Working in the Reliability Gap

Systems behave as designed except when they don't, and that gap is where reliability actually begins.

As a community, we design systems, we test them, we deploy them. On paper, the loop looks closed, with requirements, implementation, evaluation, and release. It is easy to assume that if a system "passed" that process, its behavior in the world will track what we had in mind.

That assumption holds, mostly. The reliability gap opens when the conditions shift, inputs change, models are updated, or people start using the system in ways we can't anticipate.

Systems built on Artificial Intelligence are moving quickly into everyday tools. The number of deployments grows, the range of use cases expands, the level of dependence deepens. People use these systems in thousands of ways, some mundane, some critical, some barely visible. As deployment accelerates, so does the degree to which these systems touch daily life, from the tools individuals use, to the systems institutions depend on, to the infrastructures that quietly shape how the world functions. With every new point of integration, the surface area for failure grows, and we have quietly moved from experimentation into the foundation of modern systems.

What has not kept pace is our visibility into how these systems behave, both once they are released and in the design and integration choices that shape that behavior long before deployment.

On the surface, many systems look fine. They are fluent, confident, and often helpful. They produce outputs that feel reasonable. But sounding good is not the same thing as doing good. A system can be unstable or drifting into inaccurate territory, and we may have no practical way to notice that change until it matters.

For many people, that instability is an unknown unknown. Known unknowns can be managed; unknown unknowns manage us. Rockin' M Lab exists to push instability into the first category, something we can name, inspect, and design around.

We care about what happens after deployment—whether a system continues to do what we want it to do (however that is defined in context), or whether it slowly drifts into behavior that no longer matches our intent.

Reliability, in this sense, is not a static label. It is an ongoing question about behavior under real conditions, a question we have to keep asking, not a stamp we apply once.

To work on that question in a useful way, we approach reliability across three connected domains:

How people use the tools — the habits, expectations, and workflows that shape real interaction with intelligent systems.

Where and how the system appears in products and infrastructures — the integration decisions about when it acts, when it stays quiet, and what it is allowed to influence.

The tools that support reliable behavior over time — methods from statistics, machine learning, and Computational Intelligence; uncertainty, drift detection, evaluation, and monitoring that make instability visible instead of latent.

None of these domains is new on its own. The reliability gap sits at their intersection. Rockin' M Lab works in that crossing, not in a single lane.

Rockin' M Lab is an applied Artificial Intelligence lab focused on that intersection, with three pillars of work: Training, Product, and Research.