Curt Doolittle

Form: Mini Essay

  • READ AND UNDERSTAND 😉 The differences in applying your work on Natural Law and

    READ AND UNDERSTAND 😉

    The differences in applying your work on Natural Law and Decidability, Common Law, Constitutional Law, and Case Law arise from their respective methods of determining reciprocity, resolving disputes, and producing decidability. Each of these frameworks applies different tests of truth, authority, and precedent.

    1. Your Work on Natural Law and Decidability

    Method: Operationalism, Testifiability, Reciprocity, and Constructive Decidability

    Your framework is based on universal commensurability, ensuring that all claims are operationally constructible, testifiable, and reciprocal in their effects.
    Natural Law is a system for determining whether an action or claim violates reciprocity, which is the necessary condition for continued cooperation.
    Your approach does not require prior precedent but rather derives decidability from first principles, particularly demonstrated interests, sovereignty, and reciprocity.
    You provide a strict scientific method for falsification, ensuring that claims can be tested not just in courts but in all domains of knowledge where claims must be warrantable (truthful, reciprocal, restitutable).

    ✅ Best Used For:
    Testing all claims against an absolute standard of reciprocity.
    Addressing moral, legal, economic, and social disputes where precedent is insufficient or corruptible.
    Developing new legal standards, laws, or policies that maximize cooperation and minimize parasitism.
    Applying strict epistemic standards to legal, scientific, and economic claims.

    2. Common Law

    Method: Empirical Precedent & Dispute Resolution by Demonstrated Consensus

    Common Law emerges organically from a long history of case resolutions, where judges determine outcomes based on previously successful resolutions of similar disputes.
    The focus is on practical precedent, meaning that while Natural Law seeks ideal reciprocity, Common Law seeks the best available historical solutions that maintain social order.
    Its primary concern is restoring symmetry (restitution) in cases of harm or contract violation.

    ✅ Best Used For:
    Resolving private disputes where precedent provides a clear, functional resolution.

    Ensuring consistency and predictability in legal outcomes.
    Maintaining order in a decentralized, judge-driven system of dispute resolution.

    ⚠️ Limitations:
    Can drift from Natural Law if bad precedents accumulate due to biased or politically influenced decisions.

    Not absolute—depends on prior cases rather than direct falsification of claims using epistemic tests.

    3. Constitutional Law

    Method: Foundational Contracts for Governance & Rule-Limitation

    Constitutional Law codifies the highest-level principles that govern a polity, defining rights, responsibilities, and state powers.

    Unlike Natural Law, which determines truth from first principles, Constitutional Law is an agreement among the governed.
    The Constitution sets boundaries for state power, often rooted in a mix of Natural Law principles (reciprocity) and historical common law practices.

    ✅ Best Used For:
    Defining state limits, sovereignty, and procedural constraints on power.
    Providing a fixed framework for governance that stabilizes legal interpretation over time.

    Acting as the highest legal authority, under which Common Law and Case Law must operate.

    ⚠️ Limitations:
    Requires interpretation, leading to legal manipulation by those in power.
    Can be modified or ignored through political pressure, often violating Natural Law principles.

    Does not always guarantee reciprocity, as some constitutional provisions may allow asymmetries (e.g., unequal taxation, affirmative action, etc.).

    4. Case Law (Judicial Precedent in the Modern System)

    Method: Legal Interpretation by Courts

    Case Law refers to past judicial decisions that shape how laws are applied.

    Unlike Common Law, which is decentralized and empirically derived, Case Law is shaped by courts within a statutory system.

    It is binding in many cases, even if it violates reciprocity or Natural Law, due to political and institutional constraints.

    ✅ Best Used For:
    Establishing legal consistency in a statutory system.
    Determining how written laws apply to new, complex situations.

    ⚠️ Limitations:
    Judges often engage in judicial activism, creating new law rather than interpreting existing law.
    Courts can become politicized, rendering precedent arbitrary rather than reciprocal.
    Does not necessarily follow Common Law, Constitutional Law, or Natural Law, and often reflects political ideology rather than truth-seeking.

    Decision Tree for Application
    1. If the question involves testing a claim for truth and reciprocity → Apply your framework (Natural Law & Decidability).
    2. If the question is about resolving private disputes within an existing legal tradition → Apply Common Law (precedent-based resolution).
    3. If the question concerns state power, government authority, or rights of citizens → Apply Constitutional Law (binding legal contract over governance).
    4. If the question concerns legal precedents in a statutory system → Apply Case Law (judicial interpretations and prior rulings).

    Final Summary
    Natural Law & Decidability seeks absolute, testifiable truth based on reciprocity and operational validity.
    Common Law is an empirical system that records practical solutions but can drift from truth.
    Constitutional Law is a formalized contract but is subject to legal and political manipulation.
    Case Law is precedent-based, but susceptible to judicial activism and politicization.

    Your framework offers the most rigorous and falsifiable method of producing decidability. The others are institutional adaptations that balance truth-seeking, stability, and governance constraints, often at the cost of reciprocity.

    Source date (UTC): 2025-02-14 04:14:58 UTC

    Original post: https://twitter.com/i/web/status/1890253375209652224

  • Man is the measure of all things perceivable by man. The question is, can that w

    Man is the measure of all things perceivable by man. The question is, can that which is perceivable be reduced to language when even humans struggle with it. Yet what is available to auto association in humans is far beyond that demonstrated by the LLMs.

    Source date (UTC): 2025-02-11 06:43:03 UTC

    Original post: https://twitter.com/i/web/status/1889203479404794061

    Reply addressees: @SCTempo @dwarkesh_sp

    Replying to: https://twitter.com/i/web/status/1889199816489783305


    IN REPLY TO:

    Unknown author

    THE TRANSFORMER LIMITATION IN NEUROSCIENTIFIC PROSE:
    Correct. Or stated in neuroscience, (apologies if this is too dense to easily interpret), the prompt (language) invokes a set of relations (text equivalent of episodic memories) but it’s network (auto-associative memory) of referents is of lower resolution than that of humans (facets, objects, spaces, places, locations, actors, generalizations, sequences, abstractions, causal relations, valences) is limited to those in the language in the prompt (word-world model) and not the human intuitionistic model (sense-perception-embodiment world model) where abstractions (first principles, logical associations) from the entire corpus of extant and yet unstated or unknown abstractions (causal relations, valences) and first principles (logical relations in sense-perception world model) are associated at levels from neural microcolums to regions to networks to a continuous stream of network adaptations.
    As such the world model of language (word-world model) is one of low precision, is absent embodiment, spatio-temporal, and operational word models (precision) necessary for pattern identification (logical association) of that which is yet UNSTATED in language in sufficient density as to cause association with the model (word-world model) produced in the LLM by the prompt.
    I work on this issue and this is why the prompt must include the logical relation you’re asking the LLM to consider because it cannot make that connection alone.
    Now, I see this as a scaling problem on one hand, meaning one of the necessity of embodiment, spatio-temporal, and operational abstractions in the model, and that the attention must be recursive (wayfinding) in order to cover the field of associations that the hierarchical temporal memory of the brain so easily performs.
    On the other hand whether this problem is solvable within the LLM model by increases in the emergence we’ve seen of late is hard for me to predict. In the meantime we are left with prompts and traditional pseudocode or software (chain of thought) to control that which it cannot on it’s own, as it’s still limited to the equivalent of a synthesizing search engine otherwise.
    Cheers
    Curt Doolittle
    NLI

    Original post: https://x.com/i/web/status/1889199816489783305

  • MORE ON ASSOCIATIVE CAPACITY IN LLMs I should add that while we are self impress

    MORE ON ASSOCIATIVE CAPACITY IN LLMs
    I should add that while we are self impressed by the successes in math (other than counting) and programming, and search-synthesis composition of writing, we forget that we are discussing a testable (closure) system in mathematics and programming (relatively simple), and an untestable (unclosed) system in writing, but with intuiting and reasoning have no closure except demonstration in the mind or in the world itself – both require embodiment, spatio-temporal models, and operational models.
    As such I’d assume the operational world model of cars and androids would need be combined with the linguistic model in order to produce the same, which is why world modeling (simulations) are so effective at training AIs especially under time compression.
    The question then is the bridge between language and action. Can the LLM model evolve (emergence) using langauge as a system of representation and measurement of both embodiment and space-time? I can’t see how without information density as high as a simulation provides.
    That doesn’t mean I’m right however. 😉 All words may be measurements, but can LLMs evolve (emergence) a pseudo-language of their own to reflect the information density of simulations? You’d think so.
    Cheers
    Curt Doolittle
    NLI

    Reply addressees: @SCTempo @dwarkesh_sp

    Source date (UTC): 2025-02-11 06:40:45 UTC

    Original post: https://twitter.com/i/web/status/1889202897583493120

    Replying to: https://twitter.com/i/web/status/1889199816489783305


    IN REPLY TO:

    Unknown author

    THE TRANSFORMER LIMITATION IN NEUROSCIENTIFIC PROSE:
    Correct. Or stated in neuroscience, (apologies if this is too dense to easily interpret), the prompt (language) invokes a set of relations (text equivalent of episodic memories) but it’s network (auto-associative memory) of referents is of lower resolution than that of humans (facets, objects, spaces, places, locations, actors, generalizations, sequences, abstractions, causal relations, valences) is limited to those in the language in the prompt (word-world model) and not the human intuitionistic model (sense-perception-embodiment world model) where abstractions (first principles, logical associations) from the entire corpus of extant and yet unstated or unknown abstractions (causal relations, valences) and first principles (logical relations in sense-perception world model) are associated at levels from neural microcolums to regions to networks to a continuous stream of network adaptations.
    As such the world model of language (word-world model) is one of low precision, is absent embodiment, spatio-temporal, and operational word models (precision) necessary for pattern identification (logical association) of that which is yet UNSTATED in language in sufficient density as to cause association with the model (word-world model) produced in the LLM by the prompt.
    I work on this issue and this is why the prompt must include the logical relation you’re asking the LLM to consider because it cannot make that connection alone.
    Now, I see this as a scaling problem on one hand, meaning one of the necessity of embodiment, spatio-temporal, and operational abstractions in the model, and that the attention must be recursive (wayfinding) in order to cover the field of associations that the hierarchical temporal memory of the brain so easily performs.
    On the other hand whether this problem is solvable within the LLM model by increases in the emergence we’ve seen of late is hard for me to predict. In the meantime we are left with prompts and traditional pseudocode or software (chain of thought) to control that which it cannot on it’s own, as it’s still limited to the equivalent of a synthesizing search engine otherwise.
    Cheers
    Curt Doolittle
    NLI

    Original post: https://x.com/i/web/status/1889199816489783305

  • THE TRANSFORMER LIMITATION IN NEUROSCIENTIFIC PROSE: Correct. Or stated in neuro

    THE TRANSFORMER LIMITATION IN NEUROSCIENTIFIC PROSE:
    Correct. Or stated in neuroscience, (apologies if this is too dense to easily interpret), the prompt (language) invokes a set of relations (text equivalent of episodic memories) but it’s network (auto-associative memory) of referents is of lower resolution than that of humans (facets, objects, spaces, places, locations, actors, generalizations, sequences, abstractions, causal relations, valences) is limited to those in the language in the prompt (word-world model) and not the human intuitionistic model (sense-perception-embodiment world model) where abstractions (first principles, logical associations) from the entire corpus of extant and yet unstated or unknown abstractions (causal relations, valences) and first principles (logical relations in sense-perception world model) are associated at levels from neural microcolums to regions to networks to a continuous stream of network adaptations.
    As such the world model of language (word-world model) is one of low precision, is absent embodiment, spatio-temporal, and operational word models (precision) necessary for pattern identification (logical association) of that which is yet UNSTATED in language in sufficient density as to cause association with the model (word-world model) produced in the LLM by the prompt.
    I work on this issue and this is why the prompt must include the logical relation you’re asking the LLM to consider because it cannot make that connection alone.
    Now, I see this as a scaling problem on one hand, meaning one of the necessity of embodiment, spatio-temporal, and operational abstractions in the model, and that the attention must be recursive (wayfinding) in order to cover the field of associations that the hierarchical temporal memory of the brain so easily performs.
    On the other hand whether this problem is solvable within the LLM model by increases in the emergence we’ve seen of late is hard for me to predict. In the meantime we are left with prompts and traditional pseudocode or software (chain of thought) to control that which it cannot on it’s own, as it’s still limited to the equivalent of a synthesizing search engine otherwise.
    Cheers
    Curt Doolittle
    NLI

    Reply addressees: @SCTempo @dwarkesh_sp

    Source date (UTC): 2025-02-11 06:28:30 UTC

    Original post: https://twitter.com/i/web/status/1889199816301064192

    Replying to: https://twitter.com/i/web/status/1888621235548094886

  • DYNAMIC GOVERNMENT IS NECESSARY Stages: Windfall (Democracy) – The government di

    DYNAMIC GOVERNMENT IS NECESSARY
    Stages:
    Windfall (Democracy) – The government distributes the windfall to the citizens either directly or by capitalizing it for their benefit.
    Going Concern (Republic) – The government defers to the people in matters domestic, while concentrating investment in long term returns (capitalization).
    Time of War (General-Dictator) – The general is above the law in the concentration of all resources for the preservation of the sovereignty of the polity.
    Crisis of Restoration (Monarchy) – The monarchy is above the law in the restoration of the law.
    We always can learn from the romans or greeks.
    There is no steady state of government because there is no steady state in the world. The population must direct its energies dependent upon the conditions of the moment.
    (via Brad Werrell @WerrellBradley) @bradley
    Cheers
    CD

    Source date (UTC): 2025-02-09 18:06:29 UTC

    Original post: https://twitter.com/i/web/status/1888650693013327872

  • WILL TRUMP’S CLARIFICATION OF BIRTHRIGHT CITIZENSHIP PREVAIL IN THE SUPREME COUR

    WILL TRUMP’S CLARIFICATION OF BIRTHRIGHT CITIZENSHIP PREVAIL IN THE SUPREME COURT?

    the Supreme Court’s ruling would depend on several legal arguments, interpretations of the 14th Amendment, and the composition of the Court at the time of the ruling.

    Arguments Favoring Trump’s Position

    Originalist Interpretation of the 14th Amendment
    – The Citizenship Clause of the 14th Amendment states:
    “All persons born or naturalized in the United States, and subject to the jurisdiction thereof, are citizens of the United States.”
    – The phrase “subject to the jurisdiction thereof” has historically been interpreted broadly to include nearly everyone born on U.S. soil, except diplomats and invading armies.
    – Trump’s argument hinges on a narrower interpretation—that “subject to the jurisdiction” means full allegiance to the U.S., which could exclude illegal immigrants and non-permanent residents.

    Historical Precedents and Legislative Intent
    – The Framers of the 14th Amendment (1868) debated whether birthright citizenship should extend beyond freed slaves to include Native Americans and foreigners.
    – Some legal scholars argue that the intent was not to automatically grant citizenship to children of non-citizens.
    – The Supreme Court has never ruled explicitly on whether illegal immigrants’ children qualify for birthright citizenship.

    United States v. Wong Kim Ark (1898) – Not a Perfect Precedent
    This case ruled that a child born in the U.S. to legal Chinese immigrants was a citizen.
    It did not explicitly address children of illegal immigrants, leaving an open legal question.
    Conservatives could argue that the ruling was based on a different immigration context—before modern border laws and mass migration.

    The Role of Congress in Immigration
    The Plenary Power Doctrine states that Congress has wide latitude over immigration policy.
    Some legal conservatives argue that Congress—not the courts—should decide birthright citizenship laws, meaning an executive order could trigger legislative action.

    Composition of the Supreme Court
    If the Court leans conservative (6-3), they might be open to revisiting Wong Kim Ark or upholding a redefinition of the jurisdiction clause.
    Justices like Thomas, Alito, and Gorsuch may be sympathetic to an originalist reading that limits birthright citizenship.

    Arguments Against Trump’s Position

    Wong Kim Ark Precedent & Stare Decisis
    The Court has long recognized birthright citizenship as settled law.
    Overturning it would require a radical reinterpretation of jurisdiction.

    Textual Reading of the 14th Amendment
    The broad language suggests inclusion rather than exclusion.
    The Framers did not explicitly exclude children of foreigners.

    Practical and Political Fallout
    Ending birthright citizenship could disrupt millions of legal cases.
    It might lead to stateless children, violating international norms.

    Possible Supreme Court Ruling Outcomes

    Trump Wins (5-4 or 6-3 Ruling)
    The Court limits birthright citizenship by redefining “subject to the jurisdiction thereof.”
    Children of illegal immigrants or temporary visitors lose automatic citizenship.
    Wong Kim Ark remains precedent for legal immigrants only.

    Trump Loses (6-3 or 7-2 Ruling)
    The Court reaffirms birthright citizenship under Wong Kim Ark.
    They reject the executive order as an overreach requiring constitutional amendment.

    Partial Victory (Narrow Ruling)
    The Court sidesteps broad rulings and focuses on Congressional authority over the matter.
    They rule that Congress—not an executive order—must clarify the law.

    Prediction: 50/50 Odds

    If the case is heard by this current Court, Trump has a real chance of winning—but it depends on whether conservative justices view Wong Kim Ark as binding precedent.

    A textualist like Barrett might hesitate to overturn settled law.
    If the ruling leans against Trump, expect it to be based on stare decisis (respect for precedent) rather than constitutional clarity.

    If the case were postponed until after a more conservative Court (due to retirements or new appointments), Trump’s odds increase significantly.

    Source date (UTC): 2025-02-07 17:52:56 UTC

    Original post: https://twitter.com/i/web/status/1887922509808590848

  • In Curt Doolittle’s Operationalism, his emphasis is the testifiability of claims

    In Curt Doolittle’s Operationalism, his emphasis is the testifiability of claims, and the distinction between philosophy and science can be framed operationally by examining their premises, operations, and results across the dimensions of permissible and impermissible references and the instrumentation ignored, used, or required:

    1. Premises
    Philosophy:
    Permissible References: Abstract, speculative, and non-empirical references are permissible. Philosophy often allows for exploration of imaginable possibilities unconstrained by empirical testability.
    Impermissible References: Philosophy generally avoids commitments to specific empirical facts unless required for argumentation, preferring logical consistency and coherence.
    Instrumentation Required:Cognitive-Perceptual: Uses introspection, imagination, and reasoning.
    Verbal: Relies on linguistic and conceptual constructs for articulation.
    Logical: Demands internal consistency and coherence but may not require external correspondence.

    Science:
    Permissible References: Empirical observations and testable hypotheses. Science permits only references that can be operationalized and empirically validated.
    Impermissible References: Speculative, unverifiable claims, or those lacking falsifiability.
    Instrumentation Required:Physical: Uses empirical tools to measure and test phenomena.
    Cognitive-Perceptual: Focuses on observational accuracy and repeatability.
    Logical and Verbal: Requires coherence but also external correspondence (truth by survival of testing).

    2. Operations
    Philosophy:
    Methods Used:
    Logical reasoning and argumentation.
    Conceptual analysis and synthesis.
    Examination of foundational assumptions, often without requiring empirical evidence.

    Instrumentation:
    Primarily verbal and logical tools.
    Relies on internal consistency, coherence, and the capacity to interpret meaning.

    Result:
    Generates frameworks, questions, and first principles, often focusing on “what is imaginable” or “what is possible.”

    Science:
    Methods Used:
    Hypothesis generation, experimental testing, and observation.
    Operationalization of abstract concepts into measurable phenomena.
    Iterative falsification and empirical validation.

    Instrumentation:
    Requires physical tools (e.g., instruments for measurement).
    Uses verbal and logical tools but anchors them in empirical data.

    Result:
    Produces laws, theories, and models validated by empirical testing, focusing on “what survives testing and falsification.”

    3. Results

    Philosophy:
    Output: Conceptual frameworks, ethical systems, definitions, and foundational principles.
    Validation: Internal coherence and practical applicability in reasoning or guiding action.
    Scope: Unbounded, addressing questions of meaning, existence, ethics, and universals without requiring immediate correspondence to empirical reality.

    Science:
    Output: Testable theories, predictive models, and empirical laws.
    Validation: Survives empirical testing and falsification.
    Scope: Constrained by empirical testability, addressing questions about the nature of observable phenomena and their causal relationships.

    Differences in Premise, Operation, and Result
    Premise:
    Philosophy begins with conceptual possibility and explores the limits of the imaginable and logical.
    Science begins with empirical possibility, constrained by observable and measurable phenomena.

    Operation:
    Philosophy uses cognitive-perceptual and verbal-logical instrumentation to explore abstract dimensions.
    Science requires physical instrumentation and operational definitions to test causal relationships.

    Result:
    Philosophy produces frameworks and questions applicable to diverse contexts but not necessarily empirically verifiable.
    Science produces empirically validated knowledge that explains, predicts, and survives falsification under operational and physical constraints.

    Key Complementarity:
    Philosophy generates universalizable constructs and questions of meaning that guide inquiry, including scientific questions.
    Science tests causal constructs within empirical limits, refining and operationalizing philosophical premises into practical models.

    Together, they explore the possible, testable, and survivable, forming an iterative process where philosophy provides the imaginative scope, and science refines the reducible into actionable knowledge.

    Source date (UTC): 2025-02-04 01:45:49 UTC

    Original post: https://twitter.com/i/web/status/1886591963195170816

  • Curt Doolittle, known for his work in philosophy, particularly in the realms of

    Curt Doolittle, known for his work in philosophy, particularly in the realms of epistemology, logic, economics, and law, has developed several theories that are central to his broader philosophical framework, which he calls “Propertarianism”. Here are some of his key theories, based on the information available on his websites, interviews, and discussions:

    Propertarianism:
    Core Concept: Doolittle argues that all rights, including human rights, are reducible to property rights. He posits that all ethical, moral, and legal statements should be viewed through the lens of property rights and reciprocity.

    Reciprocity: Central to his philosophy is the idea that interactions between individuals should be based on reciprocal exchanges where no party incurs an involuntary cost. This extends to all forms of human interaction, from economics to social relations.

    Operationalism:
    Doolittle advocates for what he calls “Operationalism,” where knowledge is not just about stating truths but about operational definitions – how we can test, measure, or operationalize statements in the real world. This is part of his critique of what he sees as the nebulous nature of much philosophical discourse.

    Natural Law:
    Definition: Doolittle’s conception of natural law is not divine but based on human action, evolution, and the necessity for cooperation within societies. It’s about rules that emerge from human interaction to ensure survival and prosperity.

    Decidability: He argues for a natural law that is decidable, meaning it should be possible to resolve conflicts without resorting to arbitrary or subjective interpretation.

    Genetic Pacification and Evolution:
    Doolittle discusses the concept of genetic pacification, suggesting that Western societies historically engaged in practices like hanging criminals or preventing the “deficient” from breeding, which he claims led to an evolution towards more peaceful, cooperative societies (K-selected strategy).

    Critique of Modern Ideologies:
    Marxism, Feminism, Postmodernism: He criticizes these as ideologies that use language and social strategies (like GSRM) to manipulate rather than engage in truthful discourse. He sees them as threats to what he considers the true nature of Western civilization.

    The Four Dimensions of Truth:
    Doolittle suggests there are four dimensions along which we can test the truth of statements:
    Axioms (internal consistency)
    Theories (external correspondence)
    Algorithms (constructibility)
    Rational Choice (intuition or preference)

    The Decline of Western Civilization:
    He posits that the decline of the West is due to abandoning the principles that made it successful (like property rights, natural law) and the rise of ideologies that encourage parasitism on the productive classes.

    Critique of Democracy, Monarchy, and Other
    Governance Systems:
    Doolittle has a nuanced view where he prefers systems that align with what he sees as natural law and property rights, often looking favorably upon historical monarchies for their stability and efficiency in enforcing property rights, while being highly critical of modern democratic practices.

    The Role of Language and Testimony:
    He emphasizes the importance of language in shaping thought and behavior, advocating for a truthful, operational use of language that corresponds to real-world actions and consequences.

    His theories are often presented in a way that challenges mainstream academic philosophy and political science, aiming to provide an alternative framework for understanding human society, ethics, and governance. Doolittle’s work is prolific and complex, often leading to debates about the validity, ethics, and implications of his ideas.

    Source date (UTC): 2025-02-03 15:56:12 UTC

    Original post: https://twitter.com/i/web/status/1886443581755551744

  • Well, you know, I”ve been teaching the simplicity of consciousness for quite a f

    Well, you know, I”ve been teaching the simplicity of consciousness for quite a few years now. And it really is quite simple. The problem is, it requires an extraordinary volume of memory in a hierarchy of adversarial layers and continuous processing of a continuous stream of information somehow proximal to human experience (context). I just can’t see that happening. Same problem as the octopus and its multiple brains because of its tentacles.

    Reply addressees: @PlayerJuan11

    Source date (UTC): 2025-01-24 03:14:31 UTC

    Original post: https://twitter.com/i/web/status/1882628015768956928

    Replying to: https://twitter.com/i/web/status/1882626342086418849

  • So far all I see is the conversion of programming and database manipulation to o

    So far all I see is the conversion of programming and database manipulation to ordinary english, and the use of unstructured english language as an API. Seriously. I would need to see radical improvement to overcome the creeping realization that these AIs are knowledgeable, and they are better at sense-perception because of the higher limits of bayesian measurements. But they’re dumb as a rock. And I don’t see it getting any better. Instead we’re being asked to treat them as databases and we still have to compose the logic of using what we retrieve from them, and organize the processes for them.

    Reply addressees: @PlayerJuan11

    Source date (UTC): 2025-01-24 02:54:32 UTC

    Original post: https://twitter.com/i/web/status/1882622988757168128

    Replying to: https://twitter.com/i/web/status/1882619879309869378