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AI Breakthrough for Cognitive Reasoning
Amzeal News/10601031
NORTH SYDNEY, Australia - Amzeal -- Sydney Researcher Pushes AI to Think Like a Human Using New 'Z Model' Framework.
In a remarkable breakthrough emerging not from Silicon Valley but from a suburban home in Sydney, independent researcher Jason Notary has developed a new mathematical framework that could revolutionize how artificial intelligence thinks.
The Z Model, a compact but powerful toolkit, helps large language models (LLMs) like ChatGPT move beyond simple pattern recognition to perform something astonishingly close to human synthetic reasoning — the kind of problem-solving that considers scale, context, uncertainty, and contradictory inputs all at once.
"We're giving AI a compass — not a script," says Notary. "The Z Model tells the machine where it is in the problem space, and then it has to figure out the answer using first principles."
More on Amzeal News
Using a fusion of dimensional physics, energy-state calculations, and cognitive load modelling, the Z Model has been able to help AI engines predict real-world events — from hurricane seasons to political unrest and global vaccine hesitancy — with startling accuracy. But it's not just prediction that's new. It's how the AI arrives at those predictions.
A New Layer of AI Thinking
Current AI systems work primarily through statistical correlation: predicting the next word or answer based on training data. But Notary's Z Model adds a new dimension: it demands the system construct answers based on dynamic variables like entropy, scale, and energy loss.
"It's like going from parroting facts to reasoning from scratch — while holding multiple probabilities in mind at once," he explains.
In recent demonstrations, the model was used to simulate the 2022 Atlantic hurricane season, predict geopolitical flashpoints, and even diagnose systemic misinformation by comparing vaccine mortality data against misinformation vectors — all with no training data specific to those problems.
More on Amzeal News
A Global Framework Born in Australia
Despite lacking a formal academic post or corporate backing, Notary's open approach and wild-card thinking have caught the attention of AI insiders and strategic consultants alike.
"The Z Model isn't trying to replace AI — it's upgrading it," he says. "And we're doing it from Australia, outside the usual echo chamber."
He's already released components of the model publicly and is open to partnerships — from governments and AI labs to humanitarian agencies or climate forecasters.
Media Contact: Jason Notary
Cloud & Virtual Responsibility Managed
146 High St, North Sydney NSW 2060
+61 450 740 878
info@cloudsystems.online
https://cloudsystems.online
In a remarkable breakthrough emerging not from Silicon Valley but from a suburban home in Sydney, independent researcher Jason Notary has developed a new mathematical framework that could revolutionize how artificial intelligence thinks.
The Z Model, a compact but powerful toolkit, helps large language models (LLMs) like ChatGPT move beyond simple pattern recognition to perform something astonishingly close to human synthetic reasoning — the kind of problem-solving that considers scale, context, uncertainty, and contradictory inputs all at once.
"We're giving AI a compass — not a script," says Notary. "The Z Model tells the machine where it is in the problem space, and then it has to figure out the answer using first principles."
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Using a fusion of dimensional physics, energy-state calculations, and cognitive load modelling, the Z Model has been able to help AI engines predict real-world events — from hurricane seasons to political unrest and global vaccine hesitancy — with startling accuracy. But it's not just prediction that's new. It's how the AI arrives at those predictions.
A New Layer of AI Thinking
Current AI systems work primarily through statistical correlation: predicting the next word or answer based on training data. But Notary's Z Model adds a new dimension: it demands the system construct answers based on dynamic variables like entropy, scale, and energy loss.
"It's like going from parroting facts to reasoning from scratch — while holding multiple probabilities in mind at once," he explains.
In recent demonstrations, the model was used to simulate the 2022 Atlantic hurricane season, predict geopolitical flashpoints, and even diagnose systemic misinformation by comparing vaccine mortality data against misinformation vectors — all with no training data specific to those problems.
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A Global Framework Born in Australia
Despite lacking a formal academic post or corporate backing, Notary's open approach and wild-card thinking have caught the attention of AI insiders and strategic consultants alike.
"The Z Model isn't trying to replace AI — it's upgrading it," he says. "And we're doing it from Australia, outside the usual echo chamber."
He's already released components of the model publicly and is open to partnerships — from governments and AI labs to humanitarian agencies or climate forecasters.
Media Contact: Jason Notary
Cloud & Virtual Responsibility Managed
146 High St, North Sydney NSW 2060
+61 450 740 878
info@cloudsystems.online
https://cloudsystems.online
Source: CloudSystems
Filed Under: Technology
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