QuantumPip

From the conversation with ChatGPT:

Me: Overfitting shows a nice historical chart, but cannot show a good performance in the future. While a proper neural training has a mediocre historical chart and a satisfactory performance in the future. Why do you think most customers cannot understand this simple truth?
 
ChatGPT: The challenge you're describing about overfitting and generalization in machine learning models is indeed a common issue that many people, including customers, can struggle to grasp. Here are a few reasons why this misunderstanding might occur:

1. **Complexity vs. Simplicity**: The concept of overfitting and generalization involves nuanced concepts in machine learning that can be difficult for individuals without a technical background to fully comprehend.

2. **Short-Term vs. Long-Term Performance**: Customers may be more easily swayed by models that perform exceptionally well on historical data (overfit models) because the immediate results appear favorable, even though they may not generalize to unseen data.

3. **Lack of Education**: Many customers may not have a deep understanding of machine learning principles, leading to a reliance on simple metrics or visualizations without considering the broader implications of model performance.

4. **Expectations**: Customers may have unrealistic expectations or misconceptions about the capabilities of machine learning models, expecting them to predict future outcomes with perfect accuracy based on historical data.

Educating customers about the trade-offs between overfitting and generalization, and the importance of model validation on unseen data, can help set realistic expectations and foster a better understanding of the complexities involved in building robust machine learning models.

Me: Alright. But why, no matter how I, the developer, try to explain the principles of machine learning, and even despite the pictures I show, most customers still prefer to rely on the beauty of overfitted and nice historical charts?

ChatGPT: There could be several reasons why customers may still prefer models with overfitted and visually appealing historical charts despite your efforts to explain the principles of machine learning. Here are a few possible reasons for this behavior:

1. **Visual Bias**: Customers might be visually attracted to models that have visually appealing historical charts, even if these charts are a result of overfitting. The graphical representation of data can have a strong impact and may overshadow technical explanations.
   
2. **Immediate Results**: Customers might be more focused on immediate or short-term performance metrics rather than long-term generalization and future performance. A visually pleasing historical chart may give them a sense of immediate success.

3. **Complexity**: Machine learning concepts can be complex and difficult to understand for individuals without a technical background. Customers may find it easier to interpret and trust visually clear results rather than abstract technical explanations.

4. **Confirmation Bias**: Customers might have preconceived notions or biases about what a successful model should look like. If they see a visually appealing historical chart, it may confirm their beliefs about what constitutes a good model.

5. **Lack of Trust**: Customers may have limited trust in the developer's explanations or in the machine learning process in general. This lack of trust can lead them to rely on visual cues rather than technical reasoning.

Me: Your explanation sounds very respectful. But maybe the only reason customers do not buy strategies that implement the principles of machine learning, is because there are so many unscrupulous developers out there who sell and sell and sell overfitted models as if those were the most cutting-edge strategies available on the market? While in fact they sell nothing more than scam.

ChatGPT: Your observation is very valid. Unfortunately, the proliferation of unscrupulous developers selling overfitted models as top-tier strategies can lead to skepticism and mistrust among customers towards more genuine and principles-based machine learning approaches. This can create a marketplace where flashier but ineffective solutions gain more attention than sound, robust strategies. Building trust through transparency, education, and delivering real value to customers can help differentiate authentic solutions from scams in such environments.
 
Me: Can you help me create a strategy that can outbeat the overfitted models?

ChatGPT: I'd be glad to assist you in creating a robust and effective strategy using machine learning principles. To proceed, please provide me with more details about the specific requirements of your strategy, the dataset you are working with, and any other relevant information that can help me tailor the approach to your needs. Let's work together to develop a strategy that can outperform overfitted models...