Theoretical Science and Advanced Intelligence Laboratory

Where the model takes precedence over the traditions we trust (or not, I guess).

RHINELAB-RINALAB is, as its heart (for now) an open-source theoretical and semi-experimental (which means we do not have money) research laboratory in formation. We investigate the theoretical aspects, assumption stacks, and dependencies between fields — and dissect theoretical bodies with adversarial verification, and investigate its ground.

  • 01 Theory of Modelling
  • 02 Theoretical and Foundational AI
  • 03 Foundational Mathematics & Physics
  • 04 Philosophy
  • 05 Foundational and Theoretical Computer Science

About the lab

A laboratory built around foundational questions and frameworks, not consumption nor surface level application (though we also do it).

Founded on the principle of open-source, open-discourse, open-knowledge research, alongside the absolute necessity and foundational importance of 'logistics', RHINELAB works at the junction of theoretical physics, theoretical artificial intelligence, theoretical computer science, and the ontology of theory itself (or rather aimed there be), by generating questions and asking for the clarification they deserve. And, particularly, to investigate them fully, without hedging.

For a more technical introduction, you can review it here at the GitHub site.

What we mean by theory, in general

We are, at present, in the process of working out foundations -, practically means a build-up phase, by design. Most of our interest is in the theoretical aspects of inquiry: the assumption stacks, the scaffolding and dependencies, and the simplification gaps between fields and subjects of interest. Though, that said, they are the surface of the general line inquiry, for much of them are rather deep or not, depends on how far one will follow its thread, or the implication thereof, and the interpretation capacity under grounding there be.

A few directions recur throughout such would be interesting to inquire, that we want to answer and analyze. First, in artificial intelligence we look past the engineering to its theory of mind, asking the foundational questions and pressing for the relevant clarification. In theoretical computer science we study computing in more general terms — may or may not be Turing, but the middle ground between actual complex devices. In the theory of modelling, the model takes precedence over any theoretical body it serves, be it physics or biology, targeting the theoretical–practical mismatch, abstraction corruptions, and layering drifts. And in physics, both practical and foundational — from mathematical physics to material sciences.

Alongside the theory, on the practical and application aspect, we host practical work: redefining NLP–LLM structures against observed dilemmas such as catastrophic forgetting and language drift, material-science designs for cancer detection and solvent application, and hardware-hardwired neural formalism structures — a project of the lab in itself.

We are currently accepting team members and collaborators with the same mindset and sometimes, often times that is, of the same philosophy and long-burn commitment. While we do have to note of such having financial support being a resolution far away, it would be very much likely to be absolutely welcomed to have any given amount of support. The work here is in progress, and much of the preparation is unfinished - thus it will take a while until everything is in place.

The quest of library of Alexandria

While it might be of the grander scale that admitted, our focus, on the foundational identification, is the creation and sophistication thereof, for a laboratory. In cleaner words rather to be spoken, we focus on what said above, logistical system of knowledge is one of our fundamental goal. It is also partially why and how, that we desire, and commit to the conjecture thereof, that foundational research is of absolute necessity, and very much so the main directive that RHINELAB would have and must have, going forward. Such, includes everything imaginable that is consistent with its open source goal, and said to be considered, the institutional library, that perhaps, will aim to rival Alexandria, as the everlasting dream of a shadow in the past to aim forward, even if, it might not share the same fate of dissolution. Or that lack thereof, which depends on whom to operate it.

Within such, we invite collaborators and enthusiasts, of those that share our core concept, of humility and endurance, for collaborative and gentle consideration of others, the anthropology of human and respect, to then build this library - either monetary, or knowledge, or presence, or question or discourse. There might be anything humanocentric, it would be accepted - such is so, we are human doing science, not machines doing words switching. That is the purpose, and we welcome onboard those that believe in the same thing.

The core research & study fields

01

Theory of Modelling

The development of the theory of model, where model precedes axioms, and where the problem lies — the theoretical-practical mismatch, abstraction corruptions, layering drifts, and the ontology and epistemics of mathematical models.

02

Theoretical and Foundational AI

Artificial intelligence and its theory of mind: foundational questions, learning theory, the epistemology and ontology of learning, the prospect of AI itself, and the question that resembles, CRA, but is not, CRA.

03

Foundational Mathematics & Physics

From foundational mathematics to the foundational and mathematical physics — from material sciences and quantum wells to the re-concretisation of theoretical and practical knowledge from scratch.

04

Philosophy

The anthropology of science and thereof. For science, and mathematics to exist, to be thought of, and there be philosophies. Such to be researched, no matter what and who said otherwise.

05

Foundational and Theoretical Computer Science

The computer, that notion itself, and what is to be said to constitute such. Focused on the general framework of the automated machine, and theoretical computer science beyond the strictly Turing.

Five groups

One small lab, several threads of inquiry.

  • Core

    Core Management Division

    Direction, stewardship, operations, and logistics configuration of the lab - tasked with connecting people (networking), research directives, logistical maintenance and expansion, provision thereof, and long-horizon goals across the domain and group laboratories.

    5 members
  • MTAIL

    Modelling, Theoretical AI & Learning-theoretic Laboratory

    The theoretical-empirical gap: theoretical machine learning, statistical models, the double-descent phenomenon, and the coupling of modelling theory. Inherited from the Double Descent — Statistical Learning Theory Research Group.

    2 members
  • TAAI

    Theoretical & Application AI Domain

    Artificial intelligence research and its deployments — implementation and inquiry across applied and foundational AI, and adjacent concepts.

    5 members
  • MAPR

    Mathematical & Physical Research Domain

    Foundational mathematics and physics — paradox identification, mathematical utilisation on physical systems, and the theoretical-practical gap in principled physical theories. Hosts the Nuclear Physics subgroup.

    3 members
  • EPEFORM

    Encyclopaedia, Pedagogy & Foundational Research Domain

    Foundational build-up and pedagogy — assembling knowledge stacks across computer science, physics, biology, philosophy, mathematics, and chemistry, or more.

    1 members
See everyone →

Working on something foundational?

We collaborate, host visitors, and are usually hiring. If the questions here are your questions too, we would like to hear from you.

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