Embedding Alignment on AIBussin — AI applications, systems and books

Can One Embedding Space Be Translated Into Another?

Mon, 07 Sep 2026 14:00:00 +0000

Part VI — Crossing Embedding Spaces · Can a map be learned at all?

The question, stated carefully

We have two encoders, E_A and E_B, and a set of objects x. Each object has two representations: E_A(x) in space A, E_B(x) in space B. Chapter 16 established that these are distinct declared spaces with no coordinate correspondence assumed by default. When dimensions permit, raw cross-space arithmetic is numerically possible, but without an alignment contract it has no authorized cross-space semantics. That is not the same claim as “the spaces are unrelated.” Chapter 16 also measured, between several genuinely independent encoders, substantial neighborhood overlap and high linear CKA — real structural agreement, existing side by side with the absence of any authorized coordinate correspondence. Those two facts coexisting is exactly what makes this chapter’s question worth asking rather than settled in advance.

Did the Bridge Preserve the Space?

Mon, 07 Sep 2026 14:30:00 +0000

Part VI — Crossing Embedding Spaces · What did each measurement actually certify?

One bridge, eight measurements

Chapter 20 built the machinery that turns preservation evidence into a scoped authorization — a requirement, a measured value, and a decision, all cited together. What it deliberately did not do is ask how much any one of those measured values is actually entitled to say. That is this chapter’s entire job.

A preservation metric is a sensor. Before trusting its reading, ask which failure modes the sensor is even capable of seeing.

Teaching Tiny Models to Think Big: Distilling Intelligence Across Devices

Fri, 27 Jun 2025 23:24:14 +0100

🧪 Summary

As AI developers, we often face the tradeoff between intelligence and accessibility. Powerful language models like Qwen3 run beautifully on servers but what about on the edge? On devices like Raspberry Pi or old Android phones, we’re limited to small models. The question we asked was simple:

Can we teach a small model to behave like a large one without retraining it from scratch using only its outputs and embeddings?