This post begins with my notes on the topic. Answering it in a form inspired by Sakichi Toyoda's "Five Why's". Then, I use ChatGPT to summarize it.

If you enjoyed this poetic non-fiction reading and want to read more, please consider reading book: Making Data Easy.

Why do machines hallucinate?

Because we ask them to.

1) Why do we ask machines to hallucinate?

To help us answer questions.

2) What kinds of questions?

All kinds of questions.

The mania used to scare me.

Now I understand that the chaos is engineered.

To provide a purpose. A capability.

To answer any questions.

Five why’s. We’ve answered three. We have two to go:

3) Why chaos?

What is chaos? It’s a programmed randomness. We then apply statistical methods based on user-defined goals and context, and diffuse the randomness into some answers we can humanly understand. But the model doesn’t understand. It’s just providing words. Or next-best pixels. It doesn’t actually know anything.

Chaos Engineering

Modeled after the chaos that exists in nature. The ever-expanding equation. Entropy.

Seek the intuition in things.

Hope everyone is safe.

4) Can we always trust the answers?

No. The models are trained on human language. Which you can’t inherently trust. But we can make this better.

5) How can we make this better?

We provide more context.

*The more you know*

Now, let’s ask an AI to turn this into something easier to read.

What is this note talking about?

This note from your friend is discussing the idea of machine learning and AI "hallucinating," which refers to the way these systems generate information or answers based on patterns they've seen in data. Here is a simplified breakdown of the key points:

  1. Machines "hallucinate" (generate information) because we ask them to.
  2. We ask them to do this to help us answer questions.
  3. The "chaos" referred to is essentially a programmed randomness that machines use to generate answers. It's managed by applying statistical methods based on user-defined goals and context.
  4. "Chaos Engineering" here refers to intentionally adding randomness to a system to understand its behavior better, similar to how chaos exists naturally.
  5. The note mentions that AI models don't actually understand anything. They just provide words or "next-best pixels" based on patterns they've seen in data.
  6. The note asks whether we can trust the answers from these models. The answer is "No," because the models are trained on human language, which can be flawed or biased.
  7. Finally, to make these models better, the note suggests providing more context, which can help in generating more accurate and relevant responses.

Is all of this true? Let's always be a little skeptical.

The note presents a perspective on how machine learning and AI systems work. While the concepts mentioned are based on valid ideas, they may be a bit simplified or generalized. Here is a breakdown of the truthfulness of the statements:

  1. Machines "hallucinate": In a sense, this is true. Machine learning models, particularly generative models, create outputs that may seem like they are "hallucinating" information. However, it's more accurate to say they generate outputs based on patterns they've seen in data.
  2. Chaos Engineering: This is a real concept. Chaos Engineering involves intentionally introducing failures or disruptions into a system to test its resilience and reliability. However, in the context of the note, the term "chaos" is used to describe the randomness or unpredictability that machine learning models can exhibit.
  3. AI models don't actually understand anything: This is mostly true. AI models don't "understand" in the human sense. They operate based on patterns and associations found in data.
  4. We can't always trust AI models: True. AI models are only as good as the data they are trained on. They can exhibit biases or inaccuracies present in the data.
  5. Providing more context can improve AI models: Generally true. Adding context can help AI models make more informed and accurate predictions. However, there are other factors that also play a role in improving AI models, such as better algorithms and higher-quality data.

The note provides a perspective on AI and machine learning, but it's important to recognize that these are complex fields with many nuances.

What are some nuances to be aware of?

There are several nuances in AI and machine learning to be aware of, some of which include:

  1. Data quality and bias: Machine learning models rely heavily on the data they are trained on. If the training data is biased or flawed in some way, the model is likely to exhibit those biases in its predictions.
  2. Overfitting: A model that performs very well on the training data but poorly on unseen data may be overfitting. This means that the model has learned to perform very well on the specific data it has seen, but it may not generalize well to new data.
  3. Interpretability: Machine learning models, especially deep neural networks, are often criticized for being "black boxes." This means that it can be difficult to understand how the model arrived at a particular decision.
  4. Ethics: The use of AI raises several ethical questions, such as concerns about privacy, fairness, and the potential for abuse. It's important to consider these implications when using AI systems.
  5. Limitations: AI systems have limitations and are not suitable for all tasks. For example, while AI models can perform well on certain tasks like image recognition, they may struggle with other tasks that require deeper understanding or common sense.
  6. Trade-offs: There are often trade-offs in machine learning, such as the trade-off between model complexity and interpretability or the trade-off between accuracy and fairness.
  7. Feature Engineering: Selecting the right features to include in a machine learning model can have a significant impact on the model's performance. However, deciding what features to include and how to represent them is often more of an art than a science.
  8. Model Assumptions: Many machine learning models make certain assumptions about the data they are working with, such as the assumption that the data is independent and identically distributed (i.i.d). Violations of these assumptions can affect the performance of the model.

These are just a few of the many nuances to be aware of in AI and machine learning. It's important to approach these fields with a critical eye and to understand the limitations and complexities involved.

Please provide a short, kindergarten level summary of what we’ve learned today.

Today we talked about how computers can "hallucinate" or make up answers to questions by looking at lots of information. But, sometimes they can get things wrong because they don't really understand like people do. They just see patterns. We also learned that we need to be careful when using computers to answer questions because they can make mistakes or be a little bit confused. So, it's important to know that computers are helpful, but not perfect.

If this is interesting to you, please check out my book: Making Data Easy.