Finding Nature's Deeper Secrets

Volume 4 | Issue 10 | October 2024

Cosmology of Light Newsletter

Hello Friends,

With advances in quantum sensing technologies and Large Language Models (LLMs), technology is at a point where some of Nature's deeper secrets will quickly become accessible. In this newsletter I will share two initiatives to access and leverage these deeper secrets. The first has to do with altering quantum sensing models based on a perception of deep patterns that must exist at the quantum levels. The second has to do with altering the architecture of LLMs to perceive Nature's deeper patterns in language.

Warmly,

Pravir

Altering Quantum Sensing Models

The structural layers of matter and life reveal an inherent fourfold symmetry, discernible in the quantum particle structure within the Standard Model, the atomic arrangement of the Periodic Table, and the molecular organization at the cellular level. In other words, this four-part pattern emerges across multiple scales, and through a process of reverse extrapolation, it suggests that a similar foundational pattern—possibly the root of all fractal arrangements in matter and life—must exist within the quantum realm.

Advances in technologies such as magnetometry, gravimetry, optical lattice timekeeping, inertial sensing, electrometry, quantum-enhanced electron microscopy, and the growing field of atomtronics (which increasingly enables the wave-like nature of atoms to be utilized) will make it possible to delve deeper into quantum realms. These advances will allow us to uncover new patterns and properties, which Large Language Models (LLMs) and advanced systems like Large Quantitative Models (LQMs) can help interpret and synthesize.

This approach, as noted in a recent Springer Nature paper, is likely to reveal the fourfold root pattern along with other fundamental structures in matter and life that may reinforce and expand the frameworks of physics, chemistry, and biology. This deeper understanding could unlock numerous applications across materials science, medical technology, and biohacking, by enabling the enhancement of genetic codes from the ground up rather than through top-down intervention. Additionally, insights from innovative quantum sensors designed to probe these realms could lead to new kinds of quantum computers, developed from experimental data drawn directly from quantum environments.

Read More in the Forbes article, The Future of Quantum AI.

Altering Large Language Models

Language is our lens for interpreting and engaging with reality. Given that a fundamental fourfold pattern structures both matter and life—and that language reflects this reality—it's likely that this same pattern is embedded within the very structure of language itself. This pattern may even be the universal foundation underlying all languages.

With the capabilities of Large Language Models (LLMs), this hypothesis can now be tested. Recently, I had the opportunity to present a paper titled Enhancing Abstraction in Large Language Models Through Nature-Inspired Semantic Patterns at an IEEE conference hosted by IBM Research at the T.J. Watson Research Center. The paper proposes an enhancement to the Transformer Architecture at the core of today’s LLMs to allow them to intuitively recognize if such a pattern indeed exists.

Below, I present the paper's abstract and a mirrored version of the talk delivered at the IBM T.J. Watson Research Center.

Abstract — Recent advancements in artificial intelligence emphasize improving the abstraction capabilities of Large Language Models (LLMs) by integrating structured knowledge and sophisticated neural architectures. This paper proposes a novel approach grounded in a fourfold pattern observed at multiple levels of granularity in nature, referred to as 'Kn', 'Po', 'Pr', and 'H', which correlates with concepts of knowledge, presence, power, and harmony. We hypothesize that leveraging this intrinsic structure in language could enhance LLMs' ability to abstract underlying themes, recognize subtle relationships, and infer unstated implications, similar to the hierarchical complexity seen in natural systems from quantum particles to cellular structures. By incorporating these elements into the transformer architecture of LLMs, by enhancing self-attention through additional Q, K, V weights to thereby also optimize computational requirements, we aim to demonstrate a more nuanced understanding and generation of text, at least paralleling and perhaps even moving beyond normal human-like comprehension and reasoning.

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