AI Hallucinations: When Algorithms “Get Creative”

In the world of artificial intelligence, there’s an aspect that’s equal parts are hilarious and mildly terrifying: AI hallucinations. No, we’re not talking about robots dreaming of futuristic utopias or whispering poetic musings about the meaning of life. This kind of hallucination happens when an AI—let’s just say it—makes stuff up. Boldly. Confidently. And entirely wrong.

Imagine asking your AI assistant for some quick business insights, and instead, it provides you with “well-researched” facts about – in the worst case - completely fabricated data. Welcome to the delightful world of AI hallucinations! These aren’t just awkward one-offs either; they can lead to real-world complications if we don’t keep them in check. Let’s explore what these hallucinations are and how you can overcome them.

What Are AI Hallucinations?

AI hallucinations happen when an artificial intelligence system, especially large language models (LLMs), takes a detour from the path of truth. In a bid to sound like it knows what it’s talking about, some AI sometimes conjures up completely false or fabricated information. The kicker? It does so with the confidence of an overzealous know-it-all at a dinner party.

These hallucinations come in a few flavors:

• Making up facts: AI confidently delivers completely fictitious "truths" that no human has ever spoken or written.
• Inventing data/sources: Need a credible evaluation of some topic area? How about one that doesn’t exist or that no existing data supports?
• Connecting unrelated ideas: When an AI takes creative liberties and draws links between things that should have never been in the same sentence.

Funny? Perhaps. Useful? Not so much.

Hilarious (and Concerning) AI Hallucination Examples

To understand how AI hallucinations can create a lot of harm, let’s look at some real-life (or should we say AI-life?) examples from quite different areas that are as funny as they are unsettling.

1. The Case of the Fictional Sources

Imagine you’re a lawyer using AI to speed up the research process. You ask your AI tool for relevant case law, and it generates beautifully written legal precedents. The only problem? The cases don’t exist. The AI proudly served up legal arguments, complete with fake case titles and references that sound authoritative but are entirely made up.

Why stop at just giving you actual cases when AI can just invent a few for you, right? If you're wondering whether your day could get any more confusing, this is probably it. The lesson here: Always double-check those case citations before trying to win a legal battle based on AI’s creative whims.

2. The Chatbot That Got… "Creative"

We’ve all dealt with AI customer service chatbots, and let’s be honest—sometimes, they’re really do a good job. But every now and then, they take a creative leap. In one instance, a customer asked a chatbot for details about a company’s refund policy. Instead of providing accurate information, the chatbot invented a whole new policy on the spot, one that was as nonexistent as that 20% raise you keep hoping for.

What’s worse? The chatbot’s policy was entirely reasonable. The customer might have even believed it—until they called the actual company and realized they were about to embark on a quest for a refund that didn’t exist.

3. AI’s Wild Medical Diagnoses

In healthcare, AI hallucinations aren’t just funny—they can be downright dangerous. There was one instance where an AI tool, tasked with helping doctors diagnose patients, started handing out treatments for rare conditions. Treatments that didn’t exist. According to this AI, your "mild headache" could be treated by “rest, hydration, and, oh yes, a teaspoon of moon dust under the light of a waxing crescent moon.”

Good thing there was an actual human doctor around to intervene before anyone started hunting for lunar minerals. If there’s one thing we can all agree on, it’s that when it comes to your health, we’d prefer our AI to not hallucinate.

4. Dangerous Visual Hallucination: Self-Driving Cars

A common example of visual hallucinations in (partial) driving automation is when the AI misinterprets road signs or objects due to environmental conditions or limitations in its training data. For instance, a self-driving car’s AI might mistake a shadow cast by a tree for a solid obstacle and attempt to swerve unnecessarily, which could lead to dangerous driving behavior.

In another case, reflections from glass buildings or unexpected patterns on billboards might cause the car’s AI to hallucinate phantom objects—seeing obstacles that aren’t there, or failing to recognize actual hazards like a pedestrian crossing the road. These kinds of visual hallucinations arise when the AI model hasn’t been trained on enough diverse visual data, causing it to make incorrect inferences about the road environment.

To mitigate this, car manufacturers use massive amounts of training data to account for a variety of conditions, and they rely on multiple sensors (LIDAR, radar, cameras) to produce all this training data and use it to cross-check and verify what the car "sees."

Why Do AI Hallucinations Happen?

While it’s fun to laugh at these AI missteps, there’s a science behind why they happen. AI, particularly large language models, isn’t actually “understanding” anything. It’s just predicting what comes next in a logical sequence of item (e.g. words, picture elements or tasks) based on data it’s seen before. When the AI doesn’t have enough information or can’t quite figure out what’s being asked, it tries to fill in the gaps by… well, making stuff up.

In other words, it’s like when you’re asked a question you don’t know the answer to, but you still try to sound smart. Except in AI’s case, it’s not even aware it’s hallucinating—it’s just trying to be helpful. And in the process, it becomes about as helpful as that one friend who gives life advice despite clearly not having their own life together.

The Consequences of a Hallucinating AI

While AI hallucinations can be funny, they have real-world consequences that range from mild inconvenience to serious harm. In fields like healthcare, law, or finance, a hallucinating AI could recommend incorrect medical treatments, misinterpret legal documents, or generate false financial forecasts, leading to dangerous decisions.

For businesses, it can lead management and other to wrong conclusions, erode trust in AI systems, damage reputations, and even create legal liabilities. That’s why addressing hallucinations isn’t just about improving AI’s performance—it's about ensuring safety, reliability, and trustworthiness and thereby making ensuring company success.

?The Cure

One of the key ways to reduce AI hallucinations is by training models on large and diverse datasets. A broader training base helps AI recognize more patterns, avoid overfitting on specific data, and handle a wide range of queries with accuracy.

A company like Flytxt, with its long history in AI and analytics for telecommunications, excels in addressing these issues. Having worked with vast amounts of data from a lot of different companies over many, many years, Flytxt’s experience ensures its AI models are well-trained, helping to overcome the hallucination problem. Their extensive training allows the AI to generate much more accurate insights by grounding its predictions in real-world data, minimizing the risk of spurious outputs, and building systems that are both intelligent and trustworthy. In this way, Flytxt showcases the importance of high-quality, massive training datasets and years of expertise to mitigate some imminent AI shortcomings of new AI solutions.

?