Understanding AI hallucinations - separate fact from fiction

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Hi!

Did you know that AI models can hallucinate? If you've ever used a generative AI tool and got a response that you knew to be false then you may have already experienced this.

What are AI Hallucinations?

‘Hallucination’ is the term used when language models or other AI systems provide an incorrect or misleading response. This can happen in various contexts with some hallucinations easier to spot than others. 

An obvious example of a hallucination could be a chatbot giving a nonsensical response or an image generator creating an object that doesn't exist in reality.   

It may be harder to identify fabricated quotes, made-up people or organizations and falsehoods stated as fact. The problem is the information often seems plausible and when it's written with confidence and authority it can be easy to simply accept the response as truth. 

When creativity causes problems

Generative AI LLMs are a bit like an overenthusiastic intern - eager to please. They want to be helpful and provide you with a useful response but this can mean that speculation and creative liberties abound, leaving truth and fact temporarily by the wayside. 

It’s not such a problem if you’re using generative AI for creative purposes or in a low stakes context but if you’re basing an important decision on the AI response, then you want to be able to trust the information you’re getting. 

AI hallucinations pose particularly significant challenges in fields requiring high accuracy like healthcare, finance, and journalism.

So why does it happen? 

- Training data limitations 

As responses are based on the data used to train the models, any gaps in knowledge might be filled with a bit of poetic license. 

Flaws and gaps in the training data can skew the outcome and biases and inaccuracies may be perpetuated in the responses generated.

Confusion can also arise if the training data is older and the AI is not able to respond appropriately to current events due to a lack of real-world context or nuanced understanding of recent developments.

- Incorrect assumptions

AI models look for patterns in data and hallucinations can occur when the AI model applies a pattern in the wrong contexts.

The AI model might apply a pattern incorrectly and make inaccurate predictions or conclusions as a result.

This can happen if the datasets used to train the model were incomplete or misleading but can also arise due to the model's inability to distinguish between correlation and causation.

- Lack of real-world understanding

Unlike humans, AI doesn't have a genuine understanding of the world which can lead to inconsistencies in how it applies logic and information. 

False positives and false negatives may be harder for an AI model to grasp or identify, as it lacks the contextual understanding that humans naturally possess.

What’s being done?

Fortunately, the AI industry isn't ignoring this challenge! The big names in generative AI - OpenAI, Anthropic and Google - are all working hard to tackle the issue in a bid to provide more accurate and reliable responses. Challengers to the generative AI crown are also looking to crack the hallucination problem and gain an edge in the competitive landscape.

Human oversight is essential when working with AI-generated outputs but improvements to training techniques and prompt engineering, along with robust fact-checking mechanisms will all contribute to minimizing the potential for hallucinations.  

Many AI platforms give you the option to feed back to the development teams by marking responses as helpful/unhelpful. Identifying errors and learning from mistakes should help to make future models more reliable. 

How to avoid falling foul of hallucinations?

Until the issue of hallucinations has been addressed at source, it's important not to blindly trust in the information you're given. 

No matter how convincing the answer, fact check, scrutinize and cross-reference with other sources - particularly before making any big decisions!

Combining AI tools can also help to mitigate the risk of accepting hallucinations as fact. For example, using one AI to generate content and another to fact check or analyze it can provide an additional layer of verification.

The bottom line? AI is an impressive tool, but it's not infallible. Keep your critical thinking skills sharp and don’t be afraid to double-check. Remember, AI models are here to assist us, not replace our own judgment. As AI continues to evolve, so should our approach to using it.

OK, speak soon!

Inge
Top Apps Founder

P.S. Check out the Top Apps AI courses here.