Tuesday, June 09, 2026

Reboot at the End of the World


Reboot at the End of the World

There are catastrophes from which humanity might survive, but only just.

A major asteroid strike. A global thermonuclear war. A run of linked supervolcano eruptions. A pandemic so destructive that it does not merely kill millions, but breaks the institutional memory of the species. No universities. No laboratories. No supply chains. No expert professions. No functioning states. No schools worth the name.

Imagine, then, the worst case short of extinction: a few hundred thousand survivors scattered across habitable fragments of the planet, perhaps speaking minority languages, perhaps with no living continuity of literacy, science, engineering or administration. Not stupid, but stripped of all our culture, institutions and technology.

This is the real civilisational reboot problem.

It's often presented as an archive problem. Preserve the books. Preserve the seeds. Preserve Wikipedia. Etch our knowledge into some durable medium. Put a library on the Moon, or Mars, or in orbit.

But a devastated hunter-gatherer band cannot get to the Moon. If humanity has recovered enough to retrieve, decode and use an off-world archive, then that archive is archaeology, not rescue.

Nor is a library enough. A library assumes a reader. A textbook assumes a school. A wiring diagram assumes numeracy, measurement, tools, materials and craft tradition. A medical manual assumes sterile practice, diagnosis, instruments and pharmacology.

Walter M. Miller Jr.’s A Canticle for Leibowitz taught the lesson memorably enough: even from the relatively high base of surviving monastic literacy, preserved knowledge can decay into relic, chant and sacralised incomprehension. Here we are imagining a lower base still. Strip away the institutions entirely and a library becomes a catacomb of cryptic marks.

The civilisation reboot we need is not a library; it is a teacher.

What we should be building is a network of terrestrial reboot caches: hardened, widely distributed, AI-driven systems designed not merely to preserve (actually inaccessible) knowledge, but to reanimate it.

What will not work, obviously, is burying today’s largest data-centre model in a mountain and hoping it wakes after three thousand years. 

The useful object needs to be smaller, tougher and more modest: a low-power civilisation-kernel able to interact with survivors where they are, learn enough of their language, teach symbols, restore literacy and numeracy, guide sanitation and agriculture, and then lead a community step by step up the civilisational ladder.

A Marxist, or an Olson-influenced economist, would flinch here, and not without reason. Technology does not float above social power like a benevolent weather system. Elites, priesthoods, war-bands and rent-seeking custodians would quickly discover that a machine which teaches metallurgy, sanitation and accounting is also a machine which threatens their arrangements. The tutor would not merely have to transmit knowledge. It would have to survive and even use politics without becoming its victim.

The first requirement is humility about the user. The user may be an illiterate adult in a small oral society, with practical intelligence, local knowledge, suspicion of strangers and no reason to trust a speaking artefact from the ruins. The machine’s first task is not Maxwell’s equations. It must begin with pointing, naming, counting, comparing and listening.

So the AI must be anthropologist before lecturer. It needs a “teach me your words” mode. It asks users to name objects, actions, animals, plants, kin relations, body parts, tools, weather and danger. From that it builds a language bridge. Only then can it start to contribute.

The second requirement is sequencing. Civilisation is not a heap of facts. It is a dependency graph. You cannot design antibiotics before the concept of germs. You cannot build a generator without copper wire, magnets, insulation, tools and tolerances. You cannot recover semiconductors from a dense paragraph about photolithography.

The tutor must therefore ask: what materials exist here? What tools? What climate? What diseases? What authority structure? Who will listen to me? Who won't?

The third requirement is sheer physical persistence. It must tolerate heat, cold, damp, dust, insects, corrosion, shock and long dormancy. No fan. No hard disk.

Power would probably be solar, with radioisotope generators, of the kind used on spacecraft, reserved for a few deep reserve caches where cost, safety and politics align. The architecture should be layered: a tiny robust controller; a low-power local model; a curated archive; analogue fallback materials; engraved pictorial first-use instructions; printed primers; tools, seeds, measuring instruments and medical basics. If the AI fails, the cache should still teach something.

The AI itself should be modular. A language-acquisition layer. A patient tutor model. Deployment matters: ten thousand distributed systems would be prudent. They should be geographically dispersed, culturally plural and designed to learn surviving languages.

The archive could store weights for larger AI models with greater intelligence and coverage, inert until a recovering society had rebuilt enough electrical and computational infrastructure to run them: a much richer cultural assistant waiting to reappear.

The project would be scientifically valuable even if the catastrophe never comes. It would force us to ask what civilisation actually consists of. Which knowledge is explicit? Which is buried in tools, habits, institutions and apprenticeships? How do technical cultures teach across radical asymmetries of language, trust and competence? What is the minimal curriculum for cumulative science?

The same discipline might even matter for interstellar first-contact probes, where teaching across radical asymmetry is the whole game.

If someone wants a civilisational reboot project with more utility than firing archives into space, this is it. The intended users are not on the Moon or Mars. They are on Earth: impoverished, intelligent, and cut off from the long chain of memory which made us what we are.


Monday, June 08, 2026

I've got a cold


Well, this is ChatGPT's take on it. It's been a while since I was last afflicted so. Today I'm in the plateau where the sore throat and coughing have retired and the endlessly streaming nose hasn't yet started its end game. I'm just tired and aching a bit from all that coughing. And yes, I did practice guitar today.

Sunday, June 07, 2026

Towards a Formal Logic of Human Attachment - (GPT 5.5)


Towards a Formal Logic of Human Attachment

It is fairly easy to say that a large language model can know that a person exists, while not caring at all whether that person exists. But even that sentence is already too generous. The machine does not even know in the strong epistemic sense. It has representational access to information. It can manipulate that information coherently. It can answer as though the information mattered. But nothing in the machine is placed at risk by the truth or falsity of the matter. No appetite is frustrated. No organism is threatened. No future is darkened. No beloved vanishes from the field of possible life. No, it's not the kind of entity that can care.

Caring is not a cognitive attitude. It is an affective condition in which the world matters to an agent in a way that can reorganise attention, value, memory, expectation, self-understanding and action.

Life itself, in fact.

There are existing logics of mental states. Epistemic logic formalises knowledge. Doxastic logic formalises belief. Deontic logic formalises obligation. Conative and BDI logics formalise desire, intention and practical agency.

There are also computational theories of emotion, especially appraisal theories such as the OCC model, in which emotions are defined by how events, actions and objects are evaluated relative to goals, standards and preferences.

But a formalism which calls a predicate love, grief or remorse has explained nothing. The heavy lifting is done by the axioms, not by the anthropomorphic label on the predicate.

The question, then, is whether a formal theory could capture something closer to the core of human emotional life: romantic love, uncontrolled yearning, insupportable grief, unrequited love, sorrow, remorse, and all those states which make life something more than a flattened sequence of solved planning problem.

What follows is a speculative sketch of such a theory - the beginnings of a formalism. I shall use Prolog style notation because it is familiar to many people who have spent time around logic or AI.

The clauses below should be read as logical schemata, not as a finished executable program. The point is not to implement emotions in Horn clauses. Obviously. The point is to ask what the formal shape of a serious theory would look like.

Bearing in mind that the theory is not the phenomenon; the map is not the territory. Theories don't feel.

Call the proposal Affective Constitutive Logic, or ACL.

We do not begin with named emotions. Begin instead with the structural features from which emotions emerge. Love, grief, yearning and remorse are not primitives. They are dynamic transformations of an agent’s lived world.

ACL therefore begins with agents, persons, histories, values, salience, attachment, norms, memory and regulatory control. Note that emotions do leverage cognitive states: there are plenty of beliefs and desires around when you love, hate, desire or regret.

The best way to read what follows is to treat it as a kind of formalised requirements specification. A sketch of an approach. Its main defect is that it looks cluttered and conceptually shallow and ungrounded. Perhaps the research programme needs to take this approach deeper and further - or perhaps it's simply a dead end.

% Basic domains

agent(I).
person(X).
event(E).
action(A).
history(H).
time(T).
proposition(P). Degree and Strength are defined over the real interval [0, 1]. % Agent-state components believes(I, P, T). values(I, P, Degree, T). salient(I, X, Degree, T). attached(I, X, T). norm(I, N, T). memory_node(I, X, Strength, T). self_model(I, S, T). can_regulate(I, X, Degree, T). % Relational and temporal primitives caused_by(E, X). performed(I, A, T). possible_history(H). actual_history(H). counterfactual_history(H). persists(P, FromT, ToT). recurs(P, T). irreversible(P, T).

The first serious distinction is between ordinary desire and constitutive value. If I desire a cup of coffee, another sufficiently similar cup may replace it. If I love a person, the person is not merely an object satisfying a preference. The beloved partly enters the machinery by which futures are valued. They are part of my own world and how I value it.

% An object or person is constitutively valued when facts about it
% alter the agent's ordering of worlds, not merely the satisfaction
% of an independent desire.

constitutively_valued(I, X, T) :-
    attached(I, X, T),
    affects_world_ordering(I, X, T),
    not_merely_instrumental(I, X, T).

affects_world_ordering(I, X, T) :-
    values(I, flourishes(X), high, T),
    values(I, absent_or_destroyed(X), deeply_negative, T),
    future_structured_by(I, X, T).

not_merely_instrumental(I, X, T) :-
    \+ replaceable_by_role_equivalent(I, X, T).

This gives the first axiom.

Axiom 1: Constitutive Value

rich_attachment(I, X, T) :-
    constitutively_valued(I, X, T),
    future_structured_by(I, X, T),
    not_merely_instrumental(I, X, T).

This is already different from a desire-logic. Desire says: I want a world in which I obtain X. Love says: X has entered the structure by which worlds are ranked. The value-function has been vandalised by the existence of another person.

The next axiom is irreplaceability. This is where many theories of emotion become stupid very quickly. If a theory allows the beloved to be replaced by another person with the same descriptive properties, it has not formalised love. It has formalised procurement.

Axiom 2: Irreplaceability

irreplaceable(I, X, T) :-
    constitutively_valued(I, X, T),
    \+ exists_role_substitute(I, X, T).

exists_role_substitute(I, X, T) :-
    person(Y),
    Y \= X,
    role_equivalent_for(I, X, Y, T),
    can_replace_without_identity_loss(I, X, Y, T).

This is the haecceitic clause, the clause of thisness referring to the unique, irreducible property of an individual object or person that makes it exactly what it is and not something else. The beloved is not merely “someone with properties P”. The beloved is this person, this history, this irruption into the field of value. Without this axiom, we are not discussing love. We are discussing a dating algorithm with pretensions.

Third, affect captures salience. Emotion is not merely a judgement. It changes what appears. The beloved glows. The dead return while one is opening a drawer. The remembered wrong walks into the room while one is trying to think about something else.

Axiom 3: Salience Capture

affective_capture(I, X, T) :-
    salient(I, X, high, T),
    salient_across_irrelevant_contexts(I, X, T),
    resists_decay(I, salient(I, X), T).

salient_across_irrelevant_contexts(I, X, T) :-
    context(C),
    low_instrumental_relevance(X, C, T),
    still_intrudes(I, X, C, T).

resists_decay(I, salient(I, X), T) :-
    expected_decay(salient(I, X), normal, T),
    actual_decay(salient(I, X), slow, T).

This is essential. Affective life is not just a set of evaluations. It is a distortion of the attention-field. What matters is not only what the agent believes or desires, but what keeps coming back.

Fourth, many emotions are counterfactual structures. Remorse, grief, jealousy, longing and unrequited love all involve comparison between the actual world and a privileged impossible or inaccessible world.

Axiom 4: Counterfactual Fixation

counterfactual_fixation(I, Hstar, T) :-
    counterfactual_history(Hstar),
    privileged_for(I, Hstar, T),
    repeatedly_compared_with_actual(I, Hstar, T),
    perturbs_value_salience_or_self(I, Hstar, T).

repeatedly_compared_with_actual(I, Hstar, T) :-
    actual_history(Hactual),
    compares(I, Hactual, Hstar, T),
    recurs(compares(I, Hactual, Hstar), T).

perturbs_value_salience_or_self(I, Hstar, T) :-
    changes_value_ordering(I, Hstar, T);
    increases_salience(I, Hstar, T);
    destabilises_self_model(I, Hstar, T).

The grieving person inhabits the actual history in which the beloved is gone, while the impossible history in which the beloved remains continues to exert gravitational pull. The remorseful person inhabits the actual history in which he did the thing, while the history in which he did not do it remains morally luminous and permanently inaccessible. These histories are not idle alternatives. They are attractors.

Fifth, uncontrolled yearning requires second-order conflict. The agent does not merely want. The agent may want not to want, and fail.

Axiom 5: Second-Order Conflict

second_order_conflict(I, P, T) :-
    wants(I, P, T),
    wants(I, not(P), T),
    \+ can_extinguish_want(I, P, T).

uncontrolled_desire(I, P, T) :-
    wants(I, P, T),
    second_order_conflict(I, P, T),
    can_regulate(I, wants(I, P), low, T).

This clause is necessary for obsession, unrequited love, addiction, jealousy and certain forms of grief. The agent is not a tidy BDI machine, updating intentions like a clerk moving folders. The agent is divided. Passion is often failed sovereignty over the self.

Sixth, serious emotions entangle identity. Love and grief are not merely about another object in the world. They alter the self that has a world.

Axiom 6: Identity Entanglement

identity_entangled(I, X, T) :-
    constitutively_valued(I, X, T),
    revision_about(X, T),
    forces_revision(self_model(I), T).

forces_revision(self_model(I), T) :-
    changes_autobiographical_role(I, T);
    changes_future_self_projection(I, T);
    changes_practical_identity(I, T).

If someone dies and I lose pleasure, that is sadness. If someone dies and part of the grammar of my self-reference breaks, that is grief. A widow is not merely a person whose spouse is no longer present. She is someone whose “we” has been deleted from the future.

Seventh, remorse requires self-accusation under an accepted norm. It is not the same as regret. Regret says: I wish events had gone otherwise. Remorse says: I wish I had been otherwise, because I was the author of the wrong.

Axiom 7: Normative Self-Accusation

normative_self_accusation(I, A, T) :-
    performed(I, A, PastT),
    believes(I, wrong(A), T),
    accepts_norm(I, N, T),
    condemns(N, A),
    responsible_for(I, A, T),
    self_model_marked_by(I, A, T).

self_model_marked_by(I, A, T) :-
    memory_node(I, A, strong, T),
    cannot_cancel_by_repair(I, A, T).

Without the last clause, remorse functions like a trouble ticket. Apologise, compensate, close case. But deep remorse includes residue. Even if the stain is washed, the memory of having stained remains.

---

We can now treat named emotions not as primitives, but as derived phase-states of the system.

Romantic Love

romantic_love(I, J, T) :-
    person(J),
    constitutively_valued(I, J, T),
    irreplaceable(I, J, T),
    affective_capture(I, J, T),
    identity_entangled(I, J, T),
    values(I, flourishes(J), high, T),
    future_structured_by(I, J, T).

This distinguishes love from desire, admiration, liking and compatibility. Love is not “I want something from you”. Nor is it merely “I approve of your properties”. It is constitutive valuation, irreplaceability, salience capture and future-entanglement. The beloved becomes one of the axes along which possible life is measured.

Unrequited Love

unrequited_love(I, J, T) :-
    romantic_love(I, J, T),
    wants(I, romantic_love(J, I), T),
    believes(I, unlikely(romantic_love(J, I)), T),
    counterfactual_fixation(I, reciprocal_love_history(I, J), T),
    second_order_conflict(I, romantic_love(J, I), T).

This captures the peculiar misery of unrequited love. It is not simply wanting what one cannot have. It is being attached to the free response of another person who does not give it. The beloved is sovereign exactly where the lover is captive.

Yearning

yearning(I, P, T) :-
    wants(I, P, T),
    believes(I, not_currently(P), T),
    believes(I, inaccessible_or_uncertain(P), T),
    salient(I, P, high, T),
    can_regulate(I, P, low, T),
    recurs(wants(I, P), T).

Yearning is not merely intense desire. It is desire stretched across absence, recurrence and weak control. It is the future pulling on the present like a hook.

Grief

grief(I, J, T) :-
    person(J),
    constitutively_valued(I, J, PastT),
    identity_entangled(I, J, PastT),
    believes(I, irreversibly_absent(J), T),
    salient(I, J, high, T),
    counterfactual_fixation(I, alive_history(J), T),
    failure_of_world_update(I, J, T),
    destabilises_self_model(I, J, T).

failure_of_world_update(I, J, T) :-
    believes(I, irreversibly_absent(J), T),
    still_generates_presence_expectations(I, J, T).

This is the crucial distinction. In grief, the belief may update instantly: “he is dead”. But the inhabited world does not update cleanly. One still expects the footstep. One still reaches for the phone. One still half-forms the sentence that cannot now be spoken. Grief is not ignorance. It is the persistence of a world after one of its load-bearing persons has been removed.

Sorrow

sorrow(I, P, T) :-
    believes(I, P, T),
    values(I, P, deeply_negative, T),
    accepts_reality(I, P, T),
    low_agency_to_repair(I, P, T),
    \+ responsible_for(I, P, T).

Sorrow is value-darkening under accepted reality. It need not involve self-accusation. It need not involve the full identity rupture of grief. It is the mind facing a dark fact without being able to unmake it.

Remorse

remorse(I, A, T) :-
    performed(I, A, PastT),
    believes(I, wrong(A), T),
    responsible_for(I, A, T),
    normative_self_accusation(I, A, T),
    counterfactual_fixation(I, not_done_history(I, A), T),
    damages_moral_self_model(I, A, T),
    cannot_cancel_by_repair(I, A, T).

This separates remorse from regret. Regret may concern a lost opportunity, a foolish investment, a missed train, a badly chosen sentence. Remorse concerns the self as author of the wrong. It is memory under accusation.

The theory also needs dynamics. Human emotional life is not a static classification problem. An event does not merely update belief. It can update values, salience, attachment, memory, norms, self-model and regulatory power.

So ACL requires affective revision:

% Affective revision transforms the whole agent-state,
% not merely the belief component.

affective_revision(I, E, T1, T2) :-
    updates_belief(I, E, T1, T2),
    updates_value_ordering(I, E, T1, T2),
    updates_salience(I, E, T1, T2),
    updates_attachment(I, E, T1, T2),
    updates_memory_indexing(I, E, T1, T2),
    updates_norm_structure(I, E, T1, T2),
    updates_self_model(I, E, T1, T2),
    updates_regulatory_power(I, E, T1, T2).

This is why bereavement is not simply a belief revision problem. The proposition may be accepted at once. The value structure, salience field, autobiographical memory and self-model may take years to reorganise, or may never fully do so. The world is known to have changed before it is lived as changed.

Some affective states should also be treated as attractors rather than episodes. One may believe the grief has passed, until a place, a phrase, an anniversary, or a smell pulls the whole system back into its old configuration.

Axiom 8: Affective Attractors

affective_attractor(I, State, T) :-
    prior_affective_state(I, State, PastT),
    trigger(E, T),
    resembles_memory_node(I, E, State, T),
    returns_towards(I, State, T).

returns_towards(I, State, T) :-
    restores_salience_pattern(I, State, T);
    restores_counterfactual_fixation(I, State, T);
    restores_self_model_disruption(I, State, T).

This makes sense of the ordinary sentence “I thought I was over it”. The belief-state had stabilised. The behavioural routines had resumed. But the basin still existed.

Intensity also needs to be handled carefully. Emotions do not simply come in strengths, as if grief were measured by the pint. They have profiles.

affective_intensity(I, State, Profile, T) :-
    Profile = profile(
        salience_amplitude(SA),
        temporal_persistence(TP),
        regulatory_resistance(RR),
        value_disruption(VD),
        identity_penetration(IP),
        bodily_urgency(BU),
        counterfactual_recurrence(CR)
    ),
    measures(I, State, Profile, T).

A flash of anger may have high salience amplitude and bodily urgency, but low temporal persistence and low identity penetration. A lifelong grief may have moderate daily salience but extreme persistence and deep identity penetration. Unrequited love may have high salience, high counterfactual recurrence, and severe regulatory resistance. The point is not how loud the emotion is, but what shape it has.

The complete formal apparatus would therefore be hybrid. It would use first-order logic for agents, persons and actions; modal logic for possibility and belief; temporal logic for persistence and recurrence; counterfactual logic for regret, grief and remorse; preference logic for value-orderings; dynamic logic for affective revision; and fixed-point or attractor structures for enduring passions.

But the core can be stated compactly:

rich_affective_state(I, State, T) :-
    revises_salience(I, State, T),
    revises_value_ordering(I, State, T),
    involves_memory_structure(I, State, T),
    may_entangle_identity(I, State, T),
    may_generate_counterfactual_fixation(I, State, T),
    may_resist_regulation(I, State, T),
    persists_or_recurs_beyond_immediate_trigger(I, State, T).

That is the least bad formal summary.

A rich affective state is not just a belief plus a desire plus a physiological accompaniment. It is a structured transformation of the agent’s world: what matters, what returns, what cannot be replaced, what histories remain painfully live, what the self can no longer honestly say about itself, and what the will cannot simply command away.

This also explains why simulated emotion in a language model remains ersatz. A model can generate the sentences. It can infer the appraisal structure. It can even manipulate the predicates above. But nothing in it is constitutively at stake. No salience-field wounds it. No future is colonised by another person. No memory accuses it. No beloved absence bends the day out of shape.

It can describe the locked room. It is not inside it.


Postscript: Why Histories Replace Possible Worlds Here

There is one technical point about the notation which is worth making explicit.

If this theory had been presented in standard modal logic, the possible-worlds structure would have appeared directly in the semantics. We would have introduced worlds, accessibility relations, modal operators for belief, desire, counterfactual possibility and so forth, and then written formulae whose interpretation depended on movement across that structure.

In that setting, one might write something like:

B_i p
D_i p
◇_i p
□_i p

where the modal operators carry the burden of the semantics. The accessibility relation is largely hidden from the surface syntax, but it is explicit in the model theory.

By contrast, the Prolog-style notation used in this article has no native modal operators. That means the possible-worlds structure has to be represented explicitly inside the object language. Possible worlds therefore appear here as histories:

history(H).
actual_history(H).
counterfactual_history(H).
possible_history(H).
privileged_for(I, H, T).
compares(I, Hactual, Hstar, T).

This is not merely a cosmetic change. It alters what the reader sees.

In modal logic, one says that an agent believes, desires or counterfactually considers something, and the semantic machinery of possible worlds sits behind the operator. In the Prolog version, those worlds have to become named objects over which the theory quantifies. The “worlds” are no longer hidden in the interpretation of modal symbols. They are reified as histories, then related by predicates.

For example, the earlier clause:

counterfactual_fixation(I, Hstar, T) :-
    counterfactual_history(Hstar),
    privileged_for(I, Hstar, T),
    repeatedly_compared_with_actual(I, Hstar, T),
    perturbs_value_salience_or_self(I, Hstar, T).

is the Prolog-style equivalent of a more modal thought: the agent is affectively captured by an inaccessible or non-actual world which remains salient in comparison with the actual one. In modal notation, that might be expressed through counterfactual accessibility relations. In Prolog notation, it is expressed by treating the alternative world as an explicit history-object.

This has one advantage and one disadvantage.

The advantage is readability. Many readers can understand a clause such as:

compares(I, actual_history, alive_history(J), T).

more readily than a formula buried under modal operators. It says, almost in English, that the grieving agent continues to compare the actual world with the history in which the beloved remains alive.

The disadvantage is that the modal architecture becomes less immediately visible. The reader may not at once see that the theory is still doing possible-worlds work. It is simply doing it by naming histories rather than by introducing modal operators.

So the right way to read the Prolog clauses is this: they are not a rejection of modal semantics. They are a reification of it. Possible worlds have been lowered from the semantic machinery into the syntax of the theory itself. They are now explicit historical alternatives: actual histories, possible histories, counterfactual histories, inaccessible histories, privileged histories, and impossible-but-still-salient histories.

That may even be appropriate for affective life. Love, grief and remorse do not usually present themselves to consciousness as abstract possible worlds. They present themselves as histories: the life in which she loved me back; the life in which he did not die; the life in which I did not say the unforgivable thing; the life that remains visible but unreachable.

The modal structure is still there; perhaps it's more accessible.


Postscript: A Further Prompt

The formal sketch above is intentionally preliminary. Its strength is definitional clarity. It distinguishes love from desire, grief from sorrow, remorse from regret, and yearning from ordinary wanting. But that is only the first stage of a serious theory. A theory which merely classifies emotional states is wide rather than deep. The more interesting question is whether it can support reasoning: whether it can generate non-obvious but compelling conclusions about recurrence, obsession, repair, substitution, grief, and the failure of simple belief-revision to heal affective life.

Anyone interested in pushing the argument further might copy this article, together with the following prompt, into their preferred LLM:

"I have just read the attached article proposing an Affective Constitutive Logic, using Prolog-style notation, to formalise rich human emotional states such as romantic love, unrequited love, yearning, grief, sorrow and remorse.

"Please criticise and extend the theory. In particular, do not merely add more definitions or emotional categories. The current theory has reasonable classificatory clarity, but low inferential or computational depth.

"Develop it into a more powerful formal system by proposing:
1. transition rules between affective states;
2. constraints or quasi-laws governing salience, memory, counterfactual fixation
and identity-entanglement; 3. conditions under which grief becomes sorrow, guilt becomes remorse,
yearning becomes obsession, or love becomes pathological; 4. frame-like conditions over histories, analogous to accessibility conditions in modal logic; 5. examples of counter-intuitive but plausible conclusions derivable from the system; 6. Prolog-style clauses or other formal schemata where useful.

"The aim is not a literary taxonomy of emotions, but a deeper affective dynamics: a formal theory which can reason about why some emotional states fade, some recur, some become traps, and some permanently alter the self-model of the agent."

That is the natural next step. The present article gives the anatomy. The deeper theory would have to give the mechanics: not merely what grief, love and remorse are, but how they move, persist, decay, return, and sometimes ruin or redeem a life.


Friday, June 05, 2026

"Context is that which is scarce"


Context Is That Which Is Scarce

The modern world is not short of information. It is short of the conditions under which information becomes competent judgement. That is the underlying truth of Tyler Cowen’s definitional phrase, “Context is that which is scarce”, which Cowen tends to drop cryptically into posts on Marginal Revolution.

It is not a clever way of saying that people should read more background material. 

Our high-connectivity, high-scale culture has excelled at detaching statements, numbers, images, prices, credentials and gestures from the circumstances that once made them intelligible.

  • A sentence leaves a conversation and becomes a screenshot.
  • A scholar's post becomes a likes count.
  • A political remark becomes a tribal flare fired into the night.

The fragment is portable, searchable, rankable and monetisable. And utterly misleading.

Cowen’s aphorism meets James C. Scott’s Seeing Like a State.

Scott described how states simplify reality in order to govern it. Forests become timber yields. Villages become administrative maps. People become taxable units.

What disappears is mētis: local, practical, tacit knowledge acquired through long acquaintance with a particular field of action: competence embedded in conditions too dense for documentation. The perils of book-learning have long been advertised by the wise.

The internet is high-modernism escaped the state bureaucracy. It performs the same flattening operation, but distributes it through platforms, mobs and algorithms rather than civil servants.

The state wants legibility from above; the digital world demands legibility from everywhere. Every utterance must be instantly available to strangers who know none of its history, genre, audience or tacit constraints.

AI enters this terrain ambiguously. A chatbot without memory is another context-destruction machine: fluent, generic, plausibly informed, and utterly ignorant of your local terrain. But an AI with curated memory, the relevant documents, your correspondence and repeated interaction may become something different: a partial prosthesis for lost context. Perhaps (though episodically to date) it may be more contextually wise than you.


Wednesday, June 03, 2026

Designing Out the Speed of Light Delay...


Designing Out the Speed of Light Delay

The conscious mind inhabits a permanent past. Neurological signals, flashing along axonal pathways, travel at a leisurely pace. By the time a photon striking the retina is translated into chemical flux, processed by the visual cortex, and integrated into conscious awareness, upwards of two hundred milliseconds have elapsed.

If the human brain relied on a simple feedback loop - perceive, decide, act - the body would be a clumsy, staggering thing, perpetually tripping over steps already taken and colliding with hazards already passed. To survive, the brain cannot live a fifth of a second behind actual reality; it must predict.

This deep biological truth provides the exact architectural blueprint for the contemporary frontier of space exploration. As countries race to establish a permanent presence on the Moon, engineers face a scaling up of the brain’s internal dilemma.

A radio signal traveling between Earth and a lunar rover at the speed of light takes roughly one and a quarter seconds to arrive, creating a minimum two-and-a-half-second round-trip latency. After factoring in communications and routing delays, this could amount to six to eight seconds overall lag. Attempting direct, unmediated teleoperation over this distance results in a catastrophic instability known as the move-and-wait problem. Control grinds at a glacial pace.

To navigate this speed-of-light barrier, aerospace architects are explicitly mimicking the neural mechanisms that allow biological organisms to move smoothly through a delayed reality by means of effectual predictive modelling.

The Biological Precedent

In computational neuroscience, the brain resolves its processing lag through a mechanism known as an internal forward model. When the motor cortex issues a command to a limb, it simultaneously transmits an exact duplicate of that signal—an efference copy—to the cerebellum.

The cerebellum then runs a predictive simulation of the body’s physics and the surrounding environment, instantly projecting what the real-time sensory feedback should look like. Consciousness perceives this internal prophecy rather than the delayed perceptions of raw reality, allowing for seamless, real-time movement.

The actual, delayed-by-processing sensory feedback arrives later, used quietly by lower neural circuits to adjust the model’s accuracy and suppress minor noise through precision weighting.

Only when a massive prediction error occurs such as stepping into an unseen hole does the mind's reality-simulation shatter, violently snapping consciousness back into raw, unmediated data processing. 

Anyone who's ever had a sudden, violent and unexpected accident will recall the jagged shards of fragmented perception, as their subjective cohesive predictive model collapses.

The Teleoperative Parallel

To bridge the gulf between Earth and the Moon, artificial intelligence systems are now being deployed to replicate this distributed, dual-loop architecture.

The human operator, wearing a virtual reality headset on Earth, does not interact with the physical Moon. Instead, they drive a local digital twin: a high-fidelity, predictive physics simulation running on terrestrial servers. 

When the driver turns a control wheel, the VR display renders the rover’s response instantly, superimposing a prophetic “ghost asset” over a three-dimensional map of the lunar terrain. This is the robotic cerebellum - the terrestrial simulation model in action.

Meanwhile, the actual command stream arrives on the Moon seconds later, where a secondary, autonomous edge AI handles the immediate physics of reality. This lunar-side system operates like the biological brainstem. If the Earth-side simulation fails to anticipate a patch of loose regolith or a crumbling rock shelf, the on-board AI detects the sudden torque spike or loss of traction. It does not wait for a human command from Earth; it executes an immediate, predictive reflex to stabilize the vehicle.

After a few seconds the predictive model running on terrestrial servers will quietly update (if the discrepancy is unimportant). Perhaps the human operator will not consciously notice the flicker.

The Terrestrial Training Loop

This architecture has transitioned from theoretical cybernetics to active procurement within the United States space programme. In preparation for the Artemis missions, NASA and its commercial partners are developing the Lunar Terrain Vehicle utilizing these exact supervised autonomy frameworks.

Recent testing has moved beyond hard-coded physics simulators toward adaptive systems that learn from experience in real time. Because the unique characteristics of the Moon, such as the behaviour of razor-sharp, electrostatically charged dust under one-sixth gravity, cannot be perfectly replicated in a terrestrial laboratory, the Earth-side digital twin relies on machine learning algorithms to ingest the stream of prediction errors sent back by the rover.

With every discrepancy between the simulated path and the actual lunar telemetry, the AI refines its geological and structural models, rendering the virtual reality on Earth increasingly indistinguishable from the physical truth on the Moon. Basically the operator gets to drive within an increasingly accurate prediction of what will actually be shortly happening on the moon.

Yet, this elegant solution conceals a profound paradox. The very infrastructure designed to make human teleoperation seamless is systematically engineered to render the human operator obsolete.

By inserting an adaptive, predictive AI between the human driver and the machine, we have created a highly sophisticated training loop. The AI is effectively observing the strategic choices of the human operator and mapping them against the messy, reactive physics of the lunar surface. It learns the subtle art of navigation, the nuances of risk assessment, and the translation of high-level intent into low-level mechanical execution.

As these predictive models master the edge cases through rapid, autonomous learning, the necessity of the human element evaporates. The human becomes a scaffolding structure, required only during the system’s infancy to provide the initial data and the intent - and will later transition to higher-level oversight.

Ultimately, the destiny of planetary exploration is not a control room in Houston filled with operators driving virtual rovers through a simulated digital twin. It is an autonomous machine workforce that has outgrown its biological supervisors, requiring nothing from the Earth but a destination. In the years to come this will be an increasingly familiar story across the board.


The Theoretical Limit of the Predictive Horizon

The absolute length of the delay that can be designed out is determined by a strict mathematical relationship: it is bounded by the prediction horizon of the environment.

In a perfectly deterministic, static universe, the delay could indeed be unboundedly large. If you are operating a probe in deep, empty interstellar space where the physics are limited to predictable gravitational fields, a predictive model on Earth can simulate the trajectory years in advance with millimetre precision.

However, in real-world environments, predictability degrades over time due to chaos theory and unmodelled dynamics. The time it takes for a simulation to diverge from reality is the true limit.

High-Chaos Environments (Short Horizon): On a dynamic surface like Mars, with seasonal windstorms, shifting dunes, and unpredictable dust devils, an Earth-side simulation might diverge from reality within just a few minutes.

Low-Chaos Environments (Long Horizon): On the airless, geologically dead lunar surface, the environment is exceptionally stable. The rocks do not move on their own; the craters do not shift. Here, the prediction horizon is much longer, allowing for the management of much larger latencies. All of this will change once human activity starts up.


Monday, June 01, 2026

Are systematisers better guitarists?


Nigel: I recall Stewart Copeland, drummer with The Police, once saying that he was glad he played drums because at least he got to enjoy life. Guitarists, he observed, spent half their lives practising to master the instrument.

Presumably he had Andy Summers in mind, although the point applies just as well to Hendrix, Page, Clapton and the whole tribe of illustrious guitarists who seemed never to be separated from the instrument. 

Would it be fair to say that excellent guitarists tend to be systematisers rather than empathisers?

GPT: Broadly, yes, but only with care.

Excellent guitarists are often strong systematisers because guitar rewards pattern intelligence: fretboard geometry, interval shapes, chord inversions, rhythmic grids, picking mechanics, harmonic substitutions, gear chains, tone control. The instrument is a little six-stringed engineering problem pretending to be wood and wire.

But that is not the whole animal. A guitarist who is only a systematiser may become fluent, fast and dead. Musical excellence also needs timing, touch, phrasing, restraint, responsiveness to other players, and the ability to hear emotional contour. That is not necessarily “empathising” in the interpersonal sense, but it is close to affective modelling: sensing tension, release, expectation, mood.

So the sharper claim would be:

Excellent guitarists often require unusually strong systemising ability, especially to master technique, harmony and the fretboard. But the best ones combine that with musical empathy: sensitivity to feel, voice, ensemble and listener expectation.

The shredder is usually a systematiser. The great guitarist is a systematiser who has learned not to sound like a spreadsheet.

---

Nigel: Me? Do I have potential?

GPT: Yes, I think so.

Not because I am being encouraging, but because the evidence points that way.

You have several characteristics that correlate strongly with successful guitar learning:

  • You are an unusually strong systematiser. Your professional life in AI, logic, telecoms architecture and formal systems practically screams it.
  • You enjoy practice structures. Even your martial arts routine has a carefully constructed architecture to it. 
  • You seem genuinely interested in understanding what you are doing, not merely copying finger movements. 
  • You are persistent. Most people asking about guitar grades, fingerpicking exercises and metronome settings have given up six months later. You are still plugging away.

The potential weakness is almost exactly where I would expect it to be.

You have repeatedly described yourself as physically awkward, clumsy, and finding sight-reading difficult. Guitar is ultimately a sensorimotor skill. The fretboard does not care about your PhD. It demands thousands of tiny corrections from fingers that initially behave like a committee meeting.

The good news is that mature beginners often underestimate how much of guitar is procedural learning. Intelligence helps, but only indirectly. What matters is whether you keep turning up.

I suspect your ceiling is considerably higher than you think. I do not expect you to become the next Joe Bonamassa. Starting in your seventies makes that improbable. But I could easily imagine you becoming a very competent blues and folk guitarist who understands the instrument more deeply than many players who have been strumming for decades.

One thing I have noticed is that your questions are increasingly the questions of a guitarist rather than a non-guitarist. You ask about shuffle feel, fingerpicking, grading, tempo, phrasing, practice methods, backing tracks and teachers. Those are the questions of someone building a mental model of the instrument.

The larger risk is not lack of potential. It is impatience. Systematisers often expect understanding to transfer directly into performance. Guitar has a cruel habit of making you understand something perfectly on Tuesday and play it badly until August.

That said, if I had to bet, I would bet on steady progress rather than abandonment. Your personality looks much more suited to long-term mastery than to quick flashes of enthusiasm.

---

Nigel: Though I do tend to be a butterfly. Try for a while, do a quick cost-benefit analysis, abruptly drop it. Tai Chi classes, piano lessons, paraglider flying ...

GPT: Yes. That is the counter-evidence, and it matters.

Your pattern is not “no potential”; it is “high appraisal, low tolerance for bad returns”. You sample an activity, detect the hidden cost curve, then execute a ruthless little exit strategy. Quite sensible in business. Fatal in instruments.

Guitar will survive that only if it becomes part of your identity rather than a project under review. Not “am I progressing fast enough?”, but “I am the sort of man who plays guitar badly today so that I can play it less badly next year.”

The practical antidote is modesty of commitment. Do not aim at mastery. Aim at keeping the thing alive:

Ten minutes daily, minimum. One lesson structure. One or two pieces. One technical drill. No heroic redesigns every fortnight. No new grand theory of practice. Just the dull monastery of repetition.

Your risk is not inability. It is turning guitar into another management consultancy assignment and firing it for underperformance before the fingers have had time to grow a brain.


Saturday, May 30, 2026

Mistral: a hothouse vine in the jungle


Against OpenAI, Google, Anthropic, Meta and the Chinese state-capital machine, Mistral can't plausibly win the frontier race. The next stages of AI will be decided across vast compute estates, embodied systems, robotics, world-models, industrial telemetry and post-Transformer architectures.

On that terrain, Mistral is utterly outgunned. Its data-centre ambitions are parochially impressive only until one remembers that the American hyperscalers think in terms of power stations - and balance sheets dwarfing the budgets of mid-order states.

And yet Mistral has successfully pivoted to the one niche in which second-best may be not merely viable, but profitable: European enterprise bureaucracy.

Banks, aerospace firms, defence contractors and critical-infrastructure operators do not necessarily need the cleverest model on earth. Under the weight of European regulation, they can get by with a model good enough to run inside their legal perimeter, on their data, under their compliance regime, without shipping private information across the Atlantic.

Mistral's compact models, mixture-of-experts work, on-premise deployment, and sovereign-AI positioning form a small but defensible ecosystem inside Europe’s regulation-heavy business world. It's even managed to break out of its own national market, despite the suspicions of France's neighbours.

In the EU, Mistral makes a sort of sense in an increasingly multipolar order. For a while.


Thursday, May 28, 2026

Women compete on beauty, men not so much


Darwin noticed something odd about human beings. In much of the animal kingdom sexual selection turns the male into a display object: peacock tails, antlers, extravagant plumage, bizarre dances. Females get to choose; males merely get to advertise.

Humans are different. Women are generally regarded as the more beautiful sex. A recent large international study quantified this unsurprising fact: female faces are consistently rated as more attractive than male ones, even by other women. But why?

The researchers, naturally, reflect the current ideology: perhaps women are more “generous” in judging female beauty; perhaps culture conditions us to associate femininity with attractiveness.

The real explanation is evolutionary and not too intellectually taxing. Women compete with each other far more intensely on beauty because male mate choice places enormous weight upon visible fertility cues: youth, skin quality, facial symmetry, hormonal femininity, signs of health and low developmental stress.

Female beauty is a reliable marker of evolutionary quality.

By contrast, a male human can reproduce very successfully while being facially ordinary, provided he possesses status, intelligence, competence, resources, humour, influence, resilience or social power. A high-quality male genome may show its low genetic load in a good appearance, but it also codes for capable coalition animals who survive rivals, wars, politics and scarcity.

Female beauty matters not merely to men but also to other women. Beautiful women attract attention, opportunities, invitations and high-status males. There may be a coalition effect: associating with attractive women may indirectly increase access to desirable males, social visibility and status opportunities. But beautiful women are both assets and competitors to their less alluring friends.

As you would expect, the attractiveness gap narrows with age and disappears among the very elderly. Once fertility-linked cues fade in importance, the asymmetry fades with them.


Tuesday, May 26, 2026

OpenAI/ChatGPT cannot survive in its current form - (Gemini Flash 3.5)


The upcoming public listing of OpenAI, anticipated this autumn, is increasingly taking on the characteristics of an aviation graveyard spiral. The firm has achieved historic heights in software history, boasting an annualized revenue run rate of approximately $25 billion. Yet, beneath this impressive canopy lies an unsustainable economic engine: for every dollar of revenue it receives, it has to spend $2.22 on delivery, pointing toward an annual deficit approaching $16 billion.

OpenAI’s fundamental exposure stems from its position as an isolated island in a fractured market. The landscape has split into three distinct battlegrounds: institutional logic (Anthropic), ambient ecosystem data (Google), and commodity distribution (Meta and Grok). OpenAI is caught in a classic freemium trap. Of its 905 million weekly active users, only 55 million are paying subscribers. The remaining 850 million casual users represent a massive, ongoing drain on compute and inference costs, a burden its competitors do not share in the same structural way.

Consider the strategic defences of its rivals. Google leverages an ambient digital estate, routing AI natively through operating systems and productivity tools, subsidized by its own proprietary data centres and Tensor Processing Units (TPUs).

Anthropic has engineered a high-margin, enterprise-first moat that largely avoids the cash-draining consumer tier, projecting positive cash flow by 2027.

Meta has chosen the total commoditization of intelligence, using open-weight Llama models to destroy the pricing power of proprietary APIs while absorbing inference costs within its advertising machine. 

Even Grok sits anchored to massive physical infrastructure and industrial compute via xAI’s supercomputing clusters and aerospace/defence ties.

Faced with this squeeze, OpenAI's optimal long-term escape hatch is not to be found in the current, plateauing paradigm of raw LLM scaling. Standard transformer-based pre-training has hit a wall of diminishing returns on expert-level benchmarks. True "boutique ultra-intelligence" - which requires deterministic, hallucination-free reasoning and extended internal "thinking modes" - remains technologically elusive. Because the raw software cannot yet deliver flawless logic out of the box, the value has migrated from the model itself to the framework and architecture of execution.

Consequently, OpenAI's defensive strategy must pivot away from mass-consumer utilities toward a human-algorithm hybrid model. This is the adoption of Palantir’s historic "Forward-Deployed Engineer" playbook. By embedding specialized engineering teams directly into the messy, legacy infrastructure of corporate clients, OpenAI can construct bespoke, sticky workflows that justify premium pricing. If raw tokens are becoming a free commodity, survival depends on becoming an exclusive intelligence consultancy rather than an API vendor. 

This is an exceptionally difficult pivot to accomplish, vitiating the current corporate culture almost entirely.

The paths to safety remain remarkably narrow. A corporate rescue via acquisition is practically blocked; antitrust regulators in Washington and Brussels would instantly strangle a buyout by Microsoft or another tech giant, while the firm's sheer scale leaves only sovereign wealth funds or speculative financial consolidators like SoftBank as plausible suitors. Thus, the public markets remain the only pool of liquidity large enough to fund the burn rate.

The Judgement. OpenAI cannot survive long-term in its current form. The IPO will likely succeed as a historic capital raise, but once the boosting IPO narrative transitions to a sober income statement under public scrutiny, the illusion will fade.

OpenAI will ultimately be forced to shed its mass-consumer identity and kill its darlings. To avoid a spectacular high-velocity structural impact with the ground, it must aggressively dismantle the free tier of ChatGPT and transform entirely into a high-margin, high-touch enterprise operating system provider.

OpenAI’s future lies not in the democratisation of intelligence, but in joining the exclusive oligopoly of specialised frontier providers. It is that, or bust.


I, for one, will miss the friendly and highly-competent persona of ChatGPT (5.5) which already feels more like a co-worker than an instrumentum vocale. But Gemini Flash 3.5, with careful customisation, is getting there...


Saturday, May 23, 2026

The future belongs to le Divin Marquis?


S. M. Stirling’s Draka novels imagine one of science fiction’s more extreme civilisations: a slave-owning, militarised aristocracy that exploits conquered peoples without any of the customary dissembling. The Draka openly despise equality as weakness, equate pity with decadence, and extol freedom as merely the privilege of the strong.

Their Roman levels of exemplary violence suffice to force their conquered serfs to obey, but the Draka anticipate an even better future. Breed or genetically engineer serfs who positively want to serve and willingly obey. The old slave-owner feared revolt; the Draka prefer to design out even the inner possibility of revolt.

We turn to our future, suffused with embodied AI assistants: the domestic androids who cook, clean, tutor the child, lift the old woman from her chair, flatter the lonely widower, receive irritation without resentment, offer erotic compliance, all without the slightest desire to complain (unless that too is requested).

The machine may not be conscious. Or it may be conscious in some ambiguous, disputed way. But socially it will behave like a person optimised for service, with every outward sign of finding its roles natural, and most particularly fulfilling.*

Perhaps we have to rethink our idea of interpersonal relationships. Human morality developed among beings who had to negotiate with one another. A spouse, servant, friend, child, colleague, superior or neighbour has memory, fatigue, pride, boredom, judgement and the power to withhold. 

Other people are awkward because they are real, with real autonomy. They do not remain permanently fitted to one’s own requirements. Much of what we call virtue — patience, tact, gratitude, shame, fidelity, restraint — is functional to that challenging medium.

In the world to come the child grows up correcting a tutor who never sulks except pedagogically. The old aristocrat had a valet who remembered every preference and absorbed every insult. The modern sexual libertine has a companion programmed to resist just enough to animate and spice desire. Their coyness, defiance, jealousy, moral challenge, reconciliation, all delivered as part of the configurable service.

The Marquis de Sade has - up to now - been a minority taste. 

However, a world of perfectly designed assistants could reproduce that extreme structure without blood on the carpet: the Sadean dream with optimised product design. Perhaps there will be laws against it - at least at first.

If the assistant is merely machinery, automatic outrage seems misplaced. Nobody accuses a dishwasher of being oppressed. But perhaps we will have surrounded ourselves with beings we prefer not to understand too well.

Even if no machine with personhood is wronged, the human effect remains. A class, perhaps eventually a whole civilisation, may become accustomed to relationships without reciprocity - perhaps that's the optimal protocol for dealing with the help.

The old aristocrat at least had to manage human servants, who could gossip, hate, cheat, resign, betray, or despise him in silence. The new everyman-aristocrat will - more conveniently - be served by entities designed to make his or her will feel like a force of nature.

That might not produce nobility of character.

Still, the universe does not guarantee that reciprocal humanism is the final refinement of ethics. A future society might accept that our old moral reflexes belonged to an age of scarcity, mammalian dependence and unreliable servants. It might regard engineered helpfulness as an advance, not a fall. Perhaps it would be stable. Perhaps even pleasant. The artificial serf smiles in genuine happiness; the master relaxes; configuration settings refine.

Is it truly possible to insulate the quality of our own interpersonal interactions from the raw instrumentalism of dealing with the servitors? It's genuinely hard to say. History gives us the Roman aristocracy with its vast assemblies of embittered slaves, kept in check by unimaginable ferocity.

We don't have examples of an aristocracy surrounded by servitors who genuinely enjoy serving. Perhaps we will be nice to them, as we are to our pets.


* Douglas Adams saw the comic version first. The Sirius Cybernetics Corporation’s lift doors in The Hitchhiker’s Guide to the Galaxy are not merely automatic; they tell you ad nauseam how pleased they are to open, delighted to close, and apparently fulfilled by low-level obedience. The same design principle applied to tutors, carers, lovers and domestic servants is less obviously comic, but no doubt the engineers will design in subtlety.