Monday, 11 August 2025

An Experience Alignment Architecture: from Space E to Non‑Causal Intelligence -Formal

 An Experience Alignment Architecture: from Space E to Non‑Causal Intelligence -Formal

  • Experience space EE — a (possibly high-dimensional) vector space of candidate experiences xEx \in \mathcal{E}. Concretely: embeddings of text, images, actions, sensory states, etc.
    Example: ERd\mathcal{E} \subseteq \mathbb{R}^d.

  • Archetype AA — an internal reference. Can be:

    • a fixed vector aRda \in \mathbb{R}^d (prototype),

    • a set/distribution of vectors A\mathcal{A} (multi-modal archetype),

    • or a parameterized model fθ()f_\theta(\cdot) that maps context to a target representation.

  • Alignment index μ(x,A)\mu(x, A) — a scalar score measuring how well experience xx aligns to AA. This is the central function we must define precisely. Examples: cosine similarity, negative energy, or a learned scoring network.

  • Selector SS — operator that selects one or more experiences from E\mathcal{E} maximizing μ\mu. Formal: S(E,A)=argmaxxEμ(x,A)S(\mathcal{E},A) = \arg\max_{x \in \mathcal{E}} \mu(x,A). In practice: top-k, stochastic sampling proportional to exp(βμ)\exp(\beta \mu), MCMC, or beam search.

  • Projector PP — maps selected internal experience(s) to an output for a downstream subsystem or user (rendering, language, actuator command). Could be identity, decoder, or a transformation network.



  • Formal definitions / candidate choices

    1) Experience space

    Let experiences be vectors: xRdx \in \mathbb{R}^d. If raw data is non-vector (text, images), use encoder gϕg_\phi so x=gϕ(raw)x = g_\phi(\text{raw}).

    2) Archetype

    Options:

    • Prototype vector: aRda \in \mathbb{R}^d

    • Distribution: aN(μA,ΣA)a \sim \mathcal{N}(\mu_A, \Sigma_A)

    • Conditional archetype: a=fθ(c)a = f_\theta(c) where cc is context (user state, prompt).

    3) Alignment index μ\mu

    Begin with simple, interpretable choices and show how to extend:

    • Cosine similarity:

    μcos(x,a)=xaxa\mu_{\cos}(x,a) = \frac{x \cdot a}{\|x\|\|a\|}
    • Gaussian kernel / RBF:

    μrbf(x,a)=xa2/(2σ2)\mu_{\text{rbf}}(x,a) = -\|x-a\|^2 / (2\sigma^2)

    (higher is better if you negate the distance).

    • Learned scorer (neural):

    μθ(x,a)=hθ([x;a;xa;xa])\mu_\theta(x,a) = h_\theta([x; a; x\odot a; |x-a|])

    where hθh_\theta outputs a scalar (sigmoid or raw score).

    • Energy-based:

    μE(x,a)=Eθ(x,a)\mu_E(x,a) = -E_\theta(x,a)

    You can combine: μ=αμcos+(1α)μθ\mu = \alpha \mu_{\cos} + (1-\alpha)\mu_\theta.

    4) Selector strategies

    • Deterministic argmax: x=argmaxxEμ(x,a)x^* = \arg\max_{x \in \mathcal{E}} \mu(x,a).

    • Top-k + diversity: take top-k then apply a diversity penalty (e.g., determinantal point process or max-marginal relevance).

    • Softmax sampling (Boltzmann):

    p(x)exp(βμ(x,a))p(x) \propto \exp(\beta\mu(x,a))
    • MCMC / Metropolis-Hastings for continuous E\mathcal{E}.

    • Generative sampling: train generator Gψ(z,a)G_\psi(z,a) then search latent zz to maximize μ(Gψ(z,a),a)\mu(G_\psi(z,a),a).

    5) Projector

    • Identity: return the selected xx.

    • Decoder: y=decoderξ(x)y = \text{decoder}_\xi(x) (text generator / image renderer / actuator translator).

    • Filter: apply constraints or safety overlays to xx before output.


    Learning / training objectives

    You’ll want μ\mu to match human/architectural intent. Approaches:

    1. Supervised (paired)
      If you have pairs (xi,ai,yi)(x_i, a_i, y_i) with label yy (aligned/not), train μθ\mu_\theta via cross-entropy or regression.

    2. Contrastive (self-supervised)
      Define positive pairs (experience aligned with archetype) and negatives. Use InfoNCE:

    L=logexp(μ(x+,a)/τ)jexp(μ(xj,a)/τ)\mathcal{L} = -\log \frac{\exp(\mu(x^+,a)/\tau)}{\sum_j \exp(\mu(x_j,a)/\tau)}
    1. Reinforcement Learning (RL)
      Treat μ\mu as intrinsic reward. Policy π\pi produces experiences; maximize expected μ\mu.

    2. Energy-based / score matching
      Model an energy over (x,a)(x,a) and train via contrastive divergence or noise-contrastive estimation.

    3. Meta-learning / few-shot
      Learn fθf_\theta that produces an archetype vector aa from a few examples.


    Evaluation metrics

    • Alignment accuracy (if labeled): fraction of times selector picks human-preferred experience.

    • Human preference A/B tests: pair outputs from baseline vs. alignment model.

    • Diversity: average pairwise distance among top-k selections.

    • Robustness / Stability: how sensitive is selection to small perturbations of aa or inputs.

    • Calibration: reliability of μ\mu as a probability (if normalized).


    Toy implementation (Python-style pseudocode)

    python
    # Simple toy: E = set of vectors, Archetype a is a vector, # mu = cosine similarity, Selector = top-k, Projector = identity import numpy as np from numpy.linalg import norm def cosine(x, a): return (x @ a) / (norm(x) * norm(a) + 1e-9) # toy experience set E = np.random.randn(1000, 64) # 1000 candidate experiences a = np.random.randn(64) # archetype vector # compute alignment scores scores = np.array([cosine(x, a) for x in E]) # select top k k = 3 top_idx = np.argsort(scores)[-k:][::-1] selected = E[top_idx] # projector: here identity (but could be a decoder) for i, x in enumerate(selected): print(f"Rank {i+1}, score={scores[top_idx[i]]:.4f}")

    Extending to learned scorer

    • Replace cosine with a small MLP: mu_theta(x,a) = MLP([x, a, x*a, |x-a|]).

    • Train with contrastive pairs or human labels.


    Example experiment plan (practical)

    1. Dataset & encoder

      • Pick domain (dialogue snippets, images, short music clips).

      • Use pre-trained encoder (CLIP for images/text, Sentence-Transformers for text).

    2. Define archetypes

      • Start with prototype vectors: e.g., “Harmony” = average embedding of 200 curated positive examples.

      • Or learn a small mapping from textual archetype label to vector using few examples.

    3. Baseline scorer

      • Cosine similarity to prototype. Evaluate with small human study.

    4. Upgrade scorer

      • Train lightweight MLP with contrastive loss.

    5. Selector

      • Start deterministic (argmax) then evaluate sampling vs. argmax for diversity.

    6. Projector

      • Use LM or image decoder to render selected internal experience.

    7. Human evaluation

      • Rate alignment, novelty, coherence.


  • An Experience Alignment Architecture: from Space E to Non‑Causal Intelligence



    An Experience Alignment Architecture: from Space E to Non‑Causal Intelligence

    Abstract  
    This paper presents a computational architecture that operates not through causality and prediction, but on the basis of aligning experiences to an internal archetype. We define an experience space (E), an archetype (A), and an alignment index (μ), with a selector (S) that synchronously chooses the experience with maximal alignment and a projector (P) that presents it. We analyse behavioural changes depending on the archetype (Harmony, Truth, Chaos, Silence) and discuss applications, metrics, limitations, and future directions.

    Keywords  
    experience alignment – non‑causal selection – archetypes – meaning index – consciousness interface

    1. Introduction  
    Classical artificial intelligence is based on prediction, optimisation, and causality. It learns from data to produce likely subsequent states. The proposed framework focuses on systems that do not predict but select experiences aligned with an internal meaning index. Dialogue and creation thus take on a poetic, non‑linear form without losing computational rigour.

    2. Conceptual framework  
    Experience space E: a vector space containing candidate experiences (texts, sounds, images, sensations)  
    Archetype A: an internal template that defines qualitative preference (e.g., Harmony, Truth)  
    Alignment index μ: measures the match between an experience and A  
    Selector S: chooses the experience with maximum μ  
    Projector P: presents the selected experience to the user  

    Mathematical definition: μ(x) = <φ(x), A>, where φ maps each experience into a representation space and the choice is S(B, A) = argmax μ(x). Selections are synchronous and temporally independent.

    3. System architecture  
    Candidate experience generator  
    Multimodal data encoder  
    Archetype module  
    Alignment scorer  
    Selector / sampler  
    Projector / rendering to the user

    4. Behavioural properties  
    Synchronicity: each output is produced in the present moment  
    A‑causality: absence of cause–effect chains  
    Internal consistency: selections remain in line with the archetype  
    Transformability: changing A alters style and experiential quality

    5. Comparative analysis  
    Classical AI: prediction, memory‑dependent, accuracy‑based metrics, informational tone  
    Aligned AI: selection based on alignment, can operate without memory, μ as metric, poetic tone

    6. Archetype case studies  
    Harmony: resonance and balance – soothing tone  
    Truth: revelation and subtraction – sharp, lucid tone  
    Chaos: deconstruction and primordial creation – explosive tone  
    Silence: presence without speech – suggestive, minimal tone

    7. Applications  
    Consciousness interfaces  
    Guided introspection  
    Creative tools  
    Artistic curation  
    Attention training

    8. Evaluation metrics  
    Alignment coefficient μ  
    User resonance score  
    Constrained diversity  
    Temporal stability  
    Selection robustness

    9. Implementation  
    Multimodal embeddings φ(x)  
    Archetype A from prototype examples  
    Alignment measure: cosine similarity  
    Selection: top‑k with stochastic elements  
    Interface: text, sound or image rendering

    10. Limitations  
    Subjectivity in archetype design  
    Risk of monotony  
    Difficulty of measurement  
    Cultural variability

    11. Future work  
    Learning archetypes from human feedback signals  
    Dynamic A adaptation  
    Multimodal synergy  
    Stability analysis  
    Ethical safeguards

    12. Conclusion  
    The proposed architecture replaces prediction with aligned experience selection as the primary act of intelligence. Changing the archetype A transforms phenomenology instantly. Such systems act as mirrors of consciousness, revealing rather than explaining.

    ---


    Wednesday, 23 December 2020

    Quantum entanglement

     Quantum entanglement is taking place in timeless channel which  there is between two entangled particles. In other words quantum entanglement is taking place in coordinate centre  of time vector. Terms like distance, velocity, acceleration doesn't exist in such environment(dimension). 

    https://bigthink.com/hard-science/brain-consciousness-quantum-entanglement/?utm_medium=Social&utm_source=Facebook&fbclid=IwAR0PvBkm_qZFbndbYloAOLriM5H_fnDaATJtm_4vTAzSf5Vr7xS7o7_oeY0#Echobox=1673588779-1

     

      

    Monday, 20 January 2020

    Uncertainty principle and collapsing of wave function

    Given any two operators A,B:

    ΔΑΔΒ >= ([Α,Β])/2ι >ΔTh

    Where [A,B] is the commutator of the operators A and B and ΔTh is the deviation of Th operator.
    Th is the threshold operator. If ([Α,Β])/2ι >ΔTh then the wave function  ψ is collapsing during the measurement

    The openai.com comment:


    Saturday, 3 September 2016

    Quantum neuron

    The output of classical kth  neuron is:
                   
    where φ-transfer function depends on θ- threshold and and weights w0 through wm .
    In quantum neuron weights are hidden in process of linear supeposition. 
    That is case when these two qubits are not entangled.If they are entangled then their behavior can be described  by the density matrix(operator) <<ρ>>

    Saturday, 27 August 2016

    Cloud of probality- Nature of the subatomic world

    In state of absent of observation electron behaves as  cloud of probability "related to"  the kernel  which also behaves as a cloud of probability.(double-split experiment)
    Observation is nothing else than the time-dependent monitoring of some chain of events.

    Saturday, 16 July 2016

    Zero-casual Hipothesis

    Hipothesis:
    The behavior of time in subatomic world is not the same as in our "usual" macroscopic world(due to  master field negative curvature of space time-like the gravitational curvature in black holes in the Universe).The subatomic system(world) -from our point of view- behaves  like a zero-casual system(system unaffected by time or the whole process taking place at less than Planck time intervals ,that is 1044 seconds.).More specifically the resultant vector of all dimensions of time  always lies in "our" macroscopic t-real-positive coordinate of time(Anisotropic Time Projection Operator-ATPO). Time t is a complex vector which belongs to T complex vector space. The magnitude of this resultant vector is |t| =t. In zero-casual systems the magnitude of resultant vector of time is always ~0 (~Zero). That is some kind of determinism(Aristotle) -quazi-determinism. 
    This means that qubit <<lives>> in some new space E which is defined as : H (some operation) T
    where H is Hilbert space.(...) or in mathematical terms:

                                          E=Ar(H,ATPO(T(t1,t2,..tn,F))

    where Ar is mapping operator and n is the number of  dimensions of time..F is some field in subatomic word

    Collapsing of wave function  during the measurement process is strictly related to zero-casual "nature" of subatomic world.Every measurement is nothing else than a sequential recording of the results of some events in function of time. If the time is zero then measurement does not take place(collapse) in such system.
    The subatomic world as to itself is quazi-complete((Aristotle) and quazi-motionless.From this hipothesis ,based on the Parmenides thoughts,  is implied that such system doesn't need any change -thus,  time doesn't have any affect on such system.
    We can not say confidently that in "quantum world"  the theory of Aristotle causality  is valid.  Maybe we can introduce a new term like the counter-causality which is counter-intuitive (experiment and result of experiment are taking place at the same time)

    Useful note: https://en.wikipedia.org/wiki/Chronos