in an attempt to decipher background noise such as a passing truck or a crying infant. As these systems become more regularly integrated into health care, social service and legal settings, hallucinations in automatic speech recognition could lead to inaccurate clinical or legal outcomes that harm patients, criminal defendants or families in need of social support. Check AI's work Regardless of AI companies' efforts to mitigate hallucinations, users should stay vigilant and question AI outputs, especially when they are used in contexts that require precision and accuracy. Double-checking AI-generated information with trusted sources, consulting experts when necessary, and recognizing the limitations of these tools are essential steps for minimizing their risks.

The Weird World of AI Hallucinations

Serving tech enthusiasts for over 25 years. TechSpot means tech analysis and advice you can trust. When you buy through our links, we may earn a commission. When someone sees something that isn't there, people often refer to the experience as a hallucination. Hallucinations occur when your sensory perception does not correspond to external stimuli. Technologies that rely on artificial intelligence can have hallucinations, too. When an algorithmic system generates information that seems plausible but is actually inaccurate or misleading, computer scientists call it an AI hallucination. Editor's Note: Guest authors Anna Choi and Katelyn Xiaoying Mei are Information Science PhD students. Anna's work relates to the intersection between AI ethics and speech recognition. Katelyn's research work relates to psychology and Human-AI interaction. This article is republished from The Conversation under a Creative Commons license. Researchers and users alike have found these behaviors in different types of AI systems, from chatbots such as ChatGPT to image generators such as Dall-E to autonomous vehicles. We are information science researchers who have studied hallucinations in AI speech recognition systems. Wherever AI systems are used in daily life, their hallucinations can pose risks. Some may be minor - when a chatbot gives the wrong answer to a simple question, the user may end up ill-informed. But in other cases, the stakes are much higher. At this early stage of AI development, the issue isn't just with the machine's responses - it's also with how people tend to accept them as factual simply because they sound believable and plausible, even when they're not. We've already seen cases in courtrooms, where AI software is used to make sentencing decisions to health insurance companies that use algorithms to determine a patient's eligibility for coverage, AI hallucinations can have life-altering consequences. They can even be life-threatening: autonomous vehicles use AI to detect obstacles: other vehicles and pedestrians. Hallucinations and their effects depend on the type of AI system. With large language models, hallucinations are pieces of information that sound convincing but are incorrect, made up or irrelevant. A chatbot might create a reference to a scientific article that doesn't exist or provide a historical fact that is simply wrong, yet make it sound believable. In a 2023 court case, for example, a New York attorney submitted a legal brief that he had written with the help of ChatGPT. A discerning judge later noticed that the brief cited a case that ChatGPT had made up. This could lead to different outcomes in courtrooms if humans were not able to detect the hallucinated piece of information. With AI tools that can recognize objects in images, hallucinations occur when the AI generates captions that are not faithful to the provided image. Imagine asking a system to list objects in an image that only includes a woman from the chest up talking on a phone and receiving a response that says a woman talking on a phone while sitting on a bench. This inaccurate information could lead to different consequences in contexts where accuracy is critical. Engineers build AI systems by gathering massive amounts of data and feeding it into a computational system that detects patterns in the data. The system develops methods for responding to questions or performing tasks based on those patterns. Supply an AI system with 1,000 photos of different breeds of dogs, labeled accordingly, and the system will soon learn to detect the difference between a poodle and a golden retriever. But feed it a photo of a blueberry muffin and, as machine learning researchers have shown, it may tell you that the muffin is a chihuahua. Object recognition AIs can have trouble distinguishing between chihuahuas and blueberry muffins and between sheepdogs and mops. When a system doesn't understand the question or the information that it is presented with, it may hallucinate. Hallucinations often occur when the model fills in gaps based on similar contexts from its training data, or when it is built using biased or incomplete training data. This leads to incorrect guesses, as in the case of the mislabeled blueberry muffin. It's important to distinguish between AI hallucinations and intentionally creative AI outputs. When an AI system is asked to be creative - like when writing a story or generating artistic images - its novel outputs are expected and desired. Hallucinations, on the other hand, occur when an AI system is asked to provide factual information or perform specific tasks but instead generates incorrect or misleading content while presenting it as accurate. The key difference lies in the context and purpose: Creativity is appropriate for artistic tasks, while hallucinations are problematic when accuracy and reliability are required. To address these issues, companies have suggested using high-quality training data and limiting AI responses to follow certain guidelines. Nevertheless, these issues may persist in popular AI tools. The impact of an output such as calling a blueberry muffin a chihuahua may seem trivial, but consider the different kinds of technologies that use image recognition systems: an autonomous vehicle that fails to identify objects could lead to a fatal traffic accident. An autonomous military drone that misidentifies a target could put civilians' lives in danger. For AI tools that provide automatic speech recognition, hallucinations are AI transcriptions that include words or phrases that were never actually spoken. This is more likely to occur in noisy environments, where an AI system may end up adding new or irrelevant words in an attempt to decipher background noise such as a passing truck or a crying infant. As these systems become more regularly integrated into health care, social service and legal settings, hallucinations in automatic speech recognition could lead to inaccurate clinical or legal outcomes that harm patients, criminal defendants or families in need of social support. Check AI's Work - Don't Trust - Verify AI Regardless of AI companies' efforts to mitigate hallucinations, users should stay vigilant and question AI outputs, especially when they are used in contexts that require precision and accuracy. Double-checking AI-generated information with trusted sources, consulting experts when necessary, and recognizing the limitations of these tools are essential steps for minimizing their risks.

We're already trusting AI with too much - I just hope AI hallucinations disappear before it's too late

I was talking to an old friend about AI - as one often does whenever engaging in causal conversation with anyone these days - and he was describing how he'd been using AI to help him analyze insurance documents. Basically, he was feeding almost a dozen documents into the system to summarize or maybe a pair of lengthy policies to compare changes. This was work that could take him hours, but in the hands of AI (perhaps ChatGPT or Gemini, though he didn't specify), just minutes. What fascinated me is that my friend has no illusions about generative AI's accuracy. He fully expected one out of 10 facts to be inaccurate or perhaps hallucinated and made it clear that his very human hands are still part of the quality-control process. For now. The next thing he said surprised me - not because it isn't true, but because he acknowledged it. Eventually, AI won't hallucinate, it won't make a mistake. That's the trajectory and we should prepare for it. I agreed with him because this has long been my thinking. The speed of development essentially guarantees it. While I grew up with Moore's Law, which posits a doubling of transistor capacity on a microchip roughly every two years, AI's Law is, putting it roughly, a doubling of intelligence every three-to-six months. That pace is why everyone is so convinced we'll achieve Artificial General Intelligence (AGI or human-like intelligence) sooner than originally thought. I believe that, too, but I want to circle back to hallucinations because even as consumers and non-techies like my friend embrace AI for everyday work, hallucinations remain a very real part of the AI, Large Language Model (LLM) corpus. In a recent anecdotal test of multiple AI chatbots, I was chagrinned to find that most of them could not accurately recount my work history, even though it is spelled out in exquisite detail on Linkedin and Wikipedia. These were minor errors and not of any real importance because who cares about my background except me? Still, ChatGPT's 03-mini model, which uses deeper reasoning and can therefore take longer to formulate an answer, said I worked at TechRepublic. That's close to "TechRadar," but no cigar. DeepSeek, the Chinese AI chatbot wunderkund, had me working at Mashable years after I left. It also confused my PCMag history. Google Gemini smartly kept the details scant, but it got all of them right. ChatGPT's 4o model took a similar pared-down approach and achieved 100% accuracy. Claude AI lost the thread of my timeline and still had me working at Mashable. It warns that its data is out of date, but I did not think it was 8 years out of date. I ran some polls on social media about the level of hallucination most people expect to see on today's AI platforms. On Threads, 25% think AI hallucinates 25% of the time. On X, 40% think it's 30% of the time. However, I also received comments reminding me that accuracy depends on the quality of the prompt and topic areas. Information that doesn't have much of an online footprint is sure to lead to hallucinations, one person warned me. However, research is showing that models are not only getting larger, they're getting smarter, too. A year ago, one study found ChatGPT hallucinating 40% of the time in some tests. According to the Hughes Hallucination Evaluation Model (HHEM) leaderboard, some of the leading models' hallucinations are down to under 2%. Older models like Meta Llama 3.2 are where you can head back into double-digit hallucination rates. What this shows us, though, is that these models are quickly heading in the direction my friend predicts and that at some point in the not-too-distant future, they will have large enough models with real-time training data that put the hallucination rate well below 1%. My concern is that in the meantime, people without technical expertise or even an understanding of how to compose a useful prompt are relying on large language models for real work. Hallucination-driven errors are likely creeping into all sectors of home life and industry and infecting our systems with misinformation. They may not be big errors, but they will accumulate. I don't have a solution for this, but it's worth thinking about and maybe even worrying about a little bit. Perhaps, future LLMs will also include error sweeping, where you send them out into the web and through your files and have them cull all the AI-hallucination-generated mistakes. After all, why should we have to clean up AI's messes?

The surprising reason ChatGPT and other AI tools make things up - and why it's not just a glitch

Large language models (LLMs) like ChatGPT have wowed the world with their capabilities. But they've also made headlines for confidently spewing absolute nonsense. This phenomenon, known as hallucination, ranges from fairly harmless mistakes - like getting the number of 'r's in strawberry wrong - to completely fabricated legal cases that have landed lawyers in serious trouble. Sure, you could argue that everyone should rigorously fact-check anything AI suggests (and I'd agree). But as these tools become more ingrained in our work, research, and decision-making, we need to understand why hallucinations happen - and whether we can prevent them. To understand why AI hallucinates, we need a quick refresher on how large language models (LLMs) work. LLMs don't retrieve facts like a search engine or a human looking something up in a database. Instead, they generate text by making predictions. "LLMs are next-word predictors and daydreamers at their core," says software engineer Maitreyi Chatterjee. "They generate text by predicting the statistically most likely word that occurs next." We often assume these models are thinking or reasoning, but they're not. They're sophisticated pattern predictors - and that process inevitably leads to errors. This explains why LLMs struggle with seemingly simple things, like counting the 'r's in strawberry or solving basic math problems. They're not sitting there working it out like we would - not really. Another key reason is they don't check what they're pumping out. "LLMs lack an internal fact-checking mechanism, and because their goal is to predict the next token [unit of text], they sometimes prefer lucid-sounding token sequences over correct ones," Chatterjee explains. And when they don't know the answer? They often make something up. "If the model's training data has incomplete, conflicting, or insufficient information for a given query, it could generate plausible but incorrect information to 'fill in' the gaps," Chatterjee tells me. Rather than admitting uncertainty, many AI tools default to producing an answer - whether it's right or not. Other times, they have the correct information but fail to retrieve or apply it properly. This can happen when a question is complex, or the model misinterprets context. This is why prompts matter. Certain types of prompts can make hallucinations more likely. We've already covered our top tips for leveling up your AI prompts. Not just for getting more useful results, but also for reducing the chances of AI going off the rails. For example, ambiguous prompts can cause confusion, leading the model to mix up knowledge sources. Chatterjee says this is where you need to be careful, ask "Tell me about Paris" without context, and you might get a strange blend of facts about Paris, France, Paris Hilton, and Paris from Greek mythology. But more detail isn't always better. Overly long prompts can overwhelm the model, making it lose track of key details and start filling in gaps with fabrications. Similarly, when a model isn't given enough time to process a question, it's more likely to make errors. That's why techniques like chain-of-thought prompting - where the model is encouraged to reason through a problem step by step - can lead to more accurate responses. Providing a reference is another effective way to keep AI on track. "You can sometimes solve this problem by giving the model a 'pre-read' or a knowledge source to refer to so it can cross-check its answer," Chatterjee explains. Few-shot prompting, where the model is given a series of examples before answering, can also improve accuracy. Even with these techniques, hallucinations remain an inherent challenge for LLMs. As AI evolves, researchers are working on ways to make models more reliable. But for now, understanding why AI hallucinates, how to prevent it, and, most importantly, why you should fact-check everything remains essential.

What are AI hallucinations? Why AIs sometimes make things up

When someone sees something that isn't there, people often refer to the experience as a hallucination. Hallucinations occur when your sensory perception does not correspond to external stimuli. Technologies that rely on artificial intelligence can have hallucinations, too. When an algorithmic system generates information that seems plausible but is actually inaccurate or misleading, computer scientists call it an AI hallucination. Researchers have found these behaviors in different types of AI systems, from chatbots such as ChatGPT to image generators such as Dall-E to autonomous vehicles. We are information science researchers who have studied hallucinations in AI speech recognition systems. Wherever AI systems are used in daily life, their hallucinations can pose risks. Some may be minor -- when a chatbot gives the wrong answer to a simple question, the user may end up ill-informed. But in other cases, the stakes are much higher. From courtrooms where AI software is used to make sentencing decisions to health insurance companies that use algorithms to determine a patient's eligibility for coverage, AI hallucinations can have life-altering consequences. They can even be life-threatening: Autonomous vehicles use AI to detect obstacles, other vehicles and pedestrians. Making it up Hallucinations and their effects depend on the type of AI system. With large language models -- the underlying technology of AI chatbots -- hallucinations are pieces of information that sound convincing but are incorrect, made up or irrelevant. An AI chatbot might create a reference to a scientific article that doesn't exist or provide a historical fact that is simply wrong, yet make it sound believable. In a 2023 court case, for example, a New York attorney submitted a legal brief that he had written with the help of ChatGPT. A discerning judge later noticed that the brief cited a case that ChatGPT had made up. This could lead to different outcomes in courtrooms if humans were not able to detect the hallucinated piece of information. With AI tools that can recognize objects in images, hallucinations occur when the AI generates captions that are not faithful to the provided image. Imagine asking a system to list objects in an image that only includes a woman from the chest up talking on a phone and receiving a response that says a woman talking on a phone while sitting on a bench. This inaccurate information could lead to different consequences in contexts where accuracy is critical. What causes hallucinations Engineers build AI systems by gathering massive amounts of data and feeding it into a computational system that detects patterns in the data. The system develops methods for responding to questions or performing tasks based on those patterns. Supply an AI system with 1,000 photos of different breeds of dogs, labeled accordingly, and the system will soon learn to detect the difference between a poodle and a golden retriever. But feed it a photo of a blueberry muffin and, as machine-learning researchers have shown, it may tell you that the muffin is a chihuahua. When a system doesn't understand the question or the information that it is presented with, it may hallucinate. Hallucinations often occur when the model fills in gaps based on similar contexts from its training data, or when it is built using biased or incomplete training data. This leads to incorrect guesses, as in the case of the mislabeled blueberry muffin. It's important to distinguish between AI hallucinations and intentionally creative AI outputs. When an AI system is asked to be creative -- like when writing a story or generating artistic images -- its novel outputs are expected and desired. Hallucinations, on the other hand, occur when an AI system is asked to provide factual information or perform specific tasks but instead generates incorrect or misleading content while presenting it as accurate. The key difference lies in the context and purpose: Creativity is appropriate for artistic tasks, while hallucinations are problematic when accuracy and reliability are required. To address these issues, companies have suggested using high-quality training data and limiting AI responses to follow certain guidelines. Nevertheless, these issues may persist in popular AI tools. What's at risk The impact of an output such as calling a blueberry muffin a chihuahua may seem trivial, but consider the different kinds of technologies that use image recognition systems: An autonomous vehicle that fails to identify objects could lead to a fatal traffic accident. An autonomous military drone that misidentifies a target could put civilians' lives in danger. For AI tools that provide automatic speech recognition, hallucinations are AI transcriptions that include words or phrases that were never actually spoken. This is more likely to occur in noisy environments, where an AI system may end up adding new or irrelevant words in an attempt to decipher background noise such as a passing truck or a crying infant. As these systems become more regularly integrated into health care, social service and legal settings, hallucinations in automatic speech recognition could lead to inaccurate clinical or legal outcomes that harm patients, criminal defendants or families in need of social support. Check AI's work Regardless of AI companies' efforts to mitigate hallucinations, users should stay vigilant and question AI outputs, especially when they are used in contexts that require precision and accuracy. Double-checking AI-generated information with trusted sources, consulting experts when necessary, and recognizing the limitations of these tools are essential steps for minimizing their risks.

Hallucinations in large language models

Hallucinations in large language models (LLMs) represent a fascinating yet challenging facet of artificial intelligence. These occurrences, where AI generates content that lacks accuracy or reality, can significantly impact user trust and the application of these technologies. Understanding the nature and implications of hallucinations is essential for anyone interested in the evolving landscape of AI. Hallucinations in LLMs refer to instances where the model produces information that may sound plausible but is entirely fabricated or incorrect. This phenomenon can arise from various factors, including the training data and the model's inherent structure. Large language models, such as GPT-3, have revolutionized the way AI produces text, enabling coherent and contextually relevant responses. Their sophisticated architecture and extensive training datasets contribute to their impressive capabilities but also intensify the risk of hallucinations occurring during conversations or in text generation tasks. The training process of LLMs consists of several crucial steps: LLM bias is closely intertwined with the concept of hallucinations, as it underscores the ethical implications of AI outputs. Bias emerges not from an intentional design but rather from the datasets upon which the models are trained. Several factors contribute to LLM bias: To fully understand hallucinations, it is vital to grasp certain fundamental concepts tied to LLM functioning. Tokens serve as the foundational elements of language models. They can encompass anything from single characters to entire phrases. The issue of hallucinations is not confined to language models but extends across various AI applications, prompting broader discussions about their reliability and safety. Comprehending hallucinations informs various strategies aimed at enhancing the quality and fairness of AI outputs. To mitigate the risk of hallucinations and improve LLM outputs, several approaches are recommended:

What are AI hallucinations? Why AIs sometimes make things up

When an algorithmic system generates information that seems plausible but is actually inaccurate or misleading, computer scientists call it an AI hallucination. Researchers have found these behaviors in different types of AI systems, from chatbots such as ChatGPT to image generators such as Dall-E to autonomous vehicles. We are information science researchers who have studied hallucinations in AI speech recognition systems.When someone sees something that isn't there, people often refer to the experience as a hallucination. Hallucinations occur when your sensory perception does not correspond to external stimuli. Technologies that rely on artificial intelligence can have hallucinations, too. When an algorithmic system generates information that seems plausible but is actually inaccurate or misleading, computer scientists call it an AI hallucination. Researchers have found these behaviors in different types of AI systems, from chatbots such as ChatGPT to image generators such as Dall-E to autonomous vehicles. We are information science researchers who have studied hallucinations in AI speech recognition systems. Wherever AI systems are used in daily life, their hallucinations can pose risks. Some may be minor - when a chatbot gives the wrong answer to a simple question, the user may end up ill-informed. But in other cases, the stakes are much higher. From courtrooms where AI software is used to make sentencing decisions to health insurance companies that use algorithms to determine a patient's eligibility for coverage, AI hallucinations can have life-altering consequences. They can even be life-threatening: Autonomous vehicles use AI to detect obstacles, other vehicles and pedestrians. Making it up Hallucinations and their effects depend on the type of AI system. With large language models - the underlying technology of AI chatbots - hallucinations are pieces of information that sound convincing but are incorrect, made up or irrelevant. An AI chatbot might create a reference to a scientific article that doesn't exist or provide a historical fact that is simply wrong, yet make it sound believable. In a 2023 court case, for example, a New York attorney submitted a legal brief that he had written with the help of ChatGPT. A discerning judge later noticed that the brief cited a case that ChatGPT had made up. This could lead to different outcomes in courtrooms if humans were not able to detect the hallucinated piece of information. With AI tools that can recognize objects in images, hallucinations occur when the AI generates captions that are not faithful to the provided image. Imagine asking a system to list objects in an image that only includes a woman from the chest up talking on a phone and receiving a response that says a woman talking on a phone while sitting on a bench. This inaccurate information could lead to different consequences in contexts where accuracy is critical. What causes hallucinations Engineers build AI systems by gathering massive amounts of data and feeding it into a computational system that detects patterns in the data. The system develops methods for responding to questions or performing tasks based on those patterns. Supply an AI system with 1,000 photos of different breeds of dogs, labelled accordingly, and the system will soon learn to detect the difference between a poodle and a golden retriever. But feed it a photo of a blueberry muffin and, as machine learning researchers have shown, it may tell you that the muffin is a chihuahua. When a system doesn't understand the question or the information that it is presented with, it may hallucinate. Hallucinations often occur when the model fills in gaps based on similar contexts from its training data, or when it is built using biased or incomplete training data. This leads to incorrect guesses, as in the case of the mislabeled blueberry muffin. It's important to distinguish between AI hallucinations and intentionally creative AI outputs. When an AI system is asked to be creative - like when writing a story or generating artistic images - its novel outputs are expected and desired. Hallucinations, on the other hand, occur when an AI system is asked to provide factual information or perform specific tasks but instead generates incorrect or misleading content while presenting it as accurate. The key difference lies in the context and purpose: Creativity is appropriate for artistic tasks, while hallucinations are problematic when accuracy and reliability are required. To address these issues, companies have suggested using high-quality training data and limiting AI responses to follow certain guidelines. Nevertheless, these issues may persist in popular AI tools. What's at risk The impact of an output such as calling a blueberry muffin a chihuahua may seem trivial, but consider the different kinds of technologies that use image recognition systems: An autonomous vehicle that fails to identify objects could lead to a fatal traffic accident. An autonomous military drone that misidentifies a target could put civilians' lives in danger. For AI tools that provide automatic speech recognition, hallucinations are AI transcriptions that include words or phrases that were never actually spoken. This is more likely to occur in noisy environments, where an AI system may end up adding new or irrelevant words in an attempt to decipher background noise such as a passing truck or a crying infant. As these systems become more regularly integrated into health care, social service and legal settings, hallucinations in automatic speech recognition could lead to inaccurate clinical or legal outcomes that harm patients, criminal defendants or families in need of social support. Check AI's work Regardless of AI companies' efforts to mitigate hallucinations, users should stay vigilant and question AI outputs, especially when they are used in contexts that require precision and accuracy. Double-checking AI-generated information with trusted sources, consulting experts when necessary, and recognizing the limitations of these tools are essential steps for minimising their risks. (The Conversation) PY PY

Why ChatGPT and Other AIs Make Things Up - Softonic

AI models generate false information due to their predictive nature. Understanding why this happens and how to minimize hallucinations is essential for responsible AI use. Artificial intelligence has made incredible strides, but one persistent issue remains: AI models sometimes generate completely false information. This phenomenon, known as "hallucination," can range from minor errors to entirely fabricated facts. Understanding why this happens is crucial to using AI responsibly. Unlike humans, AI models do not retrieve facts from a database. Instead, they generate responses by predicting the most statistically likely sequence of words based on their training data. This means that while AI can produce coherent and fluent text, it does not "know" facts in the way humans do. It simply follows patterns without verifying their accuracy. One major reason for hallucinations is that AI lacks an internal fact-checking mechanism. Its priority is to generate text that sounds plausible rather than ensuring accuracy. When asked a question, it may provide an answer even if it has insufficient or conflicting information in its training data. Another issue is how AI interprets prompts. Vague or ambiguous prompts can lead to mixed responses, pulling from different sources and resulting in incorrect or misleading information. Conversely, overly long or complex prompts can confuse the model, leading it to fabricate details to fill perceived gaps. While hallucinations cannot be eliminated, certain strategies can reduce them. Providing clear and specific promptshelps guide AI responses more accurately. Additionally, techniques like "chain-of-thought" prompting encourage the model to break down its reasoning step by step, improving reliability. When possible, cross-referencing AI responses with trusted sources is essential to ensure accuracy. As AI technology evolves, researchers are working to minimize hallucinations. However, for now, understanding how and why AI makes mistakes is key to using it effectively.