---
title: Philosophy, Ethics, and the Future of AI
module: Frontiers
moduleNumber: 6
lessonNumber: 7
order: 607
summary: >
  Two questions have shadowed the field since its founding: can machines act
  intelligently (weak AI), and can they really think (strong AI)? We work through
  Turing's objections and their rebuttals — the arguments from disability,
  mathematics, and informality — then the strong-AI debate: the mind-body problem,
  functionalism and the brain prosthesis, Searle's Chinese Room and the systems
  reply, and consciousness and qualia. The companion lesson turns from what AI can
  do to what it should, and closes the course.
topics: [Frontiers]
sources:
  - book: AIMA
    ref: "Ch. 26 — Philosophical Foundations; §26.1 Weak AI, §26.2 Strong AI"
---

Philosophers had been asking how minds work for two millennia before there was a
computer to make the question urgent. Now there is one, and the field it defines
inherits two distinct questions that are easy to confuse. The first is whether a
machine can _act as if_ it were intelligent — pass whatever behavioral test we
care to set. Philosophers call this claim **weak AI**. The second is whether a
machine that acts intelligently is _actually_ thinking, with a real, conscious
mind, rather than merely simulating one; this is **strong AI**.[^rn-open] Most of
the research in this course takes weak AI for granted and does not much care about
strong AI: as long as the [agent](/artificial-intelligence/foundations/intelligent-agents)
works, whether you call it a simulation of intelligence or the real thing changes
nothing about the code. But every builder of such systems should care about their
consequences, and that concern — ethics — is a third question that stands apart
from both.

This lesson and its companion together close the course. They step back from
algorithms to ask what they mean, what they risk, and where they might go. This
part takes up the two questions of what a machine _can_ be — whether it can act
intelligently, and whether it can really think.

> **Definition (Weak AI / Strong AI).** **Weak AI** is the hypothesis that
> machines could act as if they were intelligent. **Strong AI** is the stronger
> hypothesis that machines that do so are actually thinking — that they have real,
> conscious mental states, not just behavior that resembles thought.

The debate spans decades. Turing framed the behavioral question in
1950; the field organized around it at Dartmouth in 1956; Searle mounted the sharpest
attack on the machinery in 1980; and the empirical facts changed
when deep networks began to pass hard perceptual tests around 2012, with
conversational systems following a decade later. The philosophy did not resolve — it
acquired working examples.

$$
% caption: A conservative timeline of the ideas and systems that frame this lesson.
% Turing (1950) proposes the behavioral test; Dartmouth (1956) founds the field;
% Searle (1980) presses the Chinese Room; deep learning (2012) makes weak AI empirical;
% instruction-tuned language models (early 2020s) turn value alignment into engineering.
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$$

## Weak AI: can machines act intelligently?

The 1956 workshop that founded the field asserted that "every aspect of learning
or any other feature of intelligence can be so precisely described that a machine
can be made to simulate it."[^rn-weak] The field thus rested from the start on the
assumption that weak AI is possible. Whether it _is_ possible depends on the
definition. Under the definition used throughout this course — AI as the search
for the best agent program on a given architecture — weak AI is trivially possible
in principle: a digital architecture with $k$ bits of program storage admits
exactly $2^k$ agent programs, and finding the best one is a finite (if astronomical)
search.[^rn-weak] Philosophers deal with the theoretical, not the practical, so
the engineering answer is _yes_.

That answer is unsatisfying because the traditional question was posed differently:
not "can we maximize expected utility?" but "**can machines think?**" Edsger
Dijkstra remarked that the question of whether machines can think "is about as
relevant as the question of whether submarines can swim."[^rn-swim] Both are
questions about the usage of English words, not about the design or capabilities of
the artifact. Turing sidestepped the verbal dispute entirely: instead of asking
whether machines can think, ask whether a machine can pass a **behavioral test**.

> **Definition (Turing Test).** A behavioral test for machine intelligence: a
> program converses with a human interrogator over a text channel; it passes if it
> fools the interrogator into judging it human. Turing proposed that fooling 30% of
> interrogators in a five-minute conversation would be a reasonable benchmark, and
> conjectured a machine would do so by 2000 — a prediction that was wrong.[^rn-turing]

The setup removes every cue except the conversation itself. The interrogator sees
no face, hears no voice, and knows only that one hidden respondent is a person and
one is a program. The judgment is forced onto behavior alone, which is precisely
Turing's point: replace the metaphysical question "does it think?" with an
operational one anyone can score.

$$
% caption: The Turing Test (the imitation game). An interrogator C exchanges typed
% messages with two hidden respondents, a human A and a machine B, over identical
% text channels; C must decide which is which. The machine passes if it is
% misidentified as often as chance would allow.
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Turing anticipated nearly every objection to thinking machines that would be raised
in the following half-century, and examined them himself. Three are worth working
through, because each names a real intuition and each has a real rebuttal.

$$
% caption: The three classic objections to weak AI, with the form of Turing's
% rebuttal to each. None establishes an in-principle barrier to a machine acting
% intelligently.
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$$

### The argument from disability

The **argument from disability** claims "a machine can never do $X$," where Turing's
own list of $X$'s runs to: be kind, resourceful, beautiful, friendly, have
initiative, have a sense of humor, tell right from wrong, make mistakes, fall in
love, learn from experience, use words properly, be the subject of its own
thought, do something really new.[^rn-disability] In retrospect many of these are
easy. We are thoroughly familiar with computers that "make mistakes." Programs play
chess and checkers, inspect parts on assembly lines, steer cars, diagnose diseases,
and make small but genuine discoveries in mathematics and the sciences — each at the
level of a human expert.

The deeper point is that the objection confuses _behavior_ with the mental
processes behind it. As far back as 1955, Paul Meehl studied trained experts making
subjective judgments — predicting a student's success, or a criminal's recidivism —
and found that in 19 of 20 studies simple statistical procedures like linear
regression predicted better than the experts.[^rn-disability] Tasks that seem to
demand human insight often yield to a
[learning](/artificial-intelligence/learning/learning-from-examples) algorithm.
That doesn't show the machine _uses_ insight; it shows that one's first guess about
which tasks require insight is often wrong.

### The mathematical objection

Through the work of Turing (1936) and Gödel (1931), certain questions are known to
be unanswerable by particular formal systems. Gödel's incompleteness theorem, which
we met in the study of [logical inference](/artificial-intelligence/logic-and-planning/inference-and-resolution),
says that for any formal axiomatic system $F$ powerful enough to do arithmetic, one
can construct a **Gödel sentence** $G(F)$ with two properties: $G(F)$ is a sentence
of $F$ but cannot be proved within $F$, and if $F$ is consistent, then $G(F)$ is
true.[^rn-math] Philosophers such as J. R. Lucas argued that this makes machines
mentally inferior to humans: a machine is a formal system and so cannot establish
the truth of its own Gödel sentence, whereas a human supposedly can.

There are three problems with the claim.[^rn-math] First, Gödel's theorem applies
only to formal systems powerful enough to do arithmetic, which requires infinite
capacity; a physical computer is a finite system describable in propositional logic,
to which the theorem does not apply. Second, an agent should not be ashamed of a
sentence it cannot assert while others can. Consider:

> J. R. Lucas cannot consistently assert that this sentence is true.

If Lucas asserted it he would contradict himself, so he cannot — and hence it is
true. There is a true sentence Lucas cannot assert while other people (and machines)
can, and this does not make us think less of Lucas. The construction is the same
self-reference that generates the Gödel sentence: the argument that a human "sees" a
truth a formal system cannot prove works just as well with the human as the system.

$$
% caption: The self-reference trap that neutralizes the Lucas argument. Substituting
% "Lucas" for "the machine" turns the same construction against him: there is a true
% sentence he cannot consistently assert, exactly as a formal system cannot prove its
% own Godel sentence G(F). The alleged human advantage does not survive the swap.
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Third, and most important, there
is no evidence that humans are _immune_ to the very limitations they attribute to
machines. It is impossible to prove that humans are not subject to Gödel's theorem,
since any such proof would require formalizing the allegedly unformalizable human
talent and so refute itself. The appeal that "we must assume our own consistency"
fails on the facts: humans are known to be inconsistent, even in careful
mathematical thought. Alfred Kempe's 1879 "proof" of the four-color theorem was
accepted for eleven years before Percy Heawood found the flaw, and the theorem
stood unproved until 1977.

### The argument from informality

The most persistent criticism of AI as an enterprise is the **argument from
informality**: human behavior is far too complex to be captured by any set of
rules, and since computers do nothing but follow rules, they cannot behave as
intelligently as humans. The inability to capture everything in logical rules is
the **qualification problem** we met in [classical planning](/artificial-intelligence/logic-and-planning/classical-planning).[^rn-informal]
Its principal proponent, Hubert Dreyfus, aimed his critique at what John Haugeland
named **Good Old-Fashioned AI (GOFAI)** — the view that all intelligent behavior can
be captured by a system reasoning logically from facts and rules, corresponding to
the simplest logical agent.

Dreyfus is right that a purely logical agent is vulnerable to the qualification
problem. But the critique lands on one _particular_ way of programming, not on
computers as such. [Probabilistic reasoning](/artificial-intelligence/uncertainty/probability-and-bayes)
is far better suited to open-ended domains than rigid rule-lists, and machine
learning generates behavior no one wrote down as a rule.[^rn-informal] Under
Dreyfus's own view, human expertise is a "holistic context" or "background" — a
grandmaster does not deliberate over each move but sees the board as demanding a
certain response. That the process is not open to introspection does not mean it
does not exist. Dreyfus and Dreyfus later proposed a five-stage model of expertise
that ends in instantaneous, non-rule-based response — and in doing so proposed a
neural-network architecture, moving from AI critics to AI theorists.

One of Dreyfus's strongest points survives as an insight rather than an objection:
the case for **situated agents** over disembodied inference engines. An agent whose
grasp of "dog" comes only from sentences like $\text{Dog}(x) \Rightarrow
\text{Mammal}(x)$ is at a disadvantage to one that has watched dogs run, played
fetch, and been licked by one. This is the **embodied cognition** thesis: cognition
takes place within a body embedded in an environment, and the brain augments its
reasoning by referring to the world rather than modeling it exhaustively. Under this
view [perception and vision](/artificial-intelligence/frontiers/vision-and-perception)
and [robotics](/artificial-intelligence/frontiers/robotics) become central to
intelligence, not peripheral.

## Strong AI: can machines really think?

Grant that a machine passes every behavioral test. Many philosophers hold it would
still not be _actually_ thinking — only simulating thought. Turing foresaw this too,
citing Professor Geoffrey Jefferson's demand that a machine "write a sonnet or
compose a concerto because of thoughts and emotions felt, and not by the chance fall
of symbols." This is the **argument from consciousness**: the machine must be aware
of its own mental states.[^rn-strong] Related objections turn on **phenomenology**
(the machine must actually _feel_) and **intentionality** (whether the machine's
purported beliefs and desires are genuinely _about_ things in the world).

Turing's reply was not to prove machines conscious but to note that the question is
as ill-defined as "can machines think?" We never have direct evidence of the inner
mental states of _other humans_ either; in ordinary life we simply adopt "the polite
convention that everyone thinks." A future in which such conversations with machines
are commonplace might dissolve the distinction between real and artificial thinking,
just as the 1848 synthesis of artificial urea dissolved the supposed line between
organic and inorganic chemistry once chemists agreed the synthetic product _was_
urea, having all the same properties.[^rn-strong]

Against dissolving the question, one may insist there is a genuine _factual_ issue:
humans have real minds, and machines might or might not. Settling it requires
understanding how humans have minds at all — the **mind–body problem**.

> **Definition (Mind–body problem).** The question of how mental states — beliefs,
> sensations, intentions — relate to physical states of the brain. **Dualism** (after
> Descartes) holds mind and body are distinct substances; **physicalism** (monism)
> holds mental states _are_ physical states of the brain. Most modern philosophers
> are physicalists, a position that at least permits strong AI in principle.[^rn-mindbody]

The positions form a small tree. Dualism splits mind off as a separate substance and
is largely abandoned in modern philosophy of mind. Physicalism keeps everything
physical, and then divides on _what makes a mental state the state it is_.
Functionalism says it is the causal role — the pattern of inputs, outputs, and
internal transitions — which makes strong AI possible in principle, since a program can
reproduce that role on any substrate. Biological naturalism says it is the specific
physical stuff — the causal powers of actual neurons — which denies that reproducing
the role on silicon is enough. The whole strong-AI dispute lives on this one branch.

$$
% caption: A taxonomy of positions on the mind-body problem. The strong-AI question
% turns on the split within physicalism: functionalism (role fixes the mental state,
% so any isomorphic system has a mind) versus biological naturalism (the physical
% substrate's causal powers fix it, so silicon may not suffice).
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$$
% caption: Where the two hypotheses sit. Weak AI concerns behavior (left column);
% strong AI concerns inner experience (right column). Searle's Chinese Room grants
% the behavior and denies the understanding — the arrow that weak-AI research
% simply declines to draw.
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### The brain replacement experiment

If physicalism is right, the proper description of a mental state is _determined_ by
the brain state producing it. **Functionalism** sharpens this: a mental state is any
intermediate causal condition between input and output, so any two systems with
isomorphic causal processes have the same mental states — and a program could then
have the mental states of a person.[^rn-func] The claim is dramatized by the **brain
replacement experiment**: replace the neurons in a brain one at a time with
electronic devices that perfectly mimic their input–output behavior. By construction
the external behavior is unchanged. What happens to the inner experience?

$$
% caption: The brain replacement experiment. Neurons are swapped for functionally
% identical electronic units one at a time; each replacement preserves every
% input-output relation, so the outward behavior is invariant at every step. The
% functionalist expects experience to persist unchanged; the biological naturalist
% expects it to fade. Nothing in the behavior can distinguish the two predictions.
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Intuitions clash sharply. The functionalist (Moravec) is convinced consciousness
would persist; the biological naturalist (Searle) is convinced it would slowly
vanish — that you would find yourself losing control, hearing your voice say "I see a
red object" while your conscious experience shrank to nothing.[^rn-func] But if
awareness shrank while behavior stayed fixed, the shrinking would have to be removed
from behavior _instantaneously and totally_, which is an odd thing to happen from
gradual neuron-at-a-time replacement. And if we poke the electronic subject and it
says "ouch," that response must have an explanation appealing only to the functional
properties of the neurons — an explanation that must apply equally to the original
biological brain. Reducing consciousness to something with no causal connection to
behavior makes it **epiphenomenal**: a shadow that "the brain says 'ouch'" is not
caused by, which is deeply counterintuitive.

The dilemma is sharp and has only two horns. Either consciousness _does_ affect
behavior, in which case a functional duplicate that reproduces all the behavior must
reproduce the consciousness that helps cause it — and functionalism wins. Or
consciousness does _not_ affect behavior, in which case it is epiphenomenal, a
by-product that could be subtracted with no observable trace — and we are left unable
to explain why we ever talk about it, since our talking is caused by brain states, not
by the experience itself. The biological naturalist wants to deny both horns: to say
the electronic brain lacks the experience _and_ that this absence would eventually
show. The gradual-replacement construction is designed to make that combination hard
to hold, because it forces any behavioral difference to appear from a single
neuron-sized swap.

### Biological naturalism and the Chinese Room

Searle's **biological naturalism** holds that mental states are high-level features
_caused by_ low-level physical processes in the neurons, and that it is the
unspecified causal properties of those neurons that matter. Running the right program
on the wrong substrate is therefore not sufficient for a mind. His argument for this
is the **Chinese Room**.[^rn-chinese]

> **Definition (Chinese Room).** A thought experiment: a human who knows no Chinese
> sits in a room with a rule book (the program) and stacks of paper (the memory).
> Chinese sentences enter through a slot; the human mechanically looks up symbols and
> follows the rules, and fluent Chinese answers come out. The system passes the
> Turing Test in Chinese. Searle argues: the person does not understand Chinese, the
> paper and rules do not understand Chinese, so there is no understanding anywhere —
> hence running the right program does not generate understanding.

$$
% caption: The Chinese Room. Chinese input enters a slot; a person who knows no
% Chinese looks each symbol up in a rule book and manipulates paper memory to produce
% Chinese output. The behavior passes the Turing Test, yet, Searle argues, no
% component understands Chinese. The systems reply locates understanding in the whole
% room rather than the person inside it.
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$$

The most cited reply is the **systems reply**: asking whether the person understands
Chinese is like asking whether a CPU can take cube roots. In both cases the answer is
_no_, and in both cases the whole _system_ has the capacity in question. The person
is one component; understanding is a property of the person-plus-rulebook-plus-paper.
Searle's rejoinder — let the person memorize the rule book, so there is no external
system — trades on the intuition that a property of the whole must reside in one of
its parts. Yet water is wet though neither hydrogen nor oxygen is.

Traced as a chain, the exchange is a sequence of moves and counter-moves, each
answering the last, and the residue is an intuition standoff rather than a settled
verdict.

$$
% caption: The Chinese Room debate as a labeled argument trace. Each box answers the
% one before it; the final counter (a whole having a property none of its parts has)
% blocks Searle's rejoinder, leaving the dispute where it started: on the intuition
% that syntax cannot yield semantics.
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  \draw[->, thick] (b) -- (c);
  \draw[->, thick] (c) -- (d);
  \node[who] at (-3.7,3.6)  {Searle};
  \node[who] at (-3.7,1.8)  {reply};
  \node[who] at (-3.7,0)    {Searle};
  \node[who] at (-3.7,-1.8) {reply};
\end{tikzpicture}
$$

The four axioms make the shape of the disagreement explicit. Axioms 1, 2, and 4 are
widely granted; the whole argument rests on axiom 3, and axiom 3 is close to the
conclusion it is used to reach.

$$
% caption: The structure of Searle's argument. Axioms 1-2 and 4 are broadly accepted;
% axiom 3 (syntax is not sufficient for semantics) is the load-bearing premise and is
% essentially a denial of functionalism. From 1, 2, 3 comes "programs are not minds";
% adding 4 yields "an artificial mind needs brain-equivalent causal powers."
\begin{tikzpicture}[>=stealth, font=\footnotesize,
  ax/.style={draw, minimum width=42mm, minimum height=9mm, align=center, font=\scriptsize},
  con/.style={draw, minimum width=44mm, minimum height=10mm, align=center, font=\scriptsize}]
  \definecolor{acc}{HTML}{2348F2}
  \definecolor{red}{HTML}{C0392B}
  \node[ax] (a1) at (0,3.6)  {A1: programs are syntactic};
  \node[ax] (a2) at (0,2.4)  {A2: minds have semantics};
  \node[ax, draw=red, text=red, thick] (a3) at (0,1.2) {A3: syntax is not enough\\for semantics};
  \node[ax] (a4) at (0,-0.3) {A4: brains cause minds};
  \node[con, draw=acc, text=acc] (c1) at (7,2.4)  {programs are not\\suf\/f\/icient for minds};
  \node[con, draw=acc, text=acc] (c2) at (7,-0.3) {artif\/icial minds need\\brain-like causal powers};
  \draw[->, thick] (a1) -- (c1);
  \draw[->, thick] (a2) -- (c1);
  \draw[->, red, thick] (a3) -- (c1);
  \draw[->, thick] (c1) -- (c2);
  \draw[->, thick] (a4) -- (c2);
  \node[font=\scriptsize, text=red, anchor=west] at (2.3,0.5) {load-bearing};
\end{tikzpicture}
$$

Searle's real argument rests on four axioms: (1) programs are formal (syntactic);
(2) minds have mental contents (semantics); (3) syntax by itself is neither
constitutive of nor sufficient for semantics; (4) brains cause minds. From the first
three he concludes programs are not sufficient for minds; from the fourth, that any
artificial mind would need causal powers equivalent to those of brains.[^rn-chinese]
Axiom 3 is the load-bearing one, and it is essentially a denial of functionalism —
close to what it sets out to prove. Daniel Dennett called the Chinese Room an
**intuition pump**: it amplifies prior intuitions, so biological naturalists leave
more convinced and functionalists leave convinced only that axiom 3 is unsupported.
It has stirred up combatants without changing many minds. And even granting the
argument, it shows only that the room is not a mind _by virtue of running the
program_ — it says nothing about how to decide what _is_ a mind, leaving everyone
with only their intuitions.

### Consciousness, qualia, and the explanatory gap

Running through every debate about strong AI is **consciousness** — and within it the
part that resists all accounts, _subjective experience_: why it should _feel_ like
something to have certain brain states (tasting a strawberry) but presumably not
others (being a rock). The technical term for the intrinsic character of an
experience is **qualia**.[^rn-qualia]

> **Definition (Qualia).** The intrinsic, subjective qualities of conscious
> experience — "what it is like" to see red or feel pain. The **inverted spectrum**
> thought experiment supposes a person whose experience of red is another's
> experience of green: their behavior and causal organization are identical, yet
> their subjective experience differs — which is a problem for functionalism, since
> different qualia would then attach to isomorphic causal processes.

$$
% caption: The inverted-spectrum thought experiment. Two observers view the same red
% and green objects and use the words "red" and "green" identically, so their behavior
% and causal organization match exactly. Yet observer 2's inner experience of red is
% observer 1's inner experience of green. Identical function, different qualia: a
% problem for any theory that fixes experience by causal role alone.
\begin{tikzpicture}[>=stealth, font=\footnotesize,
  obs/.style={draw, minimum width=26mm, minimum height=10mm, align=center, font=\scriptsize},
  qbox/.style={draw, minimum width=20mm, minimum height=9mm, align=center, font=\scriptsize}]
  \definecolor{acc}{HTML}{2348F2}
  \node[qbox] (stim) at (0,0) {a ripe\\tomato};
  \node[obs] (o1) at (4.2,1.4)  {observer 1};
  \node[obs] (o2) at (4.2,-1.4) {observer 2};
  \node[qbox] (q1) at (8.6,1.4)  {feels RED\\inside};
  \node[qbox, draw=acc, text=acc] (q2) at (8.6,-1.4) {feels GREEN\\inside};
  \node[qbox] (say) at (12.6,0) {both say\\"red"};
  \draw[->, thick] (stim) -- (o1);
  \draw[->, thick] (stim) -- (o2);
  \draw[->, thick] (o1) -- (q1);
  \draw[->, acc, thick] (o2) -- (q2);
  \draw[->, thick] (q1) -- (say);
  \draw[->, thick] (q2) -- (say);
\end{tikzpicture}
$$

Qualia challenge not just functionalism but all of science. Suppose brain research
were _complete_ — every neural process mapped, every molecule accounted for. There is
no currently accepted form of reasoning that would carry us from such findings to any
conclusion about a subject's inner experience. This **explanatory gap** leads some
philosophers to hold that humans are simply incapable of understanding their own
consciousness; Dennett in particular closes the gap by denying qualia exist at all.
Turing conceded consciousness is a mystery but denied it need be solved before we
answer the practical question: he wanted programs that _behave_ intelligently, and
so do we. Making them conscious is a further project, and not one we are equipped
either to carry out or to verify.


Whether a machine can act intelligently, and whether it can really think, are the
questions of what AI _can_ do. They leave untouched the question of what it _should_
do — and once systems act competently in the world, that becomes the pressing one.
The ethics and risks of building these systems, the problem of keeping them aligned
with human values, and where the agent components could go from here are taken up in
the companion lesson,
[The Ethics and Future of AI](/artificial-intelligence/frontiers/ai-ethics-and-future),
which also closes the course by tying its six modules into one account of
intelligence as rational agency.

[^rn-open]: **AIMA**, Ch. 26 opening — the weak-AI hypothesis (machines could act _as if_ intelligent) versus the strong-AI hypothesis (such machines are _actually_ thinking); most researchers assume the former and are indifferent to the latter, but all should attend to the ethics.
[^rn-weak]: **AIMA**, §26.1 — the 1956 founding proposal (McCarthy et al., 1955) asserting intelligence can be precisely enough described to be simulated; and AI as the search for the best of the $2^k$ agent programs on a $k$-bit architecture.
[^rn-swim]: **AIMA**, §26.1 — Dijkstra (1984): "whether machines can think" is as relevant as "whether submarines can swim"; both concern the usage of words, not the artifact.
[^rn-turing]: **AIMA**, §26.1 — Turing (1950), "Computing Machinery and Intelligence": the behavioral test, the 30%-in-five-minutes benchmark, and the failed conjecture that a machine would pass by 2000.
[^rn-disability]: **AIMA**, §26.1.1 — the argument from disability and Turing's list of alleged $X$'s; and Meehl's finding (19 of 20 studies) that simple statistical predictors beat trained human experts.
[^rn-math]: **AIMA**, §26.1.2 — the mathematical objection: Gödel's incompleteness theorem, the Gödel sentence $G(F)$, Lucas's claim, and the three rebuttals (finite systems, the self-reference sentence, and no proof of human immunity; the four-color-theorem example of human inconsistency).
[^rn-informal]: **AIMA**, §26.1.3 — the argument from informality, the qualification problem, Dreyfus's critique of GOFAI, its answer in probabilistic reasoning and learning, and the shift to situated / embodied cognition.
[^rn-strong]: **AIMA**, §26.2 — the argument from consciousness (Jefferson), phenomenology and intentionality, Turing's "polite convention" reply, and the artificial-urea (1848) analogy for dissolving the real/artificial distinction.
[^rn-mindbody]: **AIMA**, §26.2 — the mind–body problem, Descartes's dualism, and physicalism (monism) as the modern default that permits strong AI in principle.
[^rn-func]: **AIMA**, §26.2.2 — functionalism (a mental state as a causal condition between input and output) and the brain replacement experiment, with the clashing Moravec / Searle intuitions and the epiphenomenalism dilemma.
[^rn-chinese]: **AIMA**, §26.2.3 — Searle's biological naturalism, the Chinese Room, the systems reply and Searle's rejoinder, the four axioms (with axiom 3 as the crux), and Dennett's "intuition pump."
[^rn-qualia]: **AIMA**, §26.2.4 — consciousness and subjective experience, qualia, the inverted-spectrum thought experiment, the explanatory gap, and Turing's concession that consciousness is a mystery but a separable one.