Who Needs Neural Networks? The Generative Prowess of State Transition Models

This article appeared in The New Stack and quoted Franz’s CEO, Jans Aasman.

Contrary to popular belief, not all generative models for AI require deep learning, neural networks, and the large language models (LLMs) many consider synonymous with Generative AI, if not AI itself. Traditional non-statistical AI approaches, which consists of rules-based systems and are typified by symbolic reasoning, are just as viable, if not more so, for certain generative model use cases.

In fact, there are generative models that involve both non-statistical rules and probabilistic, or statistical, measures. According to Franz CEO Jans Aasman, some of these AI models utilize a “rules-based, statistical approach. If you have rules, rules can still say, ‘if the probability is higher than this, let’s do this. If it’s less than this, let’s do that’.”

STM systems scrutinize the various states — and the relationships between them — that an entity, like a patient, goes through. The objective is to determine the likelihood of the entity starting at one state and eventually reaching another in a manner that’s consistently predictable. For example, “You have many states you can be in as a person,” Aasman said. “You can have diabetes, hypertension. You can have a blood value higher than a certain value.”

Read the full article at The New Stack.