Understanding AI

Mastering Neural Network Feature Learning: Towards Transparent AI in the Age of the AI Act

In the era defined by rapid advancements in artificial intelligence (AI), understanding the complexity of neural network operation has become not just a matter of academic curiosity but a pressing necessity. This need is further amplified by the advent of regulatory frameworks like the European Union's AI Act, which mandates a new level of transparency and explainability in AI systems. The Average Gradient Outer Product (AGOP) mechanism aids in providing transparency into the inner workings of neural networks, helping to demystify the "black box" nature of AI systems as required by the AI Act.

AGOP: Bridging the Gap Between Neural Networks and Explainability

The essence of AGOP lies in its ability to demystify the process by which neural networks learn and identify relevant features from data. This innovative approach posits that the features extracted by any layer of a neural network are proportional to the average gradient outer product concerning the input to that layer. This might sound a bit confusing first so let’s break it down:

Imagine a neural network is like a complex machine in a factory, and its job is to sort incoming boxes (which are the data) into categories. Each layer of the machine is a checkpoint that makes its own decisions about what's important about the boxes to help with sorting later on.

Now, the "features" are the details on the boxes that the machine uses to sort them, like size, color, or labels. The machine looks at all these features to decide where each box should go.

The "average gradient outer product" sounds technical, but it's really just a tool the machine uses to decide which features are the most important. "Gradient" means change, so you can think of it as looking at which features change the most consistently when the boxes are moving through the machine's checkpoints. If every time a red box passes through, something specific happens, the machine starts to learn that "red" is an important feature for sorting the boxes.

So, when we say the features extracted by any layer of the neural network are "proportional to the average gradient outer product concerning the input to that layer," it means that the machine determines the importance of a feature (like color, size, etc.) based on how consistently that feature is associated with a certain type of sorting decision as the boxes move through that checkpoint.

AGOP: A New Way to Understand Language AI

In a recent Study this method was used on GPT-2, an AI model that processes language. It has proven effective in figuring out which words or phrases are considered similar by the AI. Here’s how it works:

Tokens and Embeddings: In AI language models, words and phrases are turned into numerical values called tokens, which are further represented in a multi-dimensional space known as embeddings. This is how AI understands and works with language.

Finding Relationships: The recent study found that by using AGOP, we can identify clusters of these tokens that the AI considers similar or related in meaning.

Practical Application: For example, when this method was used on an AI trained with the TinyStories database, AGOP was able to pick out groups of words related to specific themes like food and cooking or names of people and animals.

Rating Coherence: The groups of words identified by AGOP were then rated for how thematically coherent they were, essentially how well the words in a group fit together in a meaningful way.

AGOP: Enhancing Image Recognition AI

The AGOP method can also be adapted to work with Convolutional Neural Networks (CNNs), which are AI models designed to recognize and classify images.

Feature Detection: AGOP helped to pinpoint what elements in pictures the AI was focusing on to make its decisions. For example, it was able to highlight the presence of edges in images, which are fundamental features that CNNs use to understand objects and scenes.

Deepening Understanding: By examining AGOP in different layers of the network, researchers could see which parts of an image were most significant to the AI’s classification task. In a study with VGG19, a type of CNN, they found that AGOP at the first layer detected basic features like edges, while in deeper layers, it focused on more specific regions, like a dog’s eyes or a ladybug’s spots.

Comparing Techniques: The way AGOP identified important image features was different from previous techniques. Instead of looking at individual images, AGOP found a common pattern across all images that the AI used for making its decisions.

Conclusion: AGOP and the Future of AI Governance

The AGOP method represents a pivotal advancement in our quest to understand and govern neural network-based AI systems. By providing a clearer insight into how these models learn and make decisions, AGOP aligns perfectly with the objectives of regulatory frameworks like the AI Act, emphasizing the need for transparency and explainability in AI.

Best regards,

Robin Groh