Introduction (The Socratic Dialectic & Theoretical Framing)
* **Questioning Premise A (Quantum Mechanics in Clinical Cognition):** Can clinical decision-making be modeled as a quantum system?
* *Steelman:* The trajectory of a patient's clinical outcome (e.g., systemic envenomation from a snakebite) exists as a superposition of potential realities across multiple observer nodes (attending physicians, textbook-adhering doctors, and the patient). Collapse of the wave function occurs not merely at the physical event horizon, but upon the mutual exchange of information among observers, collapsing potential multiverses into a single historical reality.
* **Questioning Premise B (Event-Driven Determinism vs. Quantum Non-Locality):** Is physical and cognitive reality fundamentally probabilistic, or is it an emergent property of finite, deterministic rewrite dynamics?
* *Steelman:* Frameworks like the PEDLER (Point Event-Driven Learner) research programme posit that quantum amplitudes and spacetime are emergent from finite directed event hypergraphs. Non-locality is an artifact of mapping local graph rewrite dynamics into higher-dimensional continuum models.
Above image is accessible online from:
Choudhary, A. (2026). PEDLER Unified Physics Research Programme (Version 0.3.0). Zenodo. https://doi.org/10.5281/
* **Questioning Premise C (Orwellian Infrastructure & Constitutive Dependence):** How does modern AI and biometric governance alter the observer-observed relationship?
* *Steelman:* Automated governance infrastructures do not merely act as external observation tools; they exhibit *constitutive dependence*. They construct the legal and medical subject prior to explicit consent, serving as automated, opaque observer nodes that constrain available future trajectories.
#### 2. Methods (Epistemological Synthesis)
* **Socratic Deconstruction:** Iteratively examining the conversational transcript through three analytical lenses:
1. *Vedantic Metaphysics & Quantum Analogies:* Evaluating the observer-observed distinction (Advaita Vedanta’s *Drk-Drshya-Viveka*) alongside quantum wave function collapse and multiverse interpretations.
2. *Hypergraph Kinematics:* Mapping discrete event sequences (PEDLER architecture) to evaluate agency and identity as typed event chains.
3. *Kuhnian Paradigm Shift Analysis:* Examining the transition from human-centric scientific discovery to hybrid Human–AI collaborative networks operating under algorithmic constraints.
#### 3. Results (Analytical Convergence)
* **The Clinical Case Paradox:** In the Russell's viper envenomation case, divergence among clinical observers created competing projected realities (spontaneous recovery vs. dialysis vs. anaphylactic risk). The ultimate resolution (complete recovery) collapsed the cognitive superposition across nodes upon information sharing.
* **The Algorithmic Governance Shift:** Liberal ethics models based on consent fail under Orwellian AI infrastructures. Governance technologies systematically define eligibility and recognition *ex ante*, turning the human-AI hybrid network into the default unit of cognition and agency.
#### 4. Discussion (Synthesis & Critical Counter-Refutation)
* **How Quantum Clinical Case-Based Reasoning CAN Predict Future Non-Local Events:**
If past local events are mapped as node-connections within an event hypergraph, clinical reasoning operates as a topological search. The entanglement between a past bite event and a future clinical outcome is a structural constraint within the network. Augmented by network-wide AI interfaces, a clinician can evaluate non-local causal dependencies across distributed patient histories before the local wave function collapses.
* **How Quantum Clinical Case-Based Reasoning CANNOT Predict Future Non-Local Events:**
Applying quantum terminology to macroscopic clinical observations risks falling into metaphorical "gobbledygook." If reality at the foundational level is driven by deterministic local hypergraph rewrite rules, "quantum non-locality" and "multiverses" may simply be mathematical overfittings of incomplete observational data. Furthermore, Orwellian AI mediation introduces noise, opaque hallucination anomalies, and institutional misrecognition, breaking the cognitive fidelity required for deterministic prediction.
### Summary
The provided transcript is a multi-author dialogue exploring the intersection of clinical case reasoning, quantum physics, ancient philosophy (Advaita Vedanta), discrete hypergraph physics, and AI ethics.
1. **Clinical Case & Quantum Superposition:**
A case involving a Russell's viper bite is analyzed. Different participants in the case (the patient, primary physician, secondary physician) held divergent cognitive realities regarding the likelihood of systemic envenomation, renal failure, or recovery. These realities collapsed into a single factual trajectory once information was exchanged, drawing parallels to wave function collapse, observer dynamics, and multiverse hypotheses.
2. **Vedantic Integration:** Participants draw parallels between modern quantum observer debates and Advaita Vedanta (specifically the *Drk-Drshya-Viveka* framework of the seer and the seen), arguing that consciousness precedes and observes material realities (*Maya*).
3. **Hypergraph Physics (PEDLER Programme):** Reference is made to Abhishek Choudhary's PEDLER framework, which attempts a deterministic reconstruction of physics using finite typed directed event hypergraphs, proposing that quantum amplitudes and spacetime emerge from discrete rewrite dynamics rather than probabilistic wave mechanics.
4. **AI Ethics & Constitutive Dependence:** The conversation references Abhinav Saxena’s work on algorithmic welfare systems. It critiques consent-based AI ethics, arguing that large-scale infrastructure acts as an active observer that creates social and legal eligibility rather than merely seeking permission.
5. **Kuhnian Shift in Cognitive Architecture:** A detailed reinterpretation of Thomas Kuhn’s *Structure of Scientific Revolutions* suggests that AI alters the fundamental unit of intelligence from the isolated human mind to a hybrid Human–AI network, shifting scientific focus from memorization to question framing and judgment.
### Keywords
* Quantum Clinical Reasoning
* Wave Function Collapse
* Observer Node
* Advaita Vedanta (*Drk-Drshya-Viveka*)
* PEDLER Research Programme
* Event Hypergraphs
* Constitutive Dependence
* Algorithmic Welfare
* Kuhnian Paradigm Shift
* Hybrid Cognition
### Thematic Analysis
#### Theme 1: The Observer Problem in Clinical Decision-Making and Metaphysics
The dialogue treats the clinical domain as a complex observational field. Different actors (doctors, patient, institutional guidelines) act as distinct "observer nodes." Each node maintains a cognitive state corresponding to a possible future outcome (spontaneous cure, life-threatening renal failure, anaphylaxis due to antivenom). The transition from multiple potential clinical outcomes to a single historical record is conceptualized as a cognitive collapse of the wave function. This parallels the Vedantic concept where material outcome (*Maya*) is defined and observed through primary awareness/consciousness.
#### Theme 2: Deterministic Discrete Event Systems vs. Quantum Probability
A crucial tension in the text arises between the quantum multiverse/wave-collapse metaphor and the discrete event-hypergraph approach (PEDLER framework). While one perspective models human event trajectories as probabilistic quantum superpositions, a counter-perspective challenges this as "gobbledygook" or mathematical overfitting. Under the hypergraph paradigm, identity, agency, and causality are reduced to sequences of typed local events, suggesting that non-local effects are artifacts of higher-level abstraction rather than true physical randomness.
#### Theme 3: Algorithmic Mediation and Constitutive Infrastructure
The conversation contextualizes AI not as a mere passive calculation tool, but as an active, structural participant in society and clinical medicine. Through the lens of Abhinav Saxena's paper on algorithmic welfare, AI and large-scale data systems exert *constitutive dependence*—they define who is recognized as a patient, citizen, or beneficiary. When coupled with brain-computer interfaces (BCIs) or continuous monitoring devices, these systems become automated observers that continuously record, constrain, and shape human decision-making processes.
#### Theme 4: The Transformation of Epistemic Agency (The AI-Driven Kuhnian Paradigm Shift)
The final thematic thread applies Kuhn's paradigm shift model to argue that the locus of cognition has migrated from the individual human scientist or physician to a networked Human–AI hybrid system. In this environment:
* **The Unit of Cognition Changes:** Intelligence becomes a distributed network property (*Human + AI + Data + Infrastructure*).
* **Skills Shift from Execution to Judgment:** Memorization and manual synthesis yield to problem framing, Socratic questioning, and verification of opaque AI outputs.
* **Continuous Crisis:** Anomalies such as AI hallucinations, opaque logic, and rapid model updates make paradigm shifts continuous rather than periodic, reshaping how scientific consensus and clinical knowledge are established.
Provide a Socratic steelman imrad, summary, keywords, thematic analysis of the content below focusing on how and how not can quantum clinical case based reasoning predict future non local events entangled in past local events currently augmented by Orwellian AI
Conversational transcripts:
[19/07, 17:05]hu3: Prof
Could you kindly repost the UDLCO article on quantum field in clinical practice, on the Snake Bite case?please.
The post has diappeared due to Timer settings
Tnx
[19/07, 18:59]hu2: Sure
Here it is linked inside the latest in this series as a part of our ongoing project workflow 👇
[19/07, 20:14]hu3: There was one on wave function collapse a week ago
[19/07, 21:16]hu2: Yes I have placed all those in the series as links inside this article and i quote again from it 👇
"This project note is a follow up to the project reports and discussions archived around a specific Russell's viper human bite case that was published recently here:
https://medicinedepartment.
https://medicinedepartment.
[20/07, 11:56]hu3: I hv been thinking about this post awhile and per chance came across a video relevant to this topic. titled" Physicists
have been accidentally proving "Maya"."
The question about the nature of Observer is still debated in Physics .However,
Vedanta philosophy apparently answered this question around 3000 years ago, as per the video.
With this in mind, the UDLCO article needs to clarify the point on who the observer is and if it is separate from the apparatus.
Can the Two teams with conscious desire to alter the trajectory be the Slits ?
All one can say is out of an innumerable possible trajectories of a patient outcome , one trajectory becomes real.
The two teams are Observers with different anticipated trajectories
The patient's (a third observer) own trajectory of going home is superceded by the final observer(Destiny)'s trajectory, of a "complete cure" instead of some complicated outcome?
[20/07, 12:36]hu2: Yes of course.
Instead of teams we could even just classify them as multiple observer nodes.
Doctor observer node 1's observational reality:
Hopeful of good spontaneous outcomes due to systemic envenomation symptoms not developing fast and yet would like to keep under observation follow up for potential dialysis etc if there is a recurrence of systemic envenomation going onto life threatening renal failure
Patient observer node 1's reality: Quite confident that there won't be much systemic envenomation especially as the Russel viper snake appeared to be a baby and her local skin reaction at the site of bite was also short lived and she hadn't read up about the potential renal failure issues around Russel viper envenomation
Doctor observer node 2's reality: This was heavily influenced by text book recommendations to restart Anti venom in spite of the anaphylaxis which further scared the patient and made her LAMA (leave against medical advice).
The above realities lived in the cognition of different observers before it collapsed with time as information was exchanged and all these nodes came to know of the actual patient's outcome of full recovery from the systemic envenomation.
As soon as information was exchanged , the collapsed individual realities made way for one collective cognitive realistic outcome that we may call one reality in the universe where we currently exist.
However there is the possibility of a multiverse where there could be totally other realities getting played out such as the same patient dying of further systemic envenomation after going home in one universe or another universe where she has to undergo dialysis and then recovers etc and we currently know of these potential multiverses because we have seen different patients experience all these potential outcomes at different points in time in this very universe.
Hence every patient/individual event outcome is entangled to multiple possibilities till the event happens to one possibility and collapses all the other possibilities and as soon as that one possibility happens (which is also an event) we can say that the prior event was causally entangled to the now only outcome visible in our reality?
[20/07, 13:00]hu4: With the advances in quantum physics, the teachings of Vedanta has received more attention! Observer is the key and what is observed is maya being undefined and can be reduced back to observer.
We give importance to matter in science, but Vedanta says consciousness or awareness is primary and matter is dependent.
Among the various small texts, there is one on ‘seer seen relationship’ drk drshya viveka attributed to Sankara which is very informative.
[26/07, 09:19]hu1:
Choudhary, A. (2026). PEDLER Unified Physics Research Programme (Version 0.3.0). Zenodo. https://doi.org/10.5281/
PEDLER Unified Physics Research Programme
Authors/Creators
Choudhary, Abhishek
Description
This research poster presents the current comprehensive formulation of the PEDLER (Point Event-Driven Learner) Unified Physics Research Programme, extending the original conceptual framework into a large-scale axiomatic research architecture for the deterministic reconstruction of modern physics. Beginning with a primitive ontology of finite typed directed event hypergraphs and deterministic local rewrite dynamics, the programme systematically develops the mathematical foundations required for information conservation, category-theoretic structure, hypergraph geometry, simplicial constructions, discrete differential geometry, continuum reconstruction, emergent spacetime, Lorentzian geometry, gravity, quantum mechanics, gauge theory, particle physics, thermodynamics, black holes, and cosmology. The framework explicitly organizes formal definitions, primitive ontology, rewrite algebra, conserved invariants, working axioms, lemmas, conjectures, dependency graphs, target theorems, computational algorithms, simulation requirements, and experimentally falsifiable predictions into a coherent dependency hierarchy. It identifies the mathematical pathways required to derive quantum amplitudes, Hilbert spaces, the Born rule, Einstein field equations, gauge symmetries, Standard Model structures, cosmological evolution, and other established physical theories from deterministic event-hypergraph dynamics while clearly distinguishing established mathematical constructions from open research problems. The programme also outlines the computational infrastructure necessary for theorem proving, numerical simulation, automated verification, and empirical validation. Rather than presenting a completed unified theory, this poster defines the architectural blueprint and long-term research roadmap required to establish a rigorous deterministic foundation from which the known laws of quantum mechanics, gravitation, gauge interactions, thermodynamics, black holes, and cosmology may ultimately emerge.
[26/07, 10:51]hu1:
He's done some real nice work on AI and ethics... Do find time to read
[26/07, 10:52]hu1: https://lnkd.in/p/gptyxWsp
[26/07, 10:52]hu1: His paper
[26/07, 10:56]hu2: 👏It introduces the idea of constitutive dependence, his argument for why consent-based AI governance fails in systems like India's Aadhaar, which don't just govern people but decide who counts as a person to govern. He trained in analytic philosophy (MA, University of Delhi; UGC NET) and spent three years in academic publishing, running manuscript evaluation and peer review, so he built arguments and judged them for a living. He works from New Delhi, and his cases come from Indian institutional reality, not thought experiments. That's a method, not a limitation. He's currently working on: machine testimony, moral responsibility under AI mediation.
[26/07, 10:57]hu1: Gen Z continue to amaze us @hu5 🤗
[26/07, 11:25]hu2: The actual full text link and summary of the paper:
Beyond consent: algorithmic welfare, constitutive dependence, and the limits of liberal AI ethics*
*Author:* Abhinav Saxena
*Journal:* _AI and Ethics_ (2026)
Most AI/data laws today like *GDPR, EU AI Act, and India's Aadhaar* rely on one core idea: *"consent"*.
If you click "I agree", the system is considered legitimate. If something goes wrong, the debate is also about "did you consent?"
This paper says: *that framework breaks down for large-scale algorithmic welfare systems.*
*Core idea: "Constitutive Dependence"*
For systems like Aadhaar or welfare DBTs, the algorithm doesn't just ask for your permission. It actually _creates_ your status as someone who is entitled to benefits.
- The system decides who is "eligible" automatically, without a human officer using discretion.
- So the question of "refusal/consent" is already foreclosed by the system’s design.
The author calls this *misrecognition* — the system grants you rights not because you authorized it, but because the infrastructure recognizes you.
The alternative: "Constitutive Obligation Principle"
Instead of asking "did the person consent?", institutions should be judged on:
1. *Legibility* - Can people understand how decisions are made
2. *Non-abandonment* - The system can't just drop people who don't fit the algorithm
3. *Remediation* - There must be ways to fix wrong decisions
*Why Aadhaar is the main case study*
The paper uses Aadhaar as the prime example. It's not an "outlier". The author argues it's an early version of where all algorithmic welfare/governance is heading: systems that grant rights and entitlements directly through code, not through human judgment + consent forms.
Bottom line:
For welfare systems, "consent" is conceptually the wrong tool. We need new ethics based on obligations the state/algorithm has to the people it governs, because they depend on it to even _be_ recognized.
[26/07, 11:27]hu1: He raises many pertinent questions
Still... At the same time... The assumption of AI as the "current AI" could be a fallacy
[26/07, 11:35]hu1: Think of it... What if we reframe the question from "who" gives consent.. to "what" gives consent.
Essentially, again, this appears to be an extreme reductionist premise.
But to answer that, or refute that, one will need to define both "agency" and "identity"
...
To me... Both are merely a consequence of a sequence of "typed" (as in categorised) events
[26/07, 11:38]hu2: That's the Orwellian Kuhnian shift!👇
Bottom line:
Paradigm shift
Human–AI collaborative cognition becomes the default
New normal science
Research proceeds through hybrid human–AI teams, with humans focusing more on judgment, goals, and interpretation
The deepest consequence may not be that AI knows more than any individual. It is that the locus of intelligence is moving from isolated human minds to networks of humans and AI systems
The long top line:
A precursor to finally ditching talking and using BCI thought keys to convey what you need!
On a related note 👇
The only thing the article below probably doesn't address is the phenomenal data collection devices that would be necessary to feed humans and AI to generate their Socratic questions leading to hypothesis? Those are likely to be Orwellian devices connected to every human individual through a simple brain computer interface BCI involving an EEG cap, thought keys, meta google glass interface and a collective cognitive internet!
Prompt: Can you apply Kuhn's "Structure of Scientific Revolutions" to the AI era and come up with an analysis?
Thomas Kuhn's The Structure of Scientific Revolutions (1962) provides one of the most useful frameworks for understanding the AI era—not because AI is merely another technology, but because it may represent a change in how knowledge itself is produced.
At the same time, the AI era also exposes some limits of Kuhn's theory, because AI is affecting not only science but engineering, business, education, and even cognition.
Here's how I would reinterpret Kuhn for the age of AI.
Kuhn's Original Thesis
Kuhn argued that science does not progress smoothly through accumulation.
Instead it progresses through repeated cycles:
Normal Science → Anomalies → Crisis → Paradigm Shift → New Normal Science
Each paradigm defines
what questions are worth asking
acceptable methods
legitimate evidence
standards of proof
who counts as an expert.
Scientists mostly solve puzzles inside the paradigm rather than questioning it.
AI as a New Paradigm
The AI revolution is unusual because it changes not merely the answers but the process of discovery itself.
Previous scientific paradigms changed:
Newtonian mechanics
relativity
quantum mechanics
plate tectonics
AI changes something more fundamental:
who (or what) participates in scientific reasoning.
That makes AI closer to the invention of writing or the printing press than merely another scientific theory.
Phase 1: Normal Science Before AI
For roughly seventy years, research looked remarkably similar across disciplines.
A scientist
read literature
formed hypotheses
designed experiments
analyzed data
wrote papers
The computer accelerated calculations but did not contribute intellectually.
Humans remained the bottleneck.
Knowledge production was fundamentally human.
Phase 2: Anomalies Appear
Kuhn emphasized anomalies that cannot be explained.
In AI, the anomalies were different.
Researchers noticed systems beginning to perform tasks believed to require reasoning.
Examples:
Deep Blue
↓
AlphaGo
↓
GPT
↓
AlphaFold
↓
o-series reasoning models
↓
scientific copilots
These systems solved problems using methods humans never explicitly programmed.
This violated the prevailing paradigm that intelligence required explicit symbolic reasoning.
Phase 3: Crisis
Today we are arguably living inside Kuhn's crisis stage.
Many assumptions are under pressure.
Assumption 1
Expertise comes from memorization.
AI has nearly infinite recall.
Assumption 2
Writing demonstrates understanding.
LLMs write extremely well.
Assumption 3
Programming requires humans.
AI writes software.
Assumption 4
Research requires years of literature review.
AI compresses months into hours.
Assumption 5
Creativity is uniquely human.
Generative models produce novel designs, proteins, circuits, code, and hypotheses.
None of these invalidate human intelligence, but they force a reevaluation of what counts as uniquely human intellectual work.
The New Paradigm
The emerging paradigm is no longer
Human → Tool
Instead it becomes
Human ↔ AI collaborator
Knowledge production becomes interactive.
Instead of
Question
↓
Experiment
↓
Paper
↓
Repeat
we increasingly have
Question
↓
AI generates hypotheses
↓
Human filters
↓
AI designs experiments
↓
Automation performs experiments
↓
AI analyzes results
↓
Human interprets
↓
Repeat continuously
This is fundamentally different from twentieth-century science.
AI Changes the Unit of Intelligence
Kuhn studied communities.
AI changes the unit of cognition.
The productive unit is no longer an individual scientist.
It becomes
Human + AI + data + tools.
The scientist becomes an orchestrator.
Normal Science Becomes "Prompt Science"
Kuhn argued that normal scientists become experts within a paradigm.
In AI, the new skill increasingly becomes asking better questions.
This is remarkably Kuhnian.
Within a paradigm, success comes from understanding its rules.
Within AI,
the "rules" involve
framing problems
decomposing tasks
evaluating outputs
iterative refinement
knowing when the model is wrong.
The premium shifts from remembering answers to formulating questions.
Paradigm Shifts Become Faster
Kuhn assumed paradigms lasted decades.
AI accelerates paradigm turnover.
Instead of
50 years
we may see
5 years
or even
18 months.
Scientific revolutions become continuous.
The crisis phase never fully settles.
The Scientific Literature Changes
Traditionally
Paper
↓
Citation
↓
Replication
↓
Consensus
↓
Textbook
AI changes every stage.
Literature review becomes automated.
Hypothesis generation becomes automated.
Meta-analysis becomes automated.
Replication becomes partially automated.
The bottleneck shifts from finding knowledge to judging knowledge.
The Nature of Expertise Changes
Before AI
Expertise ≈ accumulated knowledge.
After AI
Expertise increasingly becomes
judgment
taste
skepticism
framing
causal reasoning
deciding what matters.
Facts become abundant.
Judgment becomes scarce.
The New Anomalies
Every paradigm creates its own anomalies.
AI introduces entirely new ones.
Examples include:
Hallucinations
Systems produce convincing falsehoods.
Opaque reasoning
The answer works.
Nobody knows exactly why.
Evaluation
If AI discovers something beyond human understanding,
who verifies it?
Attribution
Who deserves credit?
Human?
Model?
Training data?
Institution?
Scientific reproducibility
If a model changes every few months,
can experiments remain reproducible?
These are precisely the kinds of anomalies Kuhn would predict for a new paradigm.
Competing Paradigms
Kuhn emphasized that old and new paradigms often coexist.
Today we already see competing research cultures.
Classical paradigm:
experts memorize
papers are primary
coding is manual
literature review is manual
AI-native paradigm:
AI assists every step
coding is collaborative
literature is synthesized dynamically
experiments are increasingly automated.
These communities sometimes struggle to evaluate each other's methods because they operate under different assumptions about what constitutes rigorous work.
Where Kuhn Falls Short
Kuhn viewed paradigm shifts as occurring within science.
AI extends far beyond science.
It reshapes
education
law
medicine
engineering
art
business
government
everyday decision-making.
The relevant paradigm is therefore broader than "scientific."
It concerns society's entire system for producing and using knowledge.
A New Concept:
Cognitive Paradigm Shift
Kuhn described scientific revolutions.
AI may represent something larger:
a cognitive revolution.
Previous revolutions amplified:
muscle (Industrial Revolution)
communication (Printing Press)
computation (Digital Revolution)
AI amplifies reasoning itself.
That is historically unusual.
Rather than replacing scientists, AI changes what it means to think, discover, and solve problems.
An Extension of Kuhn for the AI Era
If Kuhn were writing today, his cycle might look something like this:
Kuhn (1962) AI Era
Normal science:
Human-centered knowledge work
Anomalies AI solves "human-only" cognitive tasks
Crisis
Institutions struggle to redefine expertise, education, authorship, and trust
Paradigm shift
Human–AI collaborative cognition becomes the default
New normal science
Research proceeds through hybrid human–AI teams, with humans focusing more on judgment, goals, and interpretation
The deepest consequence may not be that AI knows more than any individual. It is that the locus of intelligence is moving from isolated human minds to networks of humans and AI systems. If that transition endures, the AI era will be remembered less as another technological advance than as a transformation in the very architecture of knowledge production—an evolution that extends Kuhn's scientific revolutions into a broader revolution in cognition.
[26/07, 11:43]hu1: Fallacies
AI cannot have infinite recall! Speed yes. Even databases did... But not infinite recall
...
Assumption 2... Matches ILM lemmas
...
The flaws of Assumption 3...
Programmability is not understood as a subsumptive Linguistic hierarchy... Shared DoIs earlier.
4 is inconsequential... All automation does that
[26/07, 11:44]hu2: From an anthrocentric simplistic perspective:
Every human events trajectory is a quantum superposition where the future "outcome event" is unpredictable but once it's known, the individual events wave function collapses and that individual human (particle) instantly knows the information it had sought in the past, in a sense that individual's past events were already entangled non locally with its future!
[26/07, 11:45]hu1: I challenge that very premise...
It's googledegok of maths trying to overfit observation
Please wait till I can DoI my *two sheets* model
[26/07, 11:47]hu2: I had to google googledegok and I really love this neologism
The old word gobbledygook was becoming boring
