Chapter 1: The AI Conundrum

What occurs when artificial intelligence learns from other AIs? It's a competitive landscape out there. I'm glad to have your attention! If you could spare just 30 seconds, I promise you it's worth it (and there's some intriguing math involved). Your support means a lot to me; thank you for being here.

In the past year, many creators have expressed frustration over AIs that have been trained on their original works. There is a growing debate about whether it should be more challenging for AIs to access the data required for training or if creators should receive compensation when their content contributes to AI training.

One lesson we've gleaned this year is that the proverbial Pandora’s box of Artificial Intelligence has been opened; AI is now a permanent fixture in our lives. This is truly "organic" AI, cultivated and refined.

AI models like ChatGPT and others rely on vast amounts of human-generated text and speech data. For further insights, take a look at "How Large Language Models Work." Fascinating, isn't it?

These extensive datasets are essential for training language models that can then generate additional text when prompted by users. As a result, we are witnessing a remarkable trend within the digital landscape:

• Fully automated AI-generated content
• Hybrid content, combining human and AI contributions
• Human-written content refined by AI
• Purely organic human content

However, this situation has led to an intriguing challenge.

What occurs when an AI is trained using a web saturated with AI-generated content? Can AI effectively learn from other AI outputs?

To explore this, researchers from the UK and Canada published findings where they introduced the concept of "Model Collapse."

Model collapse refers to a deterioration process in which models gradually lose the ability to comprehend the authentic underlying data distribution, even when there is no change in that distribution over time.

As a result, AI can begin to forget the patterns and traits of the data it has been trained on. To quote their research paper:

"Models start overlooking improbable events over time as they become tainted by their own interpretation of reality."

I’m sure you have some questions; I did too.

Let’s delve deeper. What exactly are these improbable events? What constitutes a data distribution? What does a shift in distribution mean?

Neural networks are capable of approximating various behaviors in data. This hinges on having sufficient examples for learning. Despite their abilities, neural networks require a robust dataset to function effectively and avoid becoming overly complicated. They can mimic data patterns exceedingly well, but sometimes they also learn unwanted "noise" or randomness present in the data.

How do these concepts of “distributions” and “approximations” relate to models like ChatGPT?

The term "generative" in generative models signifies that they are designed to learn data patterns to produce “new” yet similar outputs. Distributions describe how data is organized and the likelihood of different values appearing within the dataset. The model must mathematically encapsulate how information is arranged and where it might be found. By leveraging these patterns, it generates new data.

Once the model comprehends the distribution of the training data, it can create new "samples" that are statistically akin to the original data.

Recap:

AI models are trained on extensive datasets. They learn how this data is organized and develop a mathematical approximation that allows them to generate new examples similar to their training set.

Returning to the questions:

An improbable event is one with a low chance of occurrence. These may be overlooked by models that rely on approximations derived from earlier models. The intricacies matter, and the AI gradually forgets.

A data distribution conveys how data is structured within a dataset, as well as the likelihood of varying values. A shift in distribution indicates a change in how the data is organized.

Using AI to Outmaneuver AI

As we've explored, these models learn primarily to replicate the training data. Given that the datasets for training LLMs are finite, the discrepancies introduced by future approximations can accumulate until the model collapses and fails to perform as intended. Each iteration represents a small, yet flawed, divergence from the source material. Eventually, the model may veer off course.

First come, first served

The impact of AI on subsequent AI models is evident. Since the emergence of ChatGPT, the internet has been inundated with text generated by language models, and this trend is likely to continue as more individuals gain access to such technologies. Without adequate human context to guide future models, they may lose their grasp on reality.

That's a wrap!

I hope you found this narrative engaging. Have a splendid day!

Don't forget to applaud if you enjoyed it. Check out more captivating stories:

• Robots are now passing “Are you a robot?” tests more effectively than humans.
• What’s the story behind internet cookies?
• WiFi routers can be utilized for surveillance.

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Chapter 2: The Future of AI Dynamics

This video titled "What happens when AI eats itself?" delves into the paradoxes and consequences of AI systems learning from each other.

In "How AI Ate My Website," the implications of AI-generated content on individual creators and the broader digital landscape are explored.