Your business can master AI hallucinations with this data-driven approach

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Picture this: You open your favorite food delivery app to order a late-night snack. You select your preferred order and finalize your purchase. When your food arrives, you find that they gave you ranch dressing to go with your cinnamon roll. You know for sure, you asked for extra frosting on the side and you come back to the app to find that you actually asked for frosting and received ranch.

You have just experienced an artificial intelligence (AI) hallucination.

AI hallucinations are a category of content that may be inaccurate, absurd, or even harmful due to AI models that derive information from outdated or incorrect data sets. In this case, the AI ​​hallucinated what it thought would serve as a substitute for frosting when the restaurant sold out. Without proper context, the AI ​​did its best to work with the information it had.

AI hallucinations

(AI hallucinations are the category of content that may be inaccurate, absurd, or even harmful due to AI models that derive information from outdated or incorrect datasets. (Getty Images))

The advent of artificial intelligence (AI) unveils numerous business opportunities. AI predicts stock market trends, detects fraud and malware before they can infiltrate systems and devices, and communicates useful and timely updates to customers. However, new technologies bring new risks that can be far more serious than mistaking ranch dressing for frosting.


AI hallucinations are a new business threat that has risen to prominence with the introduction of generative AI (GenAI). False predictions and inaccurate data produced by mind-blowing AI can put the reputations of companies and individuals at risk due to poor decision-making or even copyright and legal issues as AI is trained on already existing data or content available in the public domain. . Companies must ensure their AI technology is based on reliable models with access to fresh, continually updated data to significantly reduce hallucinations.

Generative AI in the wild

Companies across industries are currently evaluating GenAI opportunities. Generative AI – an AI technology capable of generating different types of content (e.g. images, videos, audio, text, etc.) by leveraging existing prompts and data – can be used to industrial monitoring, medical devices, healthcare diagnostics and other endless possibilities.

It’s no surprise that IDC predicts that spending on GenAI solutions will reach $143 billion in 2027. A study conducted by Salesforce even found that 45% of the US population already uses GenAI.

McDonald’s is considering automated voice ordering for drive-thrus, Stitch Fix is ​​experimenting with GenAI to suggest styles to its customers, and Morgan Stanley is building an AI assistant with GenAI. People and organizations around the world access services that use AI every day without even knowing it. This is why it is so crucial for businesses to integrate AI into their platforms securely and strategically.

Many AI use cases require critical, even life-changing, decisions to be made in an instant, such as a medical diagnosis or a decision in the middle of a surgical procedure. Therefore, the accuracy and quality of the data on which GenAI models are trained is of utmost importance.


Data also has expiration dates; Real-time data for great results

Food and gift cards expire, as does data. What good is data if it’s outdated and inaccurate? For GenAI to do its job – generate new data – the model it is built on needs current, contextually relevant information to learn from. Deep learning is a complex computational process used by GenAI models to analyze patterns in large data sets to create new results.

Data is the fuel of AI, the quality of which depends on the data it is trained on. Data quality is increasingly important because sometimes AI models can mislabel or categorize data, leading to AI hallucinations. Businesses can alleviate this problem by integrating real-time data.


Real-time data is provided immediately after collection, providing a constant flow of information without delay. This can ensure that an AI model’s predictions are in sync with the most recent data produced. This type of fast, always-up-to-date technology can significantly reduce the risk of hallucinations and is therefore essential for businesses to properly harness the full potential of GenAI to drive decision-making and deliver positive business outcomes.

Getting ahead of AI hallucinations drives positive business outcomes

While companies may not be able to completely eliminate AI hallucinations, they must take some necessary steps to prevent them from occurring to avoid costly risks and potential harm. GenAI has already shown us a glimpse of the many ways our lives can change. When built on an AI model that leverages real-time data, AI can and will continue to be a part of our lives, providing improved services, faster response times, and new ways to operate. technology.



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