What?! AI Is Smarter Than I Thought

Machine Learning and the Quest for Low Training and Validation Loss

This is an article that results from too much time in geekdom (never thought I’d use that word in a sentence). I contemplate these ideas as everyone is so pro-machine learning they’re not considering the inherent short comings of machine learning code. Face it, machines are only as smart as their programmers. If you’ve taken a programming class you’ve probably encountered garbage in-garbage out (GIGO). Magnify this with AI – GIGO isn’t just from the programmer anymore, but has become data driven. Don’t believe me? Where are all those super rich stock market AI engineers? Why aren’t they in the news? To make matters worse there’s a false confidence being built with public perception. Issues like training/validation loss isn’t something you hear about in the media, but if you don’t understand it your AI model may be biased and worse yet – worthless (dangerous?).

The use of machine learning (ML) is revolutionizing how we examine data, make predictions, resolve challenging issues, and solve complex problems – giving us insight to the final decision making process. All ML models begin “tabula rasa” – as a blank slate. Like a child’s education, the core of AI MML models lies the process of training. The ultimate goal objective of machine learning is to create models that can recognize patterns and relationships in complex data and generalize well to new, unseen data. Equivalent to examining the situation and making a decision based on the evidence a future event. Supplying a lot of good data to the model, while obvious, and training the model over and over again seem to be the focus of most beginner tutorials. Something overlooked that should be considered early on in a model is how to have low “training loss” and “validation loss” In this post, I’d like the reader to consider the objectives of machine learning and examine why low training and validation loss is important.

Training, and (equally important) validation loss are fundamental to ML models, these concepts apply specifically to models that involve supervised learning.

The Goals of Machine Learning (courtesy of ChatGPT)

Machine learning is a subset of artificial intelligence (AI) that focuses on creating algorithms and models that enable computers to learn from data without being explicitly programmed. The primary objectives of machine learning are as follows:

  1. Pattern Recognition: ML models are designed to recognize patterns and relationships in complex data. These patterns help models generalize their knowledge and make predictions on new, unseen data.
  2. Prediction and Decision-Making: Once trained, machine learning models can predict outcomes and make decisions based on input data. This ability to forecast future events is invaluable across numerous industries, from finance and healthcare to marketing and autonomous vehicles.
  3. Automation and Efficiency: ML enables automation of tasks that were previously performed manually, leading to increased efficiency and reduced human errors. This is especially important when dealing with large datasets and repetitive tasks.

The Role of Training and Validation Loss

Training a machine learning model involves exposing it to a labeled dataset, where the model learns from the input-output pairs to form patterns and relationships. During training, the model makes predictions on the training data and compares them to the actual labels. The difference between the predictions and actual labels is known as the training loss.

1. Training Loss: The training loss quantifies how well the model is performing on the training data. The objective is to minimize this loss by adjusting the model’s parameters and fine-tuning its architecture. A low training loss indicates that the model has learned to recognize patterns in the training data effectively. However, a model that performs exceptionally well on the training data but poorly on new, unseen data may suffer from overfitting.

2. Validation Loss: To evaluate the model’s generalization performance, a separate dataset called the validation set is used. The model makes predictions on the validation set, and the difference between these predictions and the actual labels is known as the validation loss. The goal is to minimize the validation loss while training the model. A low validation loss indicates that the model can generalize well to new, unseen data.

Importance of Low Training and Validation Loss

Achieving low training and validation loss is crucial for the following reasons:

  1. Generalization to New Data: A model with low training and validation loss indicates that it has learned to recognize meaningful patterns in the data rather than memorizing the training examples. Such a model is more likely to generalize well to new, unseen data, making it reliable in real-world scenarios.
  2. Overfitting Prevention: Overfitting occurs when a model becomes too specialized in learning noise and irrelevant details in the training data. A high training loss but low validation loss might indicate overfitting. Balancing the training and validation loss helps prevent overfitting and ensures the model’s robustness.
  3. Real-World Applicability: For machine learning models to be useful in real-world applications, they must provide accurate predictions on unseen data. Achieving low training and validation loss is a sign that the model has learned the essential patterns and can be trusted to make reliable predictions.

So what does all this mean and why is it important? It comes down to reliable and repeatable predictions within an acceptable margin of error. In the past data would be analyzed and regression or similar statistical techniques would be applied. The short comings of traditional models is the inability to account for small changes and/or patterns that can significantly change the outcome of the model. Predicting the seasonality of the weather, the purchasing habits based on climate changes or the identification cancers are all impacted by these subtle changes the data used to measure these events. Training on a historical set of data (an epoch is called a data set that has been modelled once) repeatedly in a predictive manner is where the value lies. Doing so without bias and in being able to handle more data faster is the advantage of AI.

In an ideal scenario during each successive epoch (the actual learning process), the “loss” (commonly referred to as “training loss” and “validation loss” in machine learning) should generally decrease (or at least the loss values should fluctuate in a negative direction). The training loss measures how well the model is performing on the training data, while the validation loss measures its performance on unseen data (validation set). Think of these as tests given with the same answers over and over again, the goal would be to improve your score after each successive test without overfitting.

Being able to handle these tests of large sets of data repeatedly and “fit” the model data to the training data (and subsequent data to make a prediction from) is the “magic” of the algorithm that drives the machine learning. With this can be statistical losses (overfitting) that can arise. Repeated modeling with multiple epochs is how machine learning work. Each epoch builds on the previous.

During the initial epochs, the model is still learning and trying to understand the underlying patterns in the data, so the loss will typically decrease. As the training progresses, the model becomes more familiar with the data, and the loss should continue to decrease further.

It is important to note there is an exception that this ideal behavior might not always be observed. In some cases, the loss might fluctuate or even increase during certain epochs. This phenomenon is known as overfitting. Overfitting occurs when the model becomes too specialized to the training data and fails to generalize well to new, unseen data. This would be the test that never changes the order of the questions and answers. Like a student the model recognizes there is an order to the correct answers. If this is not varied then the model will not adapt to changes in the model.

To address overfitting and ensure better generalization, techniques such as regularization, dropout layers, and early stopping can be used. Regularization methods help penalize large weights and prevent the model from becoming overly complex. Dropout layers randomly deactivate certain neurons during training, which reduces co-adaptation and encourages the model to be more robust. Early stopping involves monitoring the validation loss and stopping the training process when the loss stops improving or starts increasing. These methods “encourage” through a reward system the “learning” of a system rather than the memorization of data.

Focusing on training a machine learning model to achieve low training and validation loss, better ensures the model has learned to capture the underlying patterns in the data without overfitting. However, it’s important to balance the complexity of the model and the size of the dataset to avoid overfitting and ensure good generalization to new data. Adding these methods also adds additional layers of complexity and increases the prediction processing time. If this wasn’t problematic enough the two challenges in ML not commonly considered is the continued quality of data, and the diminishing return of repeated training on the same data sets. Don’t even get me started on the topic of AI overconfidence.


The goals of machine learning revolve around pattern recognition, prediction, decision-making, and automation. To achieve these goals, training a machine learning model involves minimizing both training and validation loss. A low training loss signifies that the model is learning effectively from the training data, while a low validation loss demonstrates the model’s ability to generalize well to new, unseen data.

By aiming for low training and validation loss, machine learning practitioners (both programmers and end-users) can develop robust and accurate models that have a wide range of real-world applications. Striking this balance ensures that machine learning continues to revolutionize various industries and contributes to the advancement of AI technology. As we progress further into the age of AI, the pursuit of low training and validation loss will remain a fundamental aspect of machine learning research and practice. Turns out there’s a lot of thought put into learning considerations. The more we understand ourselves, how we’ve come to think the way we do only improves the models themselves. While the models themselves do not rationalize, the people behind them do. The (people behind the) algorithms are smarter than I thought.

Want to learn the fundamentals of AI, check out IBM’s free course on (you guessed it!) “Fundamentals of AI”. There are currently three free beginner courses”

  • AI Ethics
  • AI Concepts
  • Introducing AI

These will get “Your Intelligence” model started. Just be sure to learn the material and reduce your personal training and validation loss as you start your AI/MML training.

  • Images supplied by Sablity.AI – Stable Diffusion.

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