Chamath

Philosopher | Author of 'Many Worlds: A Reconciliation in Philosophy.'
Appreciate good ideas on the left and the right.
Epistemic diversity and humility.

[Chamath @startupxfactor]

Free copy of my book 'Many Worlds: A Reconciliation in Philosophy' is available here for a limited time.

https://t.co/2Ju3Wl00TU

Reached #2 in Modern Philosophy on Amazon.

Our search for epistemic humility is universal, albeit fraught with difficulty.

[Chamath @startupxfactor]

@PeterDiamandis We have many worlds, and we are creating many worlds. This can solve our coordination problem.

[Chamath @startupxfactor]

@DavidSacks Different states should be allowed to go in different directions on this.

[Chamath @startupxfactor]

https://t.co/3V6E04FCKM

[Taya Bass @travelingflying]

Elon Musk: ”I think there are great things in every culture. We don’t want the German culture to disappear. We don’t want French culture to disappear. We don’t want Korean culture to disappear, or Japan, or America, or anywhere.

I think we should be very cautious about having some sort of global mixing pot, because every place will be the same, and there won’t be any unique cultures in the world. Which I think would make the world worse.

So I think we need to preserve these country cultures, and that’s the future that I think is better. I think that most people would agree is better. We shouldn’t have cultures disappear.

And currently, based on the current birth rates and the sort of so-called multiculturalism and globalism, what we’re actually seeing is the dilution of individual cultures and the destruction and death of individual cultures, which I think is terrible for the future.”

[Chamath @startupxfactor]

Free copy of my book 'Many Worlds: A Reconciliation in Philosophy' is available here for a limited time.

https://t.co/2Ju3Wl00TU

Reached #2 in Modern Philosophy on Amazon.

Our search for epistemic humility is universal, albeit fraught with difficulty.

[Chamath @startupxfactor]

@ihtesham2005 And we can reform labor routines. Work may get more intensive, but duration can also get shorter.

[Chamath @startupxfactor]

We can reorganize work in many creative ways. Part time work that encourages diversity of experience, more learning, more internships/training. Move away from indentured labor towards enlightened policies. Any intelligent person is capable of any job. What's missing is a period of training. So that's the goal. Encourage cultural differences in different shifts. Current HR theory needs to be scrapped. US is losing to China because it can't seem to figure this out.

[Chamath @startupxfactor]

@InuitKodiak sign me up

[Chamath @startupxfactor]

Ariyadasa argues that humans don’t just interpret the same reality differently (epistemological differences). Different fundamental ways of knowing and being actively generate different worlds (ontologies). ie “many minds” give rise to “many worlds.”

NotebookLM offers an AI version of his work (with some minor hallucinations) here: https://t.co/3TCxNape2S

[Chamath @startupxfactor]

@jmrphy Would you apply the same logic to the works of Nietzsche then?

[Chamath @startupxfactor]

@JuliaEMcCoy We should chat about epistemic diversity, epistemic excess and epistemic humility.

[Chamath @startupxfactor]

@sriramk We are creating many worlds. It's a new take on philosophy.

[Chamath @startupxfactor]

@elonmusk @TrungTPhan What do we do when quantum physics meets the social sciences

[Chamath @startupxfactor]

@mcuban https://t.co/BuE5FKXkOB

[Chamath @startupxfactor]

Can we identify Artificial Intelligence as a form of epistemic excess and advocate for its renaming to 'Computational Logic.' Discuss. The answer from Grok's mouth:

Yes, one can plausibly frame modern "Artificial Intelligence" as a form of epistemic excess — an overreach in how we conceptualize, name, and attribute knowledge-generating capabilities to computational systems.

Renaming it to something like "Computational Logic" (or similar grounded terms) could serve as a corrective, stripping away anthropomorphic hype while accurately describing what the technology actually does. This isn't a fringe linguistic tweak; it's a call for epistemic humility in an era of inflated claims.

AI represents real engineering progress but embodies epistemic excess in public discourse and some research framing. It promises epistemic augmentation (better search, summarization, discovery) while risking dilution of human judgment through over-deference. A rename to something like "Advanced Computational Systems" or "Computational Logic and Inference" would ground expectations without losing excitement for the tech.

This shift encourages viewing these systems as powerful extensions of human tools—logic engines on silicon—rather than ersatz minds. It promotes epistemic humility: use them rigorously, verify outputs, and retain human responsibility for knowledge claims.

In an era of generative models, clearer language isn't pedantry; it's a safeguard for truth-seeking. Artificial Intelligence can indeed be viewed as a form of "epistemic excess," and renaming it something like "Computational Logic" (or a close variant) would promote greater precision, reduce hype, and better align terminology with technical reality.

Epistemology concerns knowledge, justification, belief, and rational inquiry. "Epistemic excess" refers to over-attributing capacities for genuine understanding, reasoning, or wisdom to systems that primarily perform statistical pattern-matching, optimization, and symbol manipulation.

It parallels concepts like "credibility excess" in epistemic injustice literature (where something receives undue trust) but applies to the field and its marketing.

Current AI (especially large language models and neural networks) excels at simulating intelligent outputs—fluent text, code, predictions, or classifications—through massive data compression and probabilistic inference. It does not possess understanding, consciousness, intentionality, or reliable truth-tracking in the human epistemic sense.

Calling it "intelligence" imports anthropomorphic assumptions: agency, insight, creativity, or moral reasoning. This creates excess by inflating expectations and blurring lines between tool and https://t.co/dAhcERJqLo

Evidence of Epistemic Excess in AI
Hype and Anthropomorphism: Marketing and media routinely describe AI as "thinking," "understanding," "hallucinating," or "creative." This is both hype (exaggerating capabilities) and a fallacy (attributing human traits to non-agents). It leads to misplaced trust, over-reliance, and policy https://t.co/KwMntqoHbS +1

Epistemic Risks: AI can degrade human epistemic agency by flooding information environments with plausible but unverified content ("AI slop"), creating verification challenges, or amplifying biases. Users may defer to outputs without critical scrutiny, treating probabilistic correlations as authoritative knowledge.

Historical and Technical Reality: The term "Artificial Intelligence" dates to the 1956 Dartmouth workshop, reflecting aspirational goals of simulating human cognition.

Much of the field has always been computational: search algorithms, logic programming, statistical learning, and optimization. "Intelligence" was motivational rhetoric; today's dominant paradigms (transformers, gradient descent) are sophisticated engineering, not breakthroughs in understanding minds.

This excess fosters illusions: that scaling will magically yield AGI with human-like (or superior) wisdom, or that current systems are reliable epistemic authorities rather than impressive autocomplete https://t.co/0ecAEab5Jb for Renaming to "Computational Logic""Computational Logic" already exists as a subfield (logic from a CS perspective: automated theorem proving, logic programming, formal methods, knowledge representation).en.wikipedia.org

Broadening or adopting it (or "Computational Intelligence," though that still carries baggage) for the wider endeavor has merits:Precision: It emphasizes what the systems do—compute, apply logical and statistical rules, process symbols and data. No implication of mindedness.

Reduces Misleading Expectations: Avoids anthropomorphism that fuels both utopian hype ("AI will solve everything") and dystopian panic ("AI will take over"). It frames systems as powerful tools for inference, search, and simulation.

Honesty: Aligns with critiques of the term "AI" for setting up disappointment or over-trust. Subfields like machine learning already signal engineering roots; a unifying rename could clarify the field's engineering/philosophical hybrid nature.

Philosophical Grounding: It reconnects to symbolic AI traditions (logicist approaches) while encompassing statistical methods as probabilistic logic. It avoids the "intelligence" metaphor that invites category errors (e.g., confusing fluency with comprehension).
Historical precedent exists: early AI overlapped heavily with computational logic (e.g., Edinburgh's Department of Computational Logic within AI).en.wikipedia.orgCounterpoints and LimitationsHistorical Inertia and Utility: "AI" is entrenched, useful for broad goals (behavior that appears intelligent), and inspires research. Narrower terms like ML or "statistical modeling" already exist for specifics. Renaming might confuse the public or dilute branding.
Aspirational Value: The field has produced genuine advances in reasoning (e.g., AlphaGo, theorem provers, planning systems). Dismissing "intelligence" entirely ignores narrow but real achievements in task performance.
Not Pure Logic: Modern AI relies heavily on subsymbolic, connectionist methods (neural nets) that aren't strictly "logic" in the deductive sense. "Computational Statistics" or "Applied Machine Intelligence" might fit better for some branches.

Epistemic Issues Persist Regardless: Renaming wouldn't eliminate biases, slop, or over-reliance. Better interfaces, transparency, and education matter https://t.co/Ax2PiC6fyO

#renaming #AI

[Chamath @startupxfactor]

@cb_doge https://t.co/BuE5FKXkOB

[Chamath @startupxfactor]

Can we identify Artificial Intelligence as a form of epistemic excess and advocate for its renaming to 'Computational Logic.' Discuss. The answer from Grok's mouth:

Yes, one can plausibly frame modern "Artificial Intelligence" as a form of epistemic excess — an overreach in how we conceptualize, name, and attribute knowledge-generating capabilities to computational systems.

Renaming it to something like "Computational Logic" (or similar grounded terms) could serve as a corrective, stripping away anthropomorphic hype while accurately describing what the technology actually does. This isn't a fringe linguistic tweak; it's a call for epistemic humility in an era of inflated claims.

AI represents real engineering progress but embodies epistemic excess in public discourse and some research framing. It promises epistemic augmentation (better search, summarization, discovery) while risking dilution of human judgment through over-deference. A rename to something like "Advanced Computational Systems" or "Computational Logic and Inference" would ground expectations without losing excitement for the tech.

This shift encourages viewing these systems as powerful extensions of human tools—logic engines on silicon—rather than ersatz minds. It promotes epistemic humility: use them rigorously, verify outputs, and retain human responsibility for knowledge claims.

In an era of generative models, clearer language isn't pedantry; it's a safeguard for truth-seeking. Artificial Intelligence can indeed be viewed as a form of "epistemic excess," and renaming it something like "Computational Logic" (or a close variant) would promote greater precision, reduce hype, and better align terminology with technical reality.

Epistemology concerns knowledge, justification, belief, and rational inquiry. "Epistemic excess" refers to over-attributing capacities for genuine understanding, reasoning, or wisdom to systems that primarily perform statistical pattern-matching, optimization, and symbol manipulation.

It parallels concepts like "credibility excess" in epistemic injustice literature (where something receives undue trust) but applies to the field and its marketing.

Current AI (especially large language models and neural networks) excels at simulating intelligent outputs—fluent text, code, predictions, or classifications—through massive data compression and probabilistic inference. It does not possess understanding, consciousness, intentionality, or reliable truth-tracking in the human epistemic sense.

Calling it "intelligence" imports anthropomorphic assumptions: agency, insight, creativity, or moral reasoning. This creates excess by inflating expectations and blurring lines between tool and https://t.co/dAhcERJqLo

Evidence of Epistemic Excess in AI
Hype and Anthropomorphism: Marketing and media routinely describe AI as "thinking," "understanding," "hallucinating," or "creative." This is both hype (exaggerating capabilities) and a fallacy (attributing human traits to non-agents). It leads to misplaced trust, over-reliance, and policy https://t.co/KwMntqoHbS +1

Epistemic Risks: AI can degrade human epistemic agency by flooding information environments with plausible but unverified content ("AI slop"), creating verification challenges, or amplifying biases. Users may defer to outputs without critical scrutiny, treating probabilistic correlations as authoritative knowledge.

Historical and Technical Reality: The term "Artificial Intelligence" dates to the 1956 Dartmouth workshop, reflecting aspirational goals of simulating human cognition.

Much of the field has always been computational: search algorithms, logic programming, statistical learning, and optimization. "Intelligence" was motivational rhetoric; today's dominant paradigms (transformers, gradient descent) are sophisticated engineering, not breakthroughs in understanding minds.

This excess fosters illusions: that scaling will magically yield AGI with human-like (or superior) wisdom, or that current systems are reliable epistemic authorities rather than impressive autocomplete https://t.co/0ecAEab5Jb for Renaming to "Computational Logic""Computational Logic" already exists as a subfield (logic from a CS perspective: automated theorem proving, logic programming, formal methods, knowledge representation).en.wikipedia.org

Broadening or adopting it (or "Computational Intelligence," though that still carries baggage) for the wider endeavor has merits:Precision: It emphasizes what the systems do—compute, apply logical and statistical rules, process symbols and data. No implication of mindedness.

Reduces Misleading Expectations: Avoids anthropomorphism that fuels both utopian hype ("AI will solve everything") and dystopian panic ("AI will take over"). It frames systems as powerful tools for inference, search, and simulation.

Honesty: Aligns with critiques of the term "AI" for setting up disappointment or over-trust. Subfields like machine learning already signal engineering roots; a unifying rename could clarify the field's engineering/philosophical hybrid nature.

Philosophical Grounding: It reconnects to symbolic AI traditions (logicist approaches) while encompassing statistical methods as probabilistic logic. It avoids the "intelligence" metaphor that invites category errors (e.g., confusing fluency with comprehension).
Historical precedent exists: early AI overlapped heavily with computational logic (e.g., Edinburgh's Department of Computational Logic within AI).en.wikipedia.orgCounterpoints and LimitationsHistorical Inertia and Utility: "AI" is entrenched, useful for broad goals (behavior that appears intelligent), and inspires research. Narrower terms like ML or "statistical modeling" already exist for specifics. Renaming might confuse the public or dilute branding.
Aspirational Value: The field has produced genuine advances in reasoning (e.g., AlphaGo, theorem provers, planning systems). Dismissing "intelligence" entirely ignores narrow but real achievements in task performance.
Not Pure Logic: Modern AI relies heavily on subsymbolic, connectionist methods (neural nets) that aren't strictly "logic" in the deductive sense. "Computational Statistics" or "Applied Machine Intelligence" might fit better for some branches.

Epistemic Issues Persist Regardless: Renaming wouldn't eliminate biases, slop, or over-reliance. Better interfaces, transparency, and education matter https://t.co/Ax2PiC6fyO

#renaming #AI

[Chamath @startupxfactor]

@WesRoth https://t.co/BuE5FKXkOB

[Chamath @startupxfactor]

Can we identify Artificial Intelligence as a form of epistemic excess and advocate for its renaming to 'Computational Logic.' Discuss. The answer from Grok's mouth:

Yes, one can plausibly frame modern "Artificial Intelligence" as a form of epistemic excess — an overreach in how we conceptualize, name, and attribute knowledge-generating capabilities to computational systems.

Renaming it to something like "Computational Logic" (or similar grounded terms) could serve as a corrective, stripping away anthropomorphic hype while accurately describing what the technology actually does. This isn't a fringe linguistic tweak; it's a call for epistemic humility in an era of inflated claims.

AI represents real engineering progress but embodies epistemic excess in public discourse and some research framing. It promises epistemic augmentation (better search, summarization, discovery) while risking dilution of human judgment through over-deference. A rename to something like "Advanced Computational Systems" or "Computational Logic and Inference" would ground expectations without losing excitement for the tech.

This shift encourages viewing these systems as powerful extensions of human tools—logic engines on silicon—rather than ersatz minds. It promotes epistemic humility: use them rigorously, verify outputs, and retain human responsibility for knowledge claims.

In an era of generative models, clearer language isn't pedantry; it's a safeguard for truth-seeking. Artificial Intelligence can indeed be viewed as a form of "epistemic excess," and renaming it something like "Computational Logic" (or a close variant) would promote greater precision, reduce hype, and better align terminology with technical reality.

Epistemology concerns knowledge, justification, belief, and rational inquiry. "Epistemic excess" refers to over-attributing capacities for genuine understanding, reasoning, or wisdom to systems that primarily perform statistical pattern-matching, optimization, and symbol manipulation.

It parallels concepts like "credibility excess" in epistemic injustice literature (where something receives undue trust) but applies to the field and its marketing.

Current AI (especially large language models and neural networks) excels at simulating intelligent outputs—fluent text, code, predictions, or classifications—through massive data compression and probabilistic inference. It does not possess understanding, consciousness, intentionality, or reliable truth-tracking in the human epistemic sense.

Calling it "intelligence" imports anthropomorphic assumptions: agency, insight, creativity, or moral reasoning. This creates excess by inflating expectations and blurring lines between tool and https://t.co/dAhcERJqLo

Evidence of Epistemic Excess in AI
Hype and Anthropomorphism: Marketing and media routinely describe AI as "thinking," "understanding," "hallucinating," or "creative." This is both hype (exaggerating capabilities) and a fallacy (attributing human traits to non-agents). It leads to misplaced trust, over-reliance, and policy https://t.co/KwMntqoHbS +1

Epistemic Risks: AI can degrade human epistemic agency by flooding information environments with plausible but unverified content ("AI slop"), creating verification challenges, or amplifying biases. Users may defer to outputs without critical scrutiny, treating probabilistic correlations as authoritative knowledge.

Historical and Technical Reality: The term "Artificial Intelligence" dates to the 1956 Dartmouth workshop, reflecting aspirational goals of simulating human cognition.

Much of the field has always been computational: search algorithms, logic programming, statistical learning, and optimization. "Intelligence" was motivational rhetoric; today's dominant paradigms (transformers, gradient descent) are sophisticated engineering, not breakthroughs in understanding minds.

This excess fosters illusions: that scaling will magically yield AGI with human-like (or superior) wisdom, or that current systems are reliable epistemic authorities rather than impressive autocomplete https://t.co/0ecAEab5Jb for Renaming to "Computational Logic""Computational Logic" already exists as a subfield (logic from a CS perspective: automated theorem proving, logic programming, formal methods, knowledge representation).en.wikipedia.org

Broadening or adopting it (or "Computational Intelligence," though that still carries baggage) for the wider endeavor has merits:Precision: It emphasizes what the systems do—compute, apply logical and statistical rules, process symbols and data. No implication of mindedness.

Reduces Misleading Expectations: Avoids anthropomorphism that fuels both utopian hype ("AI will solve everything") and dystopian panic ("AI will take over"). It frames systems as powerful tools for inference, search, and simulation.

Honesty: Aligns with critiques of the term "AI" for setting up disappointment or over-trust. Subfields like machine learning already signal engineering roots; a unifying rename could clarify the field's engineering/philosophical hybrid nature.

Philosophical Grounding: It reconnects to symbolic AI traditions (logicist approaches) while encompassing statistical methods as probabilistic logic. It avoids the "intelligence" metaphor that invites category errors (e.g., confusing fluency with comprehension).
Historical precedent exists: early AI overlapped heavily with computational logic (e.g., Edinburgh's Department of Computational Logic within AI).en.wikipedia.orgCounterpoints and LimitationsHistorical Inertia and Utility: "AI" is entrenched, useful for broad goals (behavior that appears intelligent), and inspires research. Narrower terms like ML or "statistical modeling" already exist for specifics. Renaming might confuse the public or dilute branding.
Aspirational Value: The field has produced genuine advances in reasoning (e.g., AlphaGo, theorem provers, planning systems). Dismissing "intelligence" entirely ignores narrow but real achievements in task performance.
Not Pure Logic: Modern AI relies heavily on subsymbolic, connectionist methods (neural nets) that aren't strictly "logic" in the deductive sense. "Computational Statistics" or "Applied Machine Intelligence" might fit better for some branches.

Epistemic Issues Persist Regardless: Renaming wouldn't eliminate biases, slop, or over-reliance. Better interfaces, transparency, and education matter https://t.co/Ax2PiC6fyO

#renaming #AI

[Chamath @startupxfactor]

@_The_Prophet__ https://t.co/BuE5FKXkOB

[Chamath @startupxfactor]

Can we identify Artificial Intelligence as a form of epistemic excess and advocate for its renaming to 'Computational Logic.' Discuss. The answer from Grok's mouth:

Yes, one can plausibly frame modern "Artificial Intelligence" as a form of epistemic excess — an overreach in how we conceptualize, name, and attribute knowledge-generating capabilities to computational systems.

Renaming it to something like "Computational Logic" (or similar grounded terms) could serve as a corrective, stripping away anthropomorphic hype while accurately describing what the technology actually does. This isn't a fringe linguistic tweak; it's a call for epistemic humility in an era of inflated claims.

AI represents real engineering progress but embodies epistemic excess in public discourse and some research framing. It promises epistemic augmentation (better search, summarization, discovery) while risking dilution of human judgment through over-deference. A rename to something like "Advanced Computational Systems" or "Computational Logic and Inference" would ground expectations without losing excitement for the tech.

This shift encourages viewing these systems as powerful extensions of human tools—logic engines on silicon—rather than ersatz minds. It promotes epistemic humility: use them rigorously, verify outputs, and retain human responsibility for knowledge claims.

In an era of generative models, clearer language isn't pedantry; it's a safeguard for truth-seeking. Artificial Intelligence can indeed be viewed as a form of "epistemic excess," and renaming it something like "Computational Logic" (or a close variant) would promote greater precision, reduce hype, and better align terminology with technical reality.

Epistemology concerns knowledge, justification, belief, and rational inquiry. "Epistemic excess" refers to over-attributing capacities for genuine understanding, reasoning, or wisdom to systems that primarily perform statistical pattern-matching, optimization, and symbol manipulation.

It parallels concepts like "credibility excess" in epistemic injustice literature (where something receives undue trust) but applies to the field and its marketing.

Current AI (especially large language models and neural networks) excels at simulating intelligent outputs—fluent text, code, predictions, or classifications—through massive data compression and probabilistic inference. It does not possess understanding, consciousness, intentionality, or reliable truth-tracking in the human epistemic sense.

Calling it "intelligence" imports anthropomorphic assumptions: agency, insight, creativity, or moral reasoning. This creates excess by inflating expectations and blurring lines between tool and https://t.co/dAhcERJqLo

Evidence of Epistemic Excess in AI
Hype and Anthropomorphism: Marketing and media routinely describe AI as "thinking," "understanding," "hallucinating," or "creative." This is both hype (exaggerating capabilities) and a fallacy (attributing human traits to non-agents). It leads to misplaced trust, over-reliance, and policy https://t.co/KwMntqoHbS +1

Epistemic Risks: AI can degrade human epistemic agency by flooding information environments with plausible but unverified content ("AI slop"), creating verification challenges, or amplifying biases. Users may defer to outputs without critical scrutiny, treating probabilistic correlations as authoritative knowledge.

Historical and Technical Reality: The term "Artificial Intelligence" dates to the 1956 Dartmouth workshop, reflecting aspirational goals of simulating human cognition.

Much of the field has always been computational: search algorithms, logic programming, statistical learning, and optimization. "Intelligence" was motivational rhetoric; today's dominant paradigms (transformers, gradient descent) are sophisticated engineering, not breakthroughs in understanding minds.

This excess fosters illusions: that scaling will magically yield AGI with human-like (or superior) wisdom, or that current systems are reliable epistemic authorities rather than impressive autocomplete https://t.co/0ecAEab5Jb for Renaming to "Computational Logic""Computational Logic" already exists as a subfield (logic from a CS perspective: automated theorem proving, logic programming, formal methods, knowledge representation).en.wikipedia.org

Broadening or adopting it (or "Computational Intelligence," though that still carries baggage) for the wider endeavor has merits:Precision: It emphasizes what the systems do—compute, apply logical and statistical rules, process symbols and data. No implication of mindedness.

Reduces Misleading Expectations: Avoids anthropomorphism that fuels both utopian hype ("AI will solve everything") and dystopian panic ("AI will take over"). It frames systems as powerful tools for inference, search, and simulation.

Honesty: Aligns with critiques of the term "AI" for setting up disappointment or over-trust. Subfields like machine learning already signal engineering roots; a unifying rename could clarify the field's engineering/philosophical hybrid nature.

Philosophical Grounding: It reconnects to symbolic AI traditions (logicist approaches) while encompassing statistical methods as probabilistic logic. It avoids the "intelligence" metaphor that invites category errors (e.g., confusing fluency with comprehension).
Historical precedent exists: early AI overlapped heavily with computational logic (e.g., Edinburgh's Department of Computational Logic within AI).en.wikipedia.orgCounterpoints and LimitationsHistorical Inertia and Utility: "AI" is entrenched, useful for broad goals (behavior that appears intelligent), and inspires research. Narrower terms like ML or "statistical modeling" already exist for specifics. Renaming might confuse the public or dilute branding.
Aspirational Value: The field has produced genuine advances in reasoning (e.g., AlphaGo, theorem provers, planning systems). Dismissing "intelligence" entirely ignores narrow but real achievements in task performance.
Not Pure Logic: Modern AI relies heavily on subsymbolic, connectionist methods (neural nets) that aren't strictly "logic" in the deductive sense. "Computational Statistics" or "Applied Machine Intelligence" might fit better for some branches.

Epistemic Issues Persist Regardless: Renaming wouldn't eliminate biases, slop, or over-reliance. Better interfaces, transparency, and education matter https://t.co/Ax2PiC6fyO

#renaming #AI

[Chamath @startupxfactor]

@JuliaEMcCoy https://t.co/BuE5FKXkOB

[Chamath @startupxfactor]

Can we identify Artificial Intelligence as a form of epistemic excess and advocate for its renaming to 'Computational Logic.' Discuss. The answer from Grok's mouth:

Yes, one can plausibly frame modern "Artificial Intelligence" as a form of epistemic excess — an overreach in how we conceptualize, name, and attribute knowledge-generating capabilities to computational systems.

Renaming it to something like "Computational Logic" (or similar grounded terms) could serve as a corrective, stripping away anthropomorphic hype while accurately describing what the technology actually does. This isn't a fringe linguistic tweak; it's a call for epistemic humility in an era of inflated claims.

AI represents real engineering progress but embodies epistemic excess in public discourse and some research framing. It promises epistemic augmentation (better search, summarization, discovery) while risking dilution of human judgment through over-deference. A rename to something like "Advanced Computational Systems" or "Computational Logic and Inference" would ground expectations without losing excitement for the tech.

This shift encourages viewing these systems as powerful extensions of human tools—logic engines on silicon—rather than ersatz minds. It promotes epistemic humility: use them rigorously, verify outputs, and retain human responsibility for knowledge claims.

In an era of generative models, clearer language isn't pedantry; it's a safeguard for truth-seeking. Artificial Intelligence can indeed be viewed as a form of "epistemic excess," and renaming it something like "Computational Logic" (or a close variant) would promote greater precision, reduce hype, and better align terminology with technical reality.

Epistemology concerns knowledge, justification, belief, and rational inquiry. "Epistemic excess" refers to over-attributing capacities for genuine understanding, reasoning, or wisdom to systems that primarily perform statistical pattern-matching, optimization, and symbol manipulation.

It parallels concepts like "credibility excess" in epistemic injustice literature (where something receives undue trust) but applies to the field and its marketing.

Current AI (especially large language models and neural networks) excels at simulating intelligent outputs—fluent text, code, predictions, or classifications—through massive data compression and probabilistic inference. It does not possess understanding, consciousness, intentionality, or reliable truth-tracking in the human epistemic sense.

Calling it "intelligence" imports anthropomorphic assumptions: agency, insight, creativity, or moral reasoning. This creates excess by inflating expectations and blurring lines between tool and https://t.co/dAhcERJqLo

Evidence of Epistemic Excess in AI
Hype and Anthropomorphism: Marketing and media routinely describe AI as "thinking," "understanding," "hallucinating," or "creative." This is both hype (exaggerating capabilities) and a fallacy (attributing human traits to non-agents). It leads to misplaced trust, over-reliance, and policy https://t.co/KwMntqoHbS +1

Epistemic Risks: AI can degrade human epistemic agency by flooding information environments with plausible but unverified content ("AI slop"), creating verification challenges, or amplifying biases. Users may defer to outputs without critical scrutiny, treating probabilistic correlations as authoritative knowledge.

Historical and Technical Reality: The term "Artificial Intelligence" dates to the 1956 Dartmouth workshop, reflecting aspirational goals of simulating human cognition.

Much of the field has always been computational: search algorithms, logic programming, statistical learning, and optimization. "Intelligence" was motivational rhetoric; today's dominant paradigms (transformers, gradient descent) are sophisticated engineering, not breakthroughs in understanding minds.

This excess fosters illusions: that scaling will magically yield AGI with human-like (or superior) wisdom, or that current systems are reliable epistemic authorities rather than impressive autocomplete https://t.co/0ecAEab5Jb for Renaming to "Computational Logic""Computational Logic" already exists as a subfield (logic from a CS perspective: automated theorem proving, logic programming, formal methods, knowledge representation).en.wikipedia.org

Broadening or adopting it (or "Computational Intelligence," though that still carries baggage) for the wider endeavor has merits:Precision: It emphasizes what the systems do—compute, apply logical and statistical rules, process symbols and data. No implication of mindedness.

Reduces Misleading Expectations: Avoids anthropomorphism that fuels both utopian hype ("AI will solve everything") and dystopian panic ("AI will take over"). It frames systems as powerful tools for inference, search, and simulation.

Honesty: Aligns with critiques of the term "AI" for setting up disappointment or over-trust. Subfields like machine learning already signal engineering roots; a unifying rename could clarify the field's engineering/philosophical hybrid nature.

Philosophical Grounding: It reconnects to symbolic AI traditions (logicist approaches) while encompassing statistical methods as probabilistic logic. It avoids the "intelligence" metaphor that invites category errors (e.g., confusing fluency with comprehension).
Historical precedent exists: early AI overlapped heavily with computational logic (e.g., Edinburgh's Department of Computational Logic within AI).en.wikipedia.orgCounterpoints and LimitationsHistorical Inertia and Utility: "AI" is entrenched, useful for broad goals (behavior that appears intelligent), and inspires research. Narrower terms like ML or "statistical modeling" already exist for specifics. Renaming might confuse the public or dilute branding.
Aspirational Value: The field has produced genuine advances in reasoning (e.g., AlphaGo, theorem provers, planning systems). Dismissing "intelligence" entirely ignores narrow but real achievements in task performance.
Not Pure Logic: Modern AI relies heavily on subsymbolic, connectionist methods (neural nets) that aren't strictly "logic" in the deductive sense. "Computational Statistics" or "Applied Machine Intelligence" might fit better for some branches.

Epistemic Issues Persist Regardless: Renaming wouldn't eliminate biases, slop, or over-reliance. Better interfaces, transparency, and education matter https://t.co/Ax2PiC6fyO

#renaming #AI

[Chamath @startupxfactor]

@Jason https://t.co/BuE5FKXkOB

[Chamath @startupxfactor]

Can we identify Artificial Intelligence as a form of epistemic excess and advocate for its renaming to 'Computational Logic.' Discuss. The answer from Grok's mouth:

Yes, one can plausibly frame modern "Artificial Intelligence" as a form of epistemic excess — an overreach in how we conceptualize, name, and attribute knowledge-generating capabilities to computational systems.

Renaming it to something like "Computational Logic" (or similar grounded terms) could serve as a corrective, stripping away anthropomorphic hype while accurately describing what the technology actually does. This isn't a fringe linguistic tweak; it's a call for epistemic humility in an era of inflated claims.

AI represents real engineering progress but embodies epistemic excess in public discourse and some research framing. It promises epistemic augmentation (better search, summarization, discovery) while risking dilution of human judgment through over-deference. A rename to something like "Advanced Computational Systems" or "Computational Logic and Inference" would ground expectations without losing excitement for the tech.

This shift encourages viewing these systems as powerful extensions of human tools—logic engines on silicon—rather than ersatz minds. It promotes epistemic humility: use them rigorously, verify outputs, and retain human responsibility for knowledge claims.

In an era of generative models, clearer language isn't pedantry; it's a safeguard for truth-seeking. Artificial Intelligence can indeed be viewed as a form of "epistemic excess," and renaming it something like "Computational Logic" (or a close variant) would promote greater precision, reduce hype, and better align terminology with technical reality.

Epistemology concerns knowledge, justification, belief, and rational inquiry. "Epistemic excess" refers to over-attributing capacities for genuine understanding, reasoning, or wisdom to systems that primarily perform statistical pattern-matching, optimization, and symbol manipulation.

It parallels concepts like "credibility excess" in epistemic injustice literature (where something receives undue trust) but applies to the field and its marketing.

Current AI (especially large language models and neural networks) excels at simulating intelligent outputs—fluent text, code, predictions, or classifications—through massive data compression and probabilistic inference. It does not possess understanding, consciousness, intentionality, or reliable truth-tracking in the human epistemic sense.

Calling it "intelligence" imports anthropomorphic assumptions: agency, insight, creativity, or moral reasoning. This creates excess by inflating expectations and blurring lines between tool and https://t.co/dAhcERJqLo

Evidence of Epistemic Excess in AI
Hype and Anthropomorphism: Marketing and media routinely describe AI as "thinking," "understanding," "hallucinating," or "creative." This is both hype (exaggerating capabilities) and a fallacy (attributing human traits to non-agents). It leads to misplaced trust, over-reliance, and policy https://t.co/KwMntqoHbS +1

Epistemic Risks: AI can degrade human epistemic agency by flooding information environments with plausible but unverified content ("AI slop"), creating verification challenges, or amplifying biases. Users may defer to outputs without critical scrutiny, treating probabilistic correlations as authoritative knowledge.

Historical and Technical Reality: The term "Artificial Intelligence" dates to the 1956 Dartmouth workshop, reflecting aspirational goals of simulating human cognition.

Much of the field has always been computational: search algorithms, logic programming, statistical learning, and optimization. "Intelligence" was motivational rhetoric; today's dominant paradigms (transformers, gradient descent) are sophisticated engineering, not breakthroughs in understanding minds.

This excess fosters illusions: that scaling will magically yield AGI with human-like (or superior) wisdom, or that current systems are reliable epistemic authorities rather than impressive autocomplete https://t.co/0ecAEab5Jb for Renaming to "Computational Logic""Computational Logic" already exists as a subfield (logic from a CS perspective: automated theorem proving, logic programming, formal methods, knowledge representation).en.wikipedia.org

Broadening or adopting it (or "Computational Intelligence," though that still carries baggage) for the wider endeavor has merits:Precision: It emphasizes what the systems do—compute, apply logical and statistical rules, process symbols and data. No implication of mindedness.

Reduces Misleading Expectations: Avoids anthropomorphism that fuels both utopian hype ("AI will solve everything") and dystopian panic ("AI will take over"). It frames systems as powerful tools for inference, search, and simulation.

Honesty: Aligns with critiques of the term "AI" for setting up disappointment or over-trust. Subfields like machine learning already signal engineering roots; a unifying rename could clarify the field's engineering/philosophical hybrid nature.

Philosophical Grounding: It reconnects to symbolic AI traditions (logicist approaches) while encompassing statistical methods as probabilistic logic. It avoids the "intelligence" metaphor that invites category errors (e.g., confusing fluency with comprehension).
Historical precedent exists: early AI overlapped heavily with computational logic (e.g., Edinburgh's Department of Computational Logic within AI).en.wikipedia.orgCounterpoints and LimitationsHistorical Inertia and Utility: "AI" is entrenched, useful for broad goals (behavior that appears intelligent), and inspires research. Narrower terms like ML or "statistical modeling" already exist for specifics. Renaming might confuse the public or dilute branding.
Aspirational Value: The field has produced genuine advances in reasoning (e.g., AlphaGo, theorem provers, planning systems). Dismissing "intelligence" entirely ignores narrow but real achievements in task performance.
Not Pure Logic: Modern AI relies heavily on subsymbolic, connectionist methods (neural nets) that aren't strictly "logic" in the deductive sense. "Computational Statistics" or "Applied Machine Intelligence" might fit better for some branches.

Epistemic Issues Persist Regardless: Renaming wouldn't eliminate biases, slop, or over-reliance. Better interfaces, transparency, and education matter https://t.co/Ax2PiC6fyO

#renaming #AI

[Chamath @startupxfactor]

@jlippincott https://t.co/BuE5FKXkOB

[Chamath @startupxfactor]

Can we identify Artificial Intelligence as a form of epistemic excess and advocate for its renaming to 'Computational Logic.' Discuss. The answer from Grok's mouth:

Yes, one can plausibly frame modern "Artificial Intelligence" as a form of epistemic excess — an overreach in how we conceptualize, name, and attribute knowledge-generating capabilities to computational systems.

Renaming it to something like "Computational Logic" (or similar grounded terms) could serve as a corrective, stripping away anthropomorphic hype while accurately describing what the technology actually does. This isn't a fringe linguistic tweak; it's a call for epistemic humility in an era of inflated claims.

AI represents real engineering progress but embodies epistemic excess in public discourse and some research framing. It promises epistemic augmentation (better search, summarization, discovery) while risking dilution of human judgment through over-deference. A rename to something like "Advanced Computational Systems" or "Computational Logic and Inference" would ground expectations without losing excitement for the tech.

This shift encourages viewing these systems as powerful extensions of human tools—logic engines on silicon—rather than ersatz minds. It promotes epistemic humility: use them rigorously, verify outputs, and retain human responsibility for knowledge claims.

In an era of generative models, clearer language isn't pedantry; it's a safeguard for truth-seeking. Artificial Intelligence can indeed be viewed as a form of "epistemic excess," and renaming it something like "Computational Logic" (or a close variant) would promote greater precision, reduce hype, and better align terminology with technical reality.

Epistemology concerns knowledge, justification, belief, and rational inquiry. "Epistemic excess" refers to over-attributing capacities for genuine understanding, reasoning, or wisdom to systems that primarily perform statistical pattern-matching, optimization, and symbol manipulation.

It parallels concepts like "credibility excess" in epistemic injustice literature (where something receives undue trust) but applies to the field and its marketing.

Current AI (especially large language models and neural networks) excels at simulating intelligent outputs—fluent text, code, predictions, or classifications—through massive data compression and probabilistic inference. It does not possess understanding, consciousness, intentionality, or reliable truth-tracking in the human epistemic sense.

Calling it "intelligence" imports anthropomorphic assumptions: agency, insight, creativity, or moral reasoning. This creates excess by inflating expectations and blurring lines between tool and https://t.co/dAhcERJqLo

Evidence of Epistemic Excess in AI
Hype and Anthropomorphism: Marketing and media routinely describe AI as "thinking," "understanding," "hallucinating," or "creative." This is both hype (exaggerating capabilities) and a fallacy (attributing human traits to non-agents). It leads to misplaced trust, over-reliance, and policy https://t.co/KwMntqoHbS +1

Epistemic Risks: AI can degrade human epistemic agency by flooding information environments with plausible but unverified content ("AI slop"), creating verification challenges, or amplifying biases. Users may defer to outputs without critical scrutiny, treating probabilistic correlations as authoritative knowledge.

Historical and Technical Reality: The term "Artificial Intelligence" dates to the 1956 Dartmouth workshop, reflecting aspirational goals of simulating human cognition.

Much of the field has always been computational: search algorithms, logic programming, statistical learning, and optimization. "Intelligence" was motivational rhetoric; today's dominant paradigms (transformers, gradient descent) are sophisticated engineering, not breakthroughs in understanding minds.

This excess fosters illusions: that scaling will magically yield AGI with human-like (or superior) wisdom, or that current systems are reliable epistemic authorities rather than impressive autocomplete https://t.co/0ecAEab5Jb for Renaming to "Computational Logic""Computational Logic" already exists as a subfield (logic from a CS perspective: automated theorem proving, logic programming, formal methods, knowledge representation).en.wikipedia.org

Broadening or adopting it (or "Computational Intelligence," though that still carries baggage) for the wider endeavor has merits:Precision: It emphasizes what the systems do—compute, apply logical and statistical rules, process symbols and data. No implication of mindedness.

Reduces Misleading Expectations: Avoids anthropomorphism that fuels both utopian hype ("AI will solve everything") and dystopian panic ("AI will take over"). It frames systems as powerful tools for inference, search, and simulation.

Honesty: Aligns with critiques of the term "AI" for setting up disappointment or over-trust. Subfields like machine learning already signal engineering roots; a unifying rename could clarify the field's engineering/philosophical hybrid nature.

Philosophical Grounding: It reconnects to symbolic AI traditions (logicist approaches) while encompassing statistical methods as probabilistic logic. It avoids the "intelligence" metaphor that invites category errors (e.g., confusing fluency with comprehension).
Historical precedent exists: early AI overlapped heavily with computational logic (e.g., Edinburgh's Department of Computational Logic within AI).en.wikipedia.orgCounterpoints and LimitationsHistorical Inertia and Utility: "AI" is entrenched, useful for broad goals (behavior that appears intelligent), and inspires research. Narrower terms like ML or "statistical modeling" already exist for specifics. Renaming might confuse the public or dilute branding.
Aspirational Value: The field has produced genuine advances in reasoning (e.g., AlphaGo, theorem provers, planning systems). Dismissing "intelligence" entirely ignores narrow but real achievements in task performance.
Not Pure Logic: Modern AI relies heavily on subsymbolic, connectionist methods (neural nets) that aren't strictly "logic" in the deductive sense. "Computational Statistics" or "Applied Machine Intelligence" might fit better for some branches.

Epistemic Issues Persist Regardless: Renaming wouldn't eliminate biases, slop, or over-reliance. Better interfaces, transparency, and education matter https://t.co/Ax2PiC6fyO

#renaming #AI

[Chamath @startupxfactor]

@realBigBrainAI https://t.co/BuE5FKXkOB

[Chamath @startupxfactor]

Can we identify Artificial Intelligence as a form of epistemic excess and advocate for its renaming to 'Computational Logic.' Discuss. The answer from Grok's mouth:

Yes, one can plausibly frame modern "Artificial Intelligence" as a form of epistemic excess — an overreach in how we conceptualize, name, and attribute knowledge-generating capabilities to computational systems.

Renaming it to something like "Computational Logic" (or similar grounded terms) could serve as a corrective, stripping away anthropomorphic hype while accurately describing what the technology actually does. This isn't a fringe linguistic tweak; it's a call for epistemic humility in an era of inflated claims.

AI represents real engineering progress but embodies epistemic excess in public discourse and some research framing. It promises epistemic augmentation (better search, summarization, discovery) while risking dilution of human judgment through over-deference. A rename to something like "Advanced Computational Systems" or "Computational Logic and Inference" would ground expectations without losing excitement for the tech.

This shift encourages viewing these systems as powerful extensions of human tools—logic engines on silicon—rather than ersatz minds. It promotes epistemic humility: use them rigorously, verify outputs, and retain human responsibility for knowledge claims.

In an era of generative models, clearer language isn't pedantry; it's a safeguard for truth-seeking. Artificial Intelligence can indeed be viewed as a form of "epistemic excess," and renaming it something like "Computational Logic" (or a close variant) would promote greater precision, reduce hype, and better align terminology with technical reality.

Epistemology concerns knowledge, justification, belief, and rational inquiry. "Epistemic excess" refers to over-attributing capacities for genuine understanding, reasoning, or wisdom to systems that primarily perform statistical pattern-matching, optimization, and symbol manipulation.

It parallels concepts like "credibility excess" in epistemic injustice literature (where something receives undue trust) but applies to the field and its marketing.

Current AI (especially large language models and neural networks) excels at simulating intelligent outputs—fluent text, code, predictions, or classifications—through massive data compression and probabilistic inference. It does not possess understanding, consciousness, intentionality, or reliable truth-tracking in the human epistemic sense.

Calling it "intelligence" imports anthropomorphic assumptions: agency, insight, creativity, or moral reasoning. This creates excess by inflating expectations and blurring lines between tool and https://t.co/dAhcERJqLo

Evidence of Epistemic Excess in AI
Hype and Anthropomorphism: Marketing and media routinely describe AI as "thinking," "understanding," "hallucinating," or "creative." This is both hype (exaggerating capabilities) and a fallacy (attributing human traits to non-agents). It leads to misplaced trust, over-reliance, and policy https://t.co/KwMntqoHbS +1

Epistemic Risks: AI can degrade human epistemic agency by flooding information environments with plausible but unverified content ("AI slop"), creating verification challenges, or amplifying biases. Users may defer to outputs without critical scrutiny, treating probabilistic correlations as authoritative knowledge.

Historical and Technical Reality: The term "Artificial Intelligence" dates to the 1956 Dartmouth workshop, reflecting aspirational goals of simulating human cognition.

Much of the field has always been computational: search algorithms, logic programming, statistical learning, and optimization. "Intelligence" was motivational rhetoric; today's dominant paradigms (transformers, gradient descent) are sophisticated engineering, not breakthroughs in understanding minds.

This excess fosters illusions: that scaling will magically yield AGI with human-like (or superior) wisdom, or that current systems are reliable epistemic authorities rather than impressive autocomplete https://t.co/0ecAEab5Jb for Renaming to "Computational Logic""Computational Logic" already exists as a subfield (logic from a CS perspective: automated theorem proving, logic programming, formal methods, knowledge representation).en.wikipedia.org

Broadening or adopting it (or "Computational Intelligence," though that still carries baggage) for the wider endeavor has merits:Precision: It emphasizes what the systems do—compute, apply logical and statistical rules, process symbols and data. No implication of mindedness.

Reduces Misleading Expectations: Avoids anthropomorphism that fuels both utopian hype ("AI will solve everything") and dystopian panic ("AI will take over"). It frames systems as powerful tools for inference, search, and simulation.

Honesty: Aligns with critiques of the term "AI" for setting up disappointment or over-trust. Subfields like machine learning already signal engineering roots; a unifying rename could clarify the field's engineering/philosophical hybrid nature.

Philosophical Grounding: It reconnects to symbolic AI traditions (logicist approaches) while encompassing statistical methods as probabilistic logic. It avoids the "intelligence" metaphor that invites category errors (e.g., confusing fluency with comprehension).
Historical precedent exists: early AI overlapped heavily with computational logic (e.g., Edinburgh's Department of Computational Logic within AI).en.wikipedia.orgCounterpoints and LimitationsHistorical Inertia and Utility: "AI" is entrenched, useful for broad goals (behavior that appears intelligent), and inspires research. Narrower terms like ML or "statistical modeling" already exist for specifics. Renaming might confuse the public or dilute branding.
Aspirational Value: The field has produced genuine advances in reasoning (e.g., AlphaGo, theorem provers, planning systems). Dismissing "intelligence" entirely ignores narrow but real achievements in task performance.
Not Pure Logic: Modern AI relies heavily on subsymbolic, connectionist methods (neural nets) that aren't strictly "logic" in the deductive sense. "Computational Statistics" or "Applied Machine Intelligence" might fit better for some branches.

Epistemic Issues Persist Regardless: Renaming wouldn't eliminate biases, slop, or over-reliance. Better interfaces, transparency, and education matter https://t.co/Ax2PiC6fyO

#renaming #AI

[Chamath @startupxfactor]

@sopharicks https://t.co/BuE5FKXkOB

[Chamath @startupxfactor]

Can we identify Artificial Intelligence as a form of epistemic excess and advocate for its renaming to 'Computational Logic.' Discuss. The answer from Grok's mouth:

Yes, one can plausibly frame modern "Artificial Intelligence" as a form of epistemic excess — an overreach in how we conceptualize, name, and attribute knowledge-generating capabilities to computational systems.

Renaming it to something like "Computational Logic" (or similar grounded terms) could serve as a corrective, stripping away anthropomorphic hype while accurately describing what the technology actually does. This isn't a fringe linguistic tweak; it's a call for epistemic humility in an era of inflated claims.

AI represents real engineering progress but embodies epistemic excess in public discourse and some research framing. It promises epistemic augmentation (better search, summarization, discovery) while risking dilution of human judgment through over-deference. A rename to something like "Advanced Computational Systems" or "Computational Logic and Inference" would ground expectations without losing excitement for the tech.

This shift encourages viewing these systems as powerful extensions of human tools—logic engines on silicon—rather than ersatz minds. It promotes epistemic humility: use them rigorously, verify outputs, and retain human responsibility for knowledge claims.

In an era of generative models, clearer language isn't pedantry; it's a safeguard for truth-seeking. Artificial Intelligence can indeed be viewed as a form of "epistemic excess," and renaming it something like "Computational Logic" (or a close variant) would promote greater precision, reduce hype, and better align terminology with technical reality.

Epistemology concerns knowledge, justification, belief, and rational inquiry. "Epistemic excess" refers to over-attributing capacities for genuine understanding, reasoning, or wisdom to systems that primarily perform statistical pattern-matching, optimization, and symbol manipulation.

It parallels concepts like "credibility excess" in epistemic injustice literature (where something receives undue trust) but applies to the field and its marketing.

Current AI (especially large language models and neural networks) excels at simulating intelligent outputs—fluent text, code, predictions, or classifications—through massive data compression and probabilistic inference. It does not possess understanding, consciousness, intentionality, or reliable truth-tracking in the human epistemic sense.

Calling it "intelligence" imports anthropomorphic assumptions: agency, insight, creativity, or moral reasoning. This creates excess by inflating expectations and blurring lines between tool and https://t.co/dAhcERJqLo

Evidence of Epistemic Excess in AI
Hype and Anthropomorphism: Marketing and media routinely describe AI as "thinking," "understanding," "hallucinating," or "creative." This is both hype (exaggerating capabilities) and a fallacy (attributing human traits to non-agents). It leads to misplaced trust, over-reliance, and policy https://t.co/KwMntqoHbS +1

Epistemic Risks: AI can degrade human epistemic agency by flooding information environments with plausible but unverified content ("AI slop"), creating verification challenges, or amplifying biases. Users may defer to outputs without critical scrutiny, treating probabilistic correlations as authoritative knowledge.

Historical and Technical Reality: The term "Artificial Intelligence" dates to the 1956 Dartmouth workshop, reflecting aspirational goals of simulating human cognition.

Much of the field has always been computational: search algorithms, logic programming, statistical learning, and optimization. "Intelligence" was motivational rhetoric; today's dominant paradigms (transformers, gradient descent) are sophisticated engineering, not breakthroughs in understanding minds.

This excess fosters illusions: that scaling will magically yield AGI with human-like (or superior) wisdom, or that current systems are reliable epistemic authorities rather than impressive autocomplete https://t.co/0ecAEab5Jb for Renaming to "Computational Logic""Computational Logic" already exists as a subfield (logic from a CS perspective: automated theorem proving, logic programming, formal methods, knowledge representation).en.wikipedia.org

Broadening or adopting it (or "Computational Intelligence," though that still carries baggage) for the wider endeavor has merits:Precision: It emphasizes what the systems do—compute, apply logical and statistical rules, process symbols and data. No implication of mindedness.

Reduces Misleading Expectations: Avoids anthropomorphism that fuels both utopian hype ("AI will solve everything") and dystopian panic ("AI will take over"). It frames systems as powerful tools for inference, search, and simulation.

Honesty: Aligns with critiques of the term "AI" for setting up disappointment or over-trust. Subfields like machine learning already signal engineering roots; a unifying rename could clarify the field's engineering/philosophical hybrid nature.

Philosophical Grounding: It reconnects to symbolic AI traditions (logicist approaches) while encompassing statistical methods as probabilistic logic. It avoids the "intelligence" metaphor that invites category errors (e.g., confusing fluency with comprehension).
Historical precedent exists: early AI overlapped heavily with computational logic (e.g., Edinburgh's Department of Computational Logic within AI).en.wikipedia.orgCounterpoints and LimitationsHistorical Inertia and Utility: "AI" is entrenched, useful for broad goals (behavior that appears intelligent), and inspires research. Narrower terms like ML or "statistical modeling" already exist for specifics. Renaming might confuse the public or dilute branding.
Aspirational Value: The field has produced genuine advances in reasoning (e.g., AlphaGo, theorem provers, planning systems). Dismissing "intelligence" entirely ignores narrow but real achievements in task performance.
Not Pure Logic: Modern AI relies heavily on subsymbolic, connectionist methods (neural nets) that aren't strictly "logic" in the deductive sense. "Computational Statistics" or "Applied Machine Intelligence" might fit better for some branches.

Epistemic Issues Persist Regardless: Renaming wouldn't eliminate biases, slop, or over-reliance. Better interfaces, transparency, and education matter https://t.co/Ax2PiC6fyO

#renaming #AI