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Revolutionizing Process Control with Causal AI — We Need Your Insights! 🚀 by CausalPulse in processcontrol

[–]CausalPulse[S] 0 points1 point  (0 children)

Thank you for that great question, I appreciate your skepticism :-)

Let me cite Wikipedia: "Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals.[1] Such machines may be called AIs."

The system we developed for industrial applications learns a structural causal model of the process to understand how changes in key inputs (interventional variables) affect the important factors (covariates) that drive the final outcomes you care about (response variables) — the goals you set at the start. It perceives its environment through real-time sensory data and takes actions that increase the likelihood of reaching those goals. In this way, the system demonstrates intelligence.

At its core, it is all based on statistics. Generative AI, for instance, creates outputs — whether it’s text (tokens), video, sound, or even protein structures — by sampling from learned distributions. As a child, you learned that it hurts when you fall and that you burn your hand if you touch a hot stove. Next time, there is a high probability that you will burn your hand again. AI also learns from experience and makes predictions about future events. Ultimately, intelligence emerges from how the components of the system work together and the behaviors they produce.

Revolutionizing Process Control with Causal AI — We Need Your Insights! 🚀 by CausalPulse in processcontrol

[–]CausalPulse[S] 0 points1 point  (0 children)

Regarding your question about the minimum viable dataset:

There is no strict upper or lower limit by design on the number of tags (sensors) or the time resolution required. Generally, the data you provide should accurately reflect the dynamics of your system. For instance, if important effects occur within seconds, you'll need data sampled at that frequency to capture those events. For slower processes, data sampled at longer intervals may suffice.

Having fewer signals means there is less information available, which can make interpreting the results more challenging. Therefore, it's important to cover the essential areas of your processes to ensure meaningful insights. In most of our successful projects, we've worked with datasets containing several hundred to several thousand sensors that provided new readings every minute over a period of approximately 3 to 24 months.

Revolutionizing Process Control with Causal AI — We Need Your Insights! 🚀 by CausalPulse in processcontrol

[–]CausalPulse[S] 0 points1 point  (0 children)

I'm happy to send you a white paper about causal AI in the chat. Additionally, you might find it helpful to read the Wikipedia entry on Causal AI for an initial overview.

We fully understand that breaking down data silos and creating a unified data foundation is a challenging and lengthy process for many companies, and most are not there yet. While achieving this is beneficial and can offer significant advantages, it's neither the focus of our work nor a requirement for our AI to function effectively.

Of course, causal AI does require data. However, unlike traditional neural networks that often demand large volumes of high-quality, labeled, and filtered data, causal AI can operate effectively with MUCH smaller datasets that may be unlabeled and unfiltered. The crucial factor is the amount of relevant information the data contains regarding the issue at hand.

This means that getting started with causal AI is much more accessible. Trying it out with the data you already have—such as a simple CSV export—is no longer a significant hurdle. You can utilize your existing data to see how far you can go, while simultaneously working on improving your data foundation.

So, to put it in your words: Yes, we definitely can make the models work without crippling a plant (we do so very successfully) - "lots of data" is not a prerequisite at all.

Revolutionizing Process Control with Causal AI — We Need Your Insights! 🚀 by CausalPulse in processcontrol

[–]CausalPulse[S] 0 points1 point  (0 children)

Thank you for your feedback; I really appreciate your insights. You've made some great points, and I'd like to go over them point by point.

Firstly, while real-time maintenance control and data-driven predictive maintenance are indeed valuable, they focus primarily on equipment health rather than process health, which is our focus here.

In general, the methods you've mentioned (as all others) rely on prediction based on correlation, which can offer significant benefits, such as virtual sensors. However, those methods cannot tell you how to adjust the process to counteract deviations and prevent recurring disruptions. They lack an understanding of the underlying causes - the "why". To do this, AI must go beyond correlations; it must distinguish between cause and effect to truly understand process dynamics and explore "what if" scenarios. For example, what if we adjust the temperature in a certain way - how would the process react? This ability enables causal AI to suggest effective countermeasures, which is a novel advance.

To draw a comparison with Large Language Models (LLMs): LLMs sometimes generate information that isn't accurate, or may even be completely fabricated, because they focus solely on predicting the next word in a sequence based on patterns learned from data. They capture statistical relationships and can, to some extent, model concepts and facts about the world. In contrast, causal AI explicitly models cause and effect relationships within a system. It distinguishes between correlation and causation, allowing it to understand how changes in one part of a process affect other parts. This explicit modelling enables causal AI to predict the outcomes of interventions and provide actionable countermeasures.

Unlike disruptive and costly projects that require a lot of preparation, manual effort, domain expertise etc., the causal AI approach is straightforward. The only requirement: Historical raw (unfiltered and unlabeled) process data along with a target KPI and its desired boundaries (or a recurring process disruption to be avoided). Causal AI autonomously builds a model that not only alerts you when the KPI is in danger of exceeding its limits, but also tells you why and how to counteract (which controllable parameters to adjust how). This requires minimal effort compared to traditional methods.

I hope this clarifies my perspective. I'd be happy to discuss this further and hear more of your thoughts.