I want to share what I've been working on during my personal time lately. Along with my colleague Cyril Sadovsky, we've create a proposal titled "THEORY MACHINE - BRIDGING GENERATIVE AI CREATIVITY AND TURING MACHINE FORMALITY: A PROPOSAL". It's an exploration into how Generative AI and Turing Machines might integrate.
This idea started to take shape during a conversation with Cyril about Turing machines. I remembered a story from my father about Alan Turing. He told me that during his university days in the early '80s in a communist country, Turing's work was almost unknown (Declassification of Bletchley Park’s operation in mid 70 and “Iron Curtain” fall in mid 80). This didn't directly inspire our work but nudged me to join Cyril on this quest. It reminded me that all great ideas begin as mere theories, often unrecognized. As Sigrid said to Jon Snow in Game of Thrones, “You know nothing” – a reminder that the end of a journey is unknown, but starting it is what counts.
In our paper, we propose the 'Theory Machine', a conceptual framework that aims to combine the imaginative potential of Generative AI with the structured precision of Turing Machines. Our goal is to see how Foundational Models might be enhanced in a self-optimizing system. This proposal isn't about claiming a breakthrough; it's about exploring a hypothesis where AI-generated Turing Machines could evolve and adapt by processing continuous streams of data, transforming abstract 'noise' into structured, testable forms.
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| Fig.1 - Our approach is based on the idea of Recursive Language/Turing Machines, which we envision as a superset of traditional Turing Machines and Neural Networks |
After introducing our 'Theory Machine' in the paper, it's important to break down what this really means for those who aren't deeply immersed in technical jargon. Simply put, the 'Theory Machine' is like a sophisticated translator that takes the language of AI creativity and meshes it seamlessly with the logical world of Turing Machines, which are the fundamental principles guiding how computers operate.
Imagine a scenario where AI isn't just following commands, but actively learning, adapting, and evolving as it receives new information. It's like to teaching a child not just to memorize facts, but to understand, question, and even challenge them to foster growth. This is what we envision with the 'Theory Machine'.
For businesses, this could mean AI systems that not only perform tasks but also continuously improve their methods for greater efficiency. In healthcare, AI could evolve to better predict patient needs and treatment outcomes. Even in everyday life, this could lead to smarter, more intuitive technology that understands and adapts to your preferences and habits over time.
Our proposal is the first step in exploring these potentials. It's about setting the stage for AI systems that aren't just tools, but partners in problem-solving and innovation.
I would also recommend read a Cyril's blog post.
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