Causal AI: Evolving from recognition to reasoning

Artificial intelligence is at a pivotal point in its evolution, moving right into a recent era that goes beyond easy pattern recognition to reasoning, and causal AI is on the forefront of this evolution.

Causal AI offers insights not only into what is occurring, but why. This leap in decision-making intelligence has the potential to redefine the marketplace as businesses use these tools to enable smarter, more responsive processes. This next phase of AI evolution will shape the longer term of the AI ecosystem. Causal AI, unlike traditional models that depend on statistical patterns, is designed to offer explanations and reasoning, in line with Scott Hebner, principal analyst at theCUBE Research.

“Lots of people speak about generative AI … but as a frontrunner, you furthermore mght need to be considering ahead, particularly with AI, which is moving at an excellent faster pace than previous technological transformations,” Hebner said. “It’s necessary to take a futuristic view … so I’m doing a series of 5 papers in regards to the advent of causal AI.”

Hebner spoke with theCUBE Research’s Principal Analyst Rob Strechay, during an AnalystANGLE segment on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how causal AI will enable a more structured approach, integrating large and small language models to construct a more cohesive, responsive AI and machine learning ecosystem.

Understanding cause and effect

As organizations push the boundaries of AI, they’re realizing that today’s models — particularly large language models — are effective at identifying patterns and making predictions but fall short in explaining the reasoning behind those predictions. LLMs operate on statistical probabilities, that are useful, but could be limiting in dynamic, ever-changing environments.

“Today’s predictive models and generative AI models which can be embodied within the LLMs are pattern recognition machines. They operate on statistical probabilities … in a static world,” Hebner said. “What causal AI will let you know is how those statistical probabilities change when the world around you changes.”

Causal AI begins with agentic AI, which brings together AI agents in an ecosystem of AI large language models and domain-specific small language models to know cause-and-effect relationships, a critical consider helping humans problem-solve and make higher decisions, as discussed within the article “The Causal AI Marketplace,” authored by Hebner.

Organizations are consistently in flux, and for AI to actually understand how the business operates, it must give you the option to know cause and effect, in line with Hebner. Why? Because in business all the pieces is a cause and all the pieces is an effect — and AI needs to maintain up with that reality.

“Causal AI is all about helping people understand how the business operates. Then from there, it supports a dynamic world of change,” Hebner said. “It’s going to permit those statistical models, probabilities that traditional AI and machine learning operates upon, to adapt.”

The flexibility to simulate and test what-if scenarios based on the model is one other good thing about causal AI. It offers businesses the flexibleness to prescriptively model best-case outcomes impacting scenarios around profitability, customer retention and revenue.

“Today’s models are pretty good at predicting what you must do [and] forecast, and so they generate the what, but they’ll’t let you know the way it did it. And it actually can’t let you know why that is the very best answer,” Hebner said. “Causal AI goes to start out to incrementally allow that explainability to be infused into these models, not only descriptively and predictively, but … prescriptively.”

The role of specialised AI models and agentive AI

While LLMs provide a general-purpose framework, small language models are designed for targeted tasks, allowing businesses to optimize AI for specific needs. These models ensure high data protection and specialized application.

“You wish small language models which can be specialized, secure and sovereign, that understand each of the domains inside a business,” Hebner said.

He envisions a network of SLMs and LLMs where AI agents collaborate and contribute specific expertise. “The entire thing goes to return together in an architectural approach, and that’s going to represent the longer term,” he added.

This architectural approach will allow AI systems to interact with one another more effectively. LLMs will provide general knowledge, while SLMs concentrate on specific domains, making a seamless flow of knowledge, Hebner explained.

“We’re moving toward an ecosystem where AI agents teach one another, learn from one another and grow to be smarter and smarter,” he added. “It’s going to be an architectural approach where agents work collaboratively, and that’s going to be key to the longer term.”

The case for causal AI

Causal AI isn’t just an idea on the horizon — it’s already gaining traction in industries that require a deeper level of decision intelligence. A recent Databricks Inc. and Dataiku Inc. survey of 400 AI professionals shows that over half of them are already using or experimenting with causal AI, which is anticipated to be one of the crucial adopted AI technologies in the approaching yr, in line with Hebner.

“The primary technology that’s not getting used today, but they plan to make use of over the following yr, is causal AI,” Hebner said. “[Customers] need to construct higher [return on investment] use cases, which require … reasoning, decision intelligence problem-solving and explainability.”

Because the demand for more explainable and adaptable AI grows, causal AI will likely play an increasingly critical role in how businesses leverage artificial intelligence for higher decision-making. The longer term of AI, in line with Hebner, shall be shaped by its ability to know cause and effect. This shift could redefine how firms approach problem-solving and decision-making in an increasingly dynamic marketplace.

“Gen AI is the massive thing today. It wasn’t five years ago. I feel over time, causal AI and the notion of why things occur and [questions such as], ‘What can I do to enhance things?’ will grow to be a much bigger and larger a part of the combination here,” Hebner said.

Here’s the CUBE’s complete AnalystANGLE segment with Scott Hebner:

Photo: SiliconANGLE/Bing