This article was developed with the assistance of AI language models (Gemini, Claude and ChatGPT) for editing and technical review.

I imagine there is an invisible contest underway over how artificial Intelligence systems represent Israel and its surrounds. It is not a battle fought through hashtags or newspaper op-eds, but one unfolding inside the architecture of the systems that millions of people increasingly consult for news, context, and explanation of the world’s most contested conflicts.

In the age of large language models, geopolitics is increasingly filtered through machines that represent ideas as mathematical relationships. The battle over Israel’s story, and that of its neighbors, is increasingly fought not only in headlines, but in the hidden geometry of machine learning and “understanding.”

For us as humans to grasp this emerging struggle, we must look at the mathematical landscape where modern AI systems store meaning.

Large language models—including systems such as ChatGPT, Claude, and Gemini—do not process language the way humans do. Instead, they represent words, phrases, and concepts numerically through embeddings: high-dimensional vectors that encode relationships between ideas.

These vectors exist within what researchers call a representation space. In that space, statistical proximity often corresponds to semantic similarity. Concepts that frequently appear together in text—such as geographic regions, political actors, or ideological terms—tend to cluster in related areas of the model’s internal landscape.

This structure is learned from enormous datasets that include news reports, books, academic writing, and online discussion. The resulting topology of meaning becomes a mathematical reflection of how human language describes the world.

I admit that I do not have the capability to travel this space in terms of machine and higher order programming languages. Yet, I can grasp the outline of that space. In a rough and ready sense, this is what I sought to map as I tried to make sense out of a squatter settlement in a sprawling Latin American city. This was also a geo-political vector space. But strangely different.

This topology is not purely passive. It can also be influenced.

In effect, when ideas become coordinates, influence becomes geometry; moreover, AI does not argue about meaning—it measures distance between concepts. This is the vector space where bias does not........

© The Times of Israel (Blogs)