We are a full-service ML/AI development and consulting agency for enterprise clients who understand that true value lies beneath the surface.
We explore techniques that bridge the gap between input data and the meaningful representations learned within AI models. Here’s a simplified conceptualization:
Imagine, as a simplification, that the activation vector \( \mathbf{v}_i^{(l)} \) for input \( i \) at layer \( l \) could be directly computed from the input embedding \( \mathbf{v}_i^{(0)} \) via a linear transformation \( \mathbf{D}^{(l)} \):
\[ \mathbf{v}_i^{(l)} \approx \mathbf{D}^{(l)} \mathbf{v}_i^{(0)} \]
While real models involve complex non-linearities, this highlights the idea of a mapping from input to internal states.
LAT focuses on finding linear directions (probes) within these activation spaces that correlate with specific features. We train a linear probe \( \mathbf{w}^{(l)} \) at layer \( l \) to predict a target feature \( y_i \) from the activation \( \mathbf{v}_i^{(l)} \):
\[ \hat{y}_i \approx (\mathbf{w}^{(l)})^T \mathbf{v}_i^{(l)} \]
Here, \( \hat{y}_i \) estimates the strength of the feature for input \( i \), and \( \mathbf{w}^{(l)} \) is the learned direction vector representing that feature in layer \( l \)'s latent space.
By substituting the simplified direct access model into the LAT probe equation, we can conceptualize how an internal feature relates back to the input embedding:
\[ \hat{y}_i \approx (\mathbf{w}^{(l)})^T \left( \mathbf{D}^{(l)} \mathbf{v}_i^{(0)} \right) = \left( (\mathbf{w}^{(l)})^T \mathbf{D}^{(l)} \right) \mathbf{v}_i^{(0)} \]
We can define an "effective probe" \( (\mathbf{w}_{\text{eff}}^{(0 \to l)})^T = (\mathbf{w}^{(l)})^T \mathbf{D}^{(l)} \) that acts directly on the input:
\[ \hat{y}_i \approx (\mathbf{w}_{\text{eff}}^{(0 \to l)})^T \mathbf{v}_i^{(0)} \]
This combined view suggests that, under simplifying assumptions, features detected deep within the model might correspond to specific linear combinations of input features. Exploring these relationships, even accounting for non-linearities, is key to understanding and controlling model behavior.
Build your conceptual AI pipeline visually. Click "Simulate" to see a latent space representation.
Latent Agency was founded on the principle that the most transformative AI insights often reside not in the obvious outputs, but within the hidden structures – the latent spaces – that models learn. We partner with forward-thinking enterprises to navigate the complexities of modern AI and machine learning.
Our team comprises experts in deep learning, natural language processing, computer vision, and MLOps. We go beyond off-the-shelf solutions, focusing on understanding the fundamental mechanics of AI models to tailor strategies that unlock unique competitive advantages for our clients. We believe in demystifying AI, translating complex concepts into actionable business value and empowering your organization to leverage the true potential beneath the surface.
We specialize in leveraging the internal representations learned by complex AI models, particularly Large Language Models (LLMs) and deep neural networks. Understanding and manipulating these 'latent spaces' allows for unprecedented control, interpretability, and efficiency.
Our approach moves beyond treating AI models as black boxes. By engaging with the underlying technology, we develop more robust, controllable, and effective AI solutions for the enterprise.
We offer a range of services to help you harness the full potential of AI:
Ready to explore the latent potential of your data and unlock new possibilities? Reach out to our team today.