Hello everyone!
In today's video we will learn about a hallucination in AI.
What is AI hallucination?
AI hallucinations are incorrect or misleading results that AI model generates.
These errors can be caused by a variety of factors, including insufficient training data, incorrect
assumptions made by the model, or biases in the data used to train the model.
AI hallucinations can be a problem for AI systems that can be used to make important decisions, such
as medical diagnosis or financial training.
Now let's understand how to hallucination occur.
AI models are trained on data, and they learn to make predictions by finding patterns in the data.
However, the accuracy of these predictions often depend on the quality and completeness of the training
data.
If the training data is incomplete, biased, or otherwise flawed, the AI model may learn incorrect
patterns, leading to incorrect predictions or hallucination.
For example, an AI model that is trained on a data set of medical images may learn to identify cancer
cells.
However, if the data set does not include any images of healthy tissue, the AI model may incorrectly
predict that the healthy tissue is cancerous.
Let's move on to learn this a little more.
Another factor that may contribute is a lack of proper grounding.
An AI model may struggle to accurately understand real world knowledge, physical properties, or factual
information.
This lack of grounding can cause the model to generate outputs that are seemingly possible, but are
factually incorrect or irrelevant.
For example, Google's Bard stated that the James Webb Space Telescope took the very first picture of
a planet outside of our solar system when the first such photo was taken, 16 years before Google Webb
Space Telescope was even launched.
This happened in a public demo of Google Bard.
So what are examples of hallucination?
They're categorized into three different categories.
First is incorrect predictions.
An AI model may predict that an event will occur when it's unlikely to happen.
For example, an AI model that is used to predict the weather may predict it will rain tomorrow when
there is no rain in the forecast.
There are false positives when working with an AI model.
It may identify something as being a threat when it is not a threat.
For example, an AI model that is used to detect fraud may flag a transaction as fraudulent when it
is actually not a fraudulent transaction.
The false negatives AI model may fail to identify something as being a threat, but it is actually a
threat.
For example, an AI model that is used to detect cancer may fail to identify a cancerous tumor.
So these are some high level examples of hallucination.
Let's understand how you can prevent AI hallucination.
Limit possible outcomes to prevent AI model from overfitting.
It's crucial to limit its prediction range using regularization.
This technique penalizes extreme predictions, improving the model's accuracy on new data.
The training data for effective I training use data relevant to the task, such as medical images for
cancer.
Identifying model irrelevant data can lead to incorrect predictions.
Template of your I to follow.
If you are training an AI model to write text, you could create a template that includes the following
elements a title, an introduction, a body, and a conclusion.
No matter how much we try, it's hard to prevent AI hallucination, but it's very important that once
AI hallucination happened, we also should be able to detect it.
In our next video we will learn about techniques to detect AI hallucination.
Thank you.
See you in the next video.