Dug a bit deeper into the technical aspects of neural networks during the last months and went through various articles etc.. An excellent free read I found here https://neuralnetworksanddeeplearning.com/. To my great disappointment neural networks can mostly be considered as black boxes, at least as far as the inner layers of a neural network are concerned. There is basically no way to trace how the weights and biases e.g. parameters in these layers are set during training and one has to accept the output as it comes. Had a chat with GPT-4 about this and this is what it explained:

You:

Why are neural networks described as black boxes?

ChatGPT:

Neural networks are often described as black boxes because, although we can see the input and output, understanding how the network processes the information to get from one to the other is complex. The inner workings of a neural network involve numerous layers and nodes, with each node performing calculations using its weights and activation functions. As the network gets deeper, with more layers and nodes, it becomes increasingly difficult to trace how specific features of the input data influence the final decision or prediction. This complexity makes it challenging to interpret the model’s decision-making process, hence the term „black box.“

You:

Could you elaborate a bit more on the decision-making process in neural networks, like how the weights and activation functions and, I think, bias work together? And why is this a black box?

ChatGPT:

Sure, let’s dive a bit deeper into how neural networks make decisions. 

  1. Weights and Biases: Think of a neural network as a complex network of neurons, similar to the human brain. Each neuron in this network receives input from multiple other neurons. The „weight“ is a value that represents the strength of the connection between neurons. It determines how much influence one neuron’s output has on another’s input. The „bias“ is an additional parameter that allows the neuron to adjust its output along with the weighted sum of its inputs. Bias helps the neuron to be more flexible, allowing it to activate (or not) even if all inputs are zero. 
  2. Activation Functions: Once a neuron has calculated the weighted sum of its inputs and added its bias, it passes this value through an activation function. The activation function’s role is to decide whether the neuron should be activated or not, based on whether the neuron’s input is relevant for the prediction. Common activation functions include ReLU (Rectified Linear Unit), sigmoid, and tanh. Each of these functions has a different way of transforming input values into outputs that are passed to the next layer in the network. 
  3. Layer-by-Layer Processing: A neural network consists of multiple layers, starting from an input layer, through one or more hidden layers, to an output layer. Data flows from the input layer, through each hidden layer, to the output. In each layer, the process of applying weights, adding biases, and passing through activation functions repeats. The complexity and depth of a network can vary, with deep neural networks having many hidden layers. 
  4. Why It’s a Black Box: Each neuron’s decision to activate depends on a complex interplay of weights, biases, and its activation function, influenced by the input it receives from neurons in the previous layer. In deep neural networks, with potentially millions of parameters (weights and biases) and nonlinear transformations (activation functions), tracing how input data transforms into output predictions becomes incredibly complex. While we can see the inputs and outputs, and even know the values of the weights and biases, the sheer volume of operations and interactions makes it hard to intuitively understand or predict how a small change in input affects the output. This complexity and lack of transparency in the decision-making process is why neural networks are often described as black boxes.

What do you think? Would it be possible to, like, say, debug the decision-making process in a neural network?

ChatGPT:

Yes, it’s possible to some extent to „debug“ or interpret the decision-making process in neural networks, and there has been significant research focused on making these models more interpretable and transparent. Several techniques and tools have been developed for this purpose: 

  1. Visualization: Techniques like feature visualization and activation maximization help us see what features or patterns a neural network is paying attention to when making decisions. For convolutional neural networks (CNNs), often used in image processing, visualization can show which parts of an image are most influential in the network’s decision. 
  2. Attention Mechanisms: Originally developed for natural language processing tasks, attention mechanisms can also provide insights into which parts of the input data the model is focusing on when making predictions. This can help in understanding how the model is processing sequential data like text or time series. 
  3. Layer-wise Relevance Propagation (LRP): This method backtracks from the output layer to the input layer to identify which neurons contributed most to the final decision. It helps in understanding the relevance of each part of the input data. 
  4. Local Interpretable Model-agnostic Explanations (LIME): LIME helps explain predictions of any model by approximating it locally with an interpretable model. It allows us to understand why the model made a certain decision for a specific instance. 
  5. Shapley Additive exPlanations (SHAP): SHAP values provide a way to understand the contribution of each feature to a particular prediction. It is based on game theory and offers a solid theoretical foundation for explaining model predictions. 

While these techniques can offer insights into the decision-making process of neural networks, fully „debugging“ them in the traditional sense of the term remains challenging. The complexity and nonlinearity of these models mean that understanding them fully, in the way we understand simpler, linear models, may not always be possible. Nonetheless, efforts in explainable AI (XAI) continue to improve our ability to interpret and trust the decisions made by neural networks. 

You:

Okay, say there is a hallucination in the results and we are not satisfied with it and we want to trace this, so we debug it, debug through the neural network to find where this hallucination came up. Is this feasible? Is this possible?

ChatGPT:

Tracing a specific „hallucination“ or incorrect output back through a neural network to find its exact origin is challenging but not entirely out of the realm of possibility, especially with the advancements in explainable AI (XAI). However, the feasibility and effectiveness of such debugging efforts depend on several factors: 

  1. Model Complexity: The more complex the model (i.e., the more layers and parameters it has), the harder it is to trace back specific decisions or errors. Deep neural networks, especially those used in tasks like image recognition or natural language processing, can have millions of parameters, making this task daunting. 
  2. Interpretability Tools: Tools and techniques like those mentioned earlier (LIME, SHAP, feature visualization, etc.) can provide insights into what the model is „thinking“ or which features it considers important. However, these tools often offer a high-level understanding rather than a step-by-step trace of how each input led to the final output. 
  3. Error Analysis: In practice, debugging a neural network often involves analyzing errors or hallucinations on a dataset level to identify patterns or commonalities among the incorrect outputs. This can sometimes lead to insights about where the model might be failing. 
  4. Modification of Inputs: Another approach to understanding hallucinations is systematically modifying inputs and observing how the outputs change. This can sometimes help identify which aspects of the input data are triggering the incorrect outputs. 
  5. Model Simplification: Simplifying the model or using a simpler model to replicate the error can make it easier to understand the decision-making process. This approach, however, might not always be feasible or desirable, especially if the complexity is necessary for the task. 

Directly debugging a neural network like traditional software, where one can step through code line by line, is not feasible due to the nature of these models. However, the field of XAI is rapidly evolving, and researchers are continuously developing more sophisticated methods for understanding and interpreting the behavior of neural networks. These efforts aim to make it increasingly possible to identify, understand, and correct errors or „hallucinations“ in neural network outputs.