AI startup company, Counterintuitive, has set out to build “reasoning-native computing,” enabling machines to understand rather than simply mimic. Such a breakthrough has the potential to shift AI from pattern recognition to genuine comprehension, paving the way for systems that can think and make decisions – in other words, to be more “human-like.”
Counterintuitive Chairman, Gerard Rego, spoke of what the company terms the ‘twin trap’ problem facing AI, stating the company’s first goal is to solve two key problems that limit current AI systems that prevent even the largest AI systems from being stable, efficient, and genuinely intelligent.
The first trap highlights how today’s AI systems lack reliable, reproducible numerical foundations, having been built on outdated mathematical grounds. Examples include floating-point arithmetic that was designed decades ago for speed in tasks including gaming and graphics. Precision and consistency is therefore lacking.
In numerical systems, each mathematical operation introduces tiny rounding errors that can build up over time. Because of this, running the same AI model twice can provide different results, causing non-determinism. Inconsistency of this nature makes it harder to verify, reproduce, and/or audit AI decisions, particularly in fields like law, finance, and healthcare. If AI outputs can not be explained or proven clearly, they become ‘hallucinations’ – a term coined for their “lack of provability.”
Modern AI has a fundamental struggle with precision that lacks truth, creating an invisible wall. The flaw has become a rigid limit, affecting overall performances, increasing costs, and wasting energy on noise corrections.
Modern AI struggles with precision that lacks truth, creating an invisible wall. The flaw has turned into a rigid limit, affecting performance, increasing costs, and wasting energy on computational noise corrections.
The second trap is found in architecture. Current AI models have no memory. Instead, they predict the next frame or token with no reasoning that helped them achieve the prediction. It’s like predictive text, just on steroids, the company says. Once modern models output something, they don’t retain why they made such a decision and are unable to revisit or build on their own reasoning. It may appear that AI has reason, but it’s only mimicking reasoning, not truly understanding how conclusions are reached.
“Counterintuitive is building a world-class team of mathematicians, computer scientists, physicists and engineers who are veterans of leading global research labs and technology companies, and who understand the Twin Trap fundamental and solve it,” Rego said.
Rego’s team has more than 80 patents pending, spanning deterministic reasoning hardware, causal memory systems, and software frameworks that it believes has the potential to “define the next generation of computing based on reasoning – not mimicry.”
Counterintuitive’s…
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