AI Hallucinations

Maryan Duritan
Maryan Duritan
IT Writer
Last updated: May 27, 2024
Why Trust Us
Our editorial policy emphasizes accuracy, relevance, and impartiality, with content crafted by experts and rigorously reviewed by seasoned editors for top-notch reporting and publishing standards.
Disclosure
Purchases via our affiliate links may earn us a commission at no extra cost to you, and by using this site, you agree to our terms and privacy policy.

Understanding AI hallucinations

An AI hallucination occurs when a sophisticated large language model (LLM), such as OpenAI’s GPT4 or Google’s PaLM, generates false assertions or facts that are not based in reality.

AI hallucinations are instances in which language models generate entirely imagined information. Despite the fact that their inventions are totally imaginary, these models exude confidence and expertise.

Generative AI-powered chatbots can generate a variety of made-up features, such as names, dates, historical happenings, quotes, and even code.

Such hallucinations are common enough that OpenAI has issued a warning to ChatGPT users, stating that the AI may communicate false information about people, places, or facts.

The main test for users is to differentiate between accurate information and fabrications.

Instances of AI hallucinations

AI technology doesn’t always hit the mark, resulting in some remarkable mix-ups. Here are a few notable situations where AI didn’t get things right, reminding us that these technologies aren’t perfect:

  • Google’s Bard chatbot made an incorrect statement, claiming that the James Webb Space Telescope took the first images of a planet beyond our solar system.
  • Sydney, Microsoft’s AI chatbot, went off script, professing love for users and even indicating it was spying on Bing employees, raising eyebrows with its surprising comments.
  • Meta had to quickly pull back their Galactica AI demo in 2022 as it began providing wrong and often biased information, illustrating how AI can unwittingly propagate false information.

Although these issues were addressed, they shed light on AI’s propensity for creating baffling and unintended outcomes. These examples underscore the need to approach AI-generated information with caution, as it may sometimes lead to misunderstandings and inaccuracies.

What causes AI to hallucinations?

When artificial intelligence systems go through the learning phase, they detect patterns in the data they are fed. However, if this data is not quite right—perhaps it is outdated, biased, or simply incorrect—the AI may wind up learning the wrong things. This sometimes causes the AI to deliver incorrect or “hallucinate” answers.

Take, for example, an AI taught to detect cancer cells in medical scans. If it has only seen scans showing cancer, it may begin to believe that all cells, including healthy ones, are cancerous. This type of error occurs because the AI did not have the opportunity to see what normal, healthy cells looked like.

This difficulty is frequently caused by difficulties with the training data, such as missing or incorrect information. It can also happen if the AI isn’t well-programmed enough to completely understand the intricacies of the data, or if it becomes confused by how we speak, including slang or humor, especially when insufficient context is provided.

That is why it is critical for those developing AI systems to feed them high-quality, reliable data and properly design them to handle this material correctly. They should also ensure that the AI has clear guidelines to follow, which will assist prevent these mistakes from occurring.

Risks of AI hallucinations

A major concern of AI hallucinations is that humans depend too strongly on what the AI tells them.

Even though some like Microsoft CEO Satya Nadella believe that AI making mistakes can sometimes be beneficial, there is a real concern that these blunders will spread incorrect facts or mean-spirited content if no one monitors what the AI says.

The problematic issue about false information from AI is that it can appear very convincing, as if it is packed with facts and sounds completely credible, even if it is not true. This may lead people to believe things that are entirely false.

If everyone simply accepts what AI comes up with without interrogating it, we may wind up with a lot of inaccurate information flowing around the internet.

There’s also a legal aspect to consider. Assume a corporation employs AI to communicate with its consumers, and the AI provides advice that ends up ruining something or saying something extremely insulting. That corporation could find itself in hot water and face legal consequences.

How can you spot AI hallucinations?

To determine whether an AI is making things up, the simplest technique is to double-check its responses. Use a search engine to evaluate what the AI says to credible sources such as news websites, expert reports, research papers, and books. This manner, you can determine whether the material is accurate.

While finding things up on your own is fine for people, corporations may find it too time-consuming or expensive to manually examine all of the information.

This is where automated tools come in useful. They can immediately scan AI outputs for evidence of hallucinations. For example, Nvidia provides a free tool called NeMo Guardrails that compares what one AI says to what others say in order to detect any fabricated information.

Another tool, TruthChecker by Got It AI, searches for hallucinations in content written with newer versions of GPT (3.5 and up).

However, firms who intend to employ tools such as NeMo Guardrails or TruthChecker should first ensure that these technologies can detect false information. They should also consider what more they can do to prevent legal ramifications if the AI makes a mistake.

Conclusion

AI and large language models (LLMs) bring some pretty cool benefits to the table for businesses, but understanding their limitations and clever ways to use them is critical to getting the most out of them.

At the heart of it, AI tools shine most when used to augment what people can already achieve, rather than when allowed to perform jobs on their own.

If people and businesses remember that AI may occasionally make things up and double-check those details elsewhere, there’s a far lower risk of incorrect information being circulated or trusted.

Related terms

Related articles

About XPS's Editorial Process

XPS's editorial policy focuses on providing content that is meticulously researched, precise, and impartial. We adhere to rigorous sourcing guidelines, and every page is subject to an exhaustive review by our team of leading technology specialists and experienced editors. This method guarantees the integrity, pertinence, and utility of our content for our audience.

Maryan Duritan
Maryan Duritan
Maryan Duritan, a seasoned U.S.-based copywriter and SEO specialist, excels in making complex ideas accessible. She crafts compelling website content, blogs, articles, ebooks, press releases, and newsletters, tailoring tone and voice to match client goals and audience needs. Her creative precision transforms ideas into impactful content.

Why Trust Us

Our editorial policy emphasizes accuracy, relevance, and impartiality, with content crafted by experts and rigorously reviewed by seasoned editors for top-notch reporting and publishing standards.

Disclosure
Purchases via our affiliate links may earn us a commission at no extra cost to you, and by using this site, you agree to our terms and privacy policy.

Popular terms

What is HRIS?

HRIS, short for Human Resource Information System, is a software platform that allows employers to store and manage employee data in an easily accessible...

What is Market Capitalization?

Market capitalization or market cap is a financial measure that denotes the value of a digital currency. It has historically been used to measure...

What is a WebSocket

In the world of web development, communicating between clients and servers in real time has become a necessity. That's where WebSocket comes in, using...

What is AI Ethics?

AI ethics is a field that is concerned with the creation and employment of artificial intelligence (AI). It is a set of values meant...

What is Relative Strength Index (RSI)?

Relative Strength Index (RSI) is a powerful technical analysis tool which is used as a momentum oscillator for measuring how fast and how much...

Latest articles