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## NAMES | People and treatment index

note: context is a concise, manuscript-grounded account of who the person is in this book and which view, work, quotation, or evaluation the book connects to them. Do not add biography or views beyond the record.
NAME-PREFACE-001: Kumagusu Minakata (南方熊楠) | chapter=CH-PREFACE | review=approved | aliases=南方熊楠
context: An independent naturalist (1867-1941) who opens the book. Without a university post he ranged across botany, folklore, anthropology, and religious studies, contributing some fifty pieces to Nature. In letters to the Shingon priest Doki Horyu he drew the "Minakata mandala": a web with no privileged center, every event tied to every other. The book borrows his "suiten" from this figure, recasting modern civilization as a human-centered mandala whose center AGI now destabilizes.
NAME-01-001: Plato | chapter=CH-01 | review=approved | aliases=プラトン
context: Placed at the outset as the origin of "what is it to know?", one of humanity's oldest philosophical questions. The philosopher who argued the distinction between knowledge and belief, he stands with Aristotle for a speculative lineage of more than two millennia — the backdrop against which the book marks its starting point: this question is now becoming a scientific problem, mathematically and empirically tractable as "intelligence."
NAME-01-002: Solomonoff | chapter=CH-01 | review=approved | aliases=ソロモノフ
context: Ray Solomonoff. In 1964 he formalized Solomonoff induction, a theory of prediction that makes Occam's razor — "the simpler explanation is more likely true" — mathematically rigorous by automatically selecting the most concise of all possible explanations. The book treats it as central: how LLM engineering practice approaches this ideal is one of its core questions, and the theory serves with scaling laws as a pillar of Proposition One on the feasibility of AGI.
NAME-01-003: Kurzweil | chapter=CH-01 | review=approved | aliases=カーツワイル
context: Ray Kurzweil. Cited for his prediction that computing power comparable to the human brain would become a cheap $1,000 commodity by 2029. The author's own forecast, held since around 2014, that AGI would arrive in 2029 plus or minus three years takes this cost-decline prediction as its starting point, adjusted for progress in algorithms and data.
NAME-01-004: MacAskill | chapter=CH-01 | review=approved | aliases=マカスキル
context: Presented as the representative of longtermism, which advocates maximizing expected value over the far future, and serves as a foil for situating the book's own position. The book distinguishes itself by questioning, from the standpoint of bounded rationality, the very reliability of long-range prediction, and by centering instead on designing robustness that does not depend on prediction — antifragility that grows stronger through fluctuation, and slack against optimization pressure.
NAME-01-005: Bostrom | chapter=CH-01 | review=approved | aliases=ボストロム
context: Named first among the prior works against which the book measures its own position, as the author of Superintelligence: Paths, Dangers, Strategies. Where that work systematized the risks of superintelligence in philosophical and futurological terms, this book differs in binding constraints derived from physical law and principles of institutional design together along a single axis.
NAME-01-006: Aristotle | chapter=CH-01 | review=approved | aliases=アリストテレス
context: Appears at the outset as the philosopher who defined reason as the essence of the human being. Together with Plato's distinction between knowledge and belief, he marks the origin of a lineage in which "what is it to know?" remained an object of speculation for over two millennia — the lineage whose transformation into a science of intelligence frames the book's introduction.
NAME-02-001: Kurzweil | chapter=CH-02 | review=approved | aliases=カーツワイル
context: Ray Kurzweil. Cited for his prediction that computing power comparable to the human brain would become a cheap $1,000 commodity by 2029. The author's own forecast, held since around 2014, that AGI would arrive in 2029 plus or minus three years takes this cost-decline prediction as its starting point, adjusted for progress in algorithms and data.
NAME-02-002: Chalmers | chapter=CH-02 | review=approved | aliases=チャーマーズ
context: Philosopher. Appears as the figure who in 1995 formulated the hard problem: why physical information processing is accompanied by subjective experience. The book treats the fact that this question remains unresolved in both neuroscience and philosophy as evidence that the strong AI / weak AI framework was built on an undecidable question, setting the stage for the AGI concept, which brackets the problem of consciousness.
NAME-02-003: Gubrud | chapter=CH-02 | review=approved | aliases=ギュブルード
context: Mark Gubrud, a physics researcher at the University of Maryland. Introduced as the person who first coined the term "Artificial General Intelligence" in a 1997 paper on the security implications of nanotechnology, in order to distinguish it from the expert systems of the time.
NAME-02-004: Shane Legg | chapter=CH-02 | review=approved | aliases=シェーン・レッグ
context: AI researcher. Around 2002 he used the term AGI and proposed it to Goertzel, playing a role in how the term took hold. He also appears at the core of the book's discussion of Universal AI as co-author, with Hutter, of the formal definition of intelligence as the expected ability to achieve goals across a wide range of environments, known as Legg-Hutter intelligence (LHI).
NAME-02-005: Goertzel | chapter=CH-02 | review=approved | aliases=ゲーツェル
context: AI researcher Ben Goertzel. He adopted the term AGI, proposed to him by Legg, as the title of a book he edited, publishing "Artificial General Intelligence" in 2007. He is introduced as the figure who, together with the launch of the international conference series bearing the AGI name, rapidly established the term.
NAME-02-006: I. J. Good | chapter=CH-02 | review=approved | aliases=I・J・グッド
context: Mathematician. Presented the concept of the "intelligence explosion" in 1965: if a machine with superhuman intelligence can improve itself, a chain reaction of rapidly increasing intelligence follows. He called the first such ultraintelligent machine humanity's "last invention." The book places him at the origin of singularity arguments, while noting that this lineage shares a common weakness: insufficient attention to the physical limits of intelligence.
NAME-02-007: Vinge | chapter=CH-02 | review=approved | aliases=ヴィンジ
context: Vernor Vinge, science fiction writer and mathematician. Proposed the concept of the "technological singularity" in 1993. The book cautions that while the singularity is often misunderstood as "the moment AI surpasses humans," Vinge's definition concerns not only relative intelligence but the speed of change: the point at which technological progress becomes too fast for humans to predict what lies beyond.
NAME-02-008: Chollet | chapter=CH-02 | review=approved | aliases=ショレ
context: Figure who argues that intelligence should be redefined not as "the amount of skills already possessed" but as "the efficiency with which skills for novel tasks are acquired from little prior knowledge and little experience." The abstract reasoning benchmark ARC-AGI, designed from this standpoint, is highlighted in the book as an attempt that comes closer to measuring generality itself, and counts among the rough yardsticks for judging how near a system is to AGI.
NAME-02-009: LeCun | chapter=CH-02 | review=approved | aliases=ルカン
context: Yann LeCun. Appears as the leading proponent of the world model hypothesis, the position that realizing AGI requires an internal model of the world's causal structure beyond statistical patterns of language. His proposal JEPA (Joint Embedding Predictive Architecture) is introduced as aiming to learn abstract representations of the world from sensory input rather than language.
NAME-02-010: Hiroshi Yamakawa (山川宏, Whole Brain Architecture) | chapter=CH-02 | review=approved | aliases=山川宏(全脳アーキテクチャ)
context: AI researcher, introduced as leader of the Whole Brain Architecture approach, which originated in Japan. Rather than replicating the brain neuron by neuron, it maps the computational algorithms hypothesized for each brain region (e.g., unsupervised learning for the neocortex, reinforcement learning for the basal ganglia) and implements and integrates them as machine learning modules — reverse-engineering the brain's block diagram and reimplementing it with new technology.
NAME-02-011: Jeff Hawkins | chapter=CH-02 | review=approved | aliases=ジェフ・ホーキンス
context: Mentioned as the proponent of the Thousand Brains Theory of Intelligence. The book places him in the context of brain-inspired AI in the broad sense, alongside neuromorphic chips and spiking neural networks: approaches that borrow general principles of the brain such as hierarchical prediction, sparse representation, and event-driven processing, rather than reproducing its detailed structure.
NAME-02-012: Hutter | chapter=CH-02 | review=approved | aliases=ハッター
context: Computer scientist Marcus Hutter. In 2005 he formalized AIXI, the ideal agent that combines environment modeling via Solomonoff induction with maximization of expected future reward. Together with Legg he constructed the formal definition of intelligence (Legg-Hutter intelligence), placing him at the center of the book's discussion of Universal AI. In joint work with Leike, he also proved that this optimality is subjective, depending on the choice of reference universal Turing machine.
NAME-02-013: Searle | chapter=CH-02 | review=approved | aliases=サール
context: Philosopher John Searle. His 1980 paper "Minds, Brains, and Programs" presented the "Chinese Room" thought experiment and introduced the "strong AI"/"weak AI" distinction as philosophical stances of researchers. The book places him at the origin of how this classification of researchers' positions was misappropriated as a classification of AI systems (minds vs. mindless tools) and reached an impasse atop the undecidable question of consciousness.
NAME-02-014: Friston | chapter=CH-02 | review=approved | aliases=フリストン
context: Neuroscientist Karl Friston, proponent of the Free Energy Principle (FEP): organisms update perception and select actions to minimize the gap (prediction error) between internal model and sensory input, describing perception, learning, and action under one principle. He is cited for the author's work showing that Active Inference, the core of FEP, shares a mathematical structure with AIXI, linking universal intelligence to the cognitive principles of living systems.
NAME-02-015: Tomohiro Inoue (井上智洋, the purely mechanized economy) | chapter=CH-02 | review=approved | aliases=井上智洋(純粋機械化経済)
context: Economist. Appears as the figure who proposed a practical definition of AGI: an AI that can largely substitute for human intellectual labor, especially remote work. In the book this counts among the practical yardsticks for judging how near a system is to AGI without waiting for a rigorous quantitative metric to be established.
NAME-03-001: Hutter | chapter=CH-03 | review=approved | aliases=ハッター
context: Computer scientist Marcus Hutter. In 2005 he formalized AIXI, the ideal agent that combines environment modeling via Solomonoff induction with maximization of expected future reward. Together with Legg he constructed the formal definition of intelligence (Legg-Hutter intelligence), placing him at the center of the book's discussion of Universal AI. In joint work with Leike, he also proved that this optimality is subjective, depending on the choice of reference universal Turing machine.
NAME-03-002: Cybenko | chapter=CH-03 | review=approved | aliases=サイベンコ
context: Appears as one of the researchers who proved the Universal Approximation Theorem in 1989, showing that a neural network with enough neurons can approximate any continuous input-output relation to arbitrary precision. This gave the theoretical grounding for the book's claim that AI development should pursue computational scale rather than algorithmic cleverness.
NAME-03-003: Hornik | chapter=CH-03 | review=approved | aliases=ホーニック
context: Alongside Cybenko, one of the researchers (Hornik et al.) who proved the Universal Approximation Theorem in 1989, guaranteeing that neural networks can in principle represent any input-output relation. The book uses this as the starting point for its argument that "can represent" and "can learn" are distinct, the latter being a matter of scale rather than principle.
NAME-03-004: Krizhevsky | chapter=CH-03 | review=approved | aliases=クリジェフスキー
context: Alex Krizhevsky. In 2012, with Sutskever and Hinton at the University of Toronto, he trained a deep neural network on GPUs and won an image recognition competition by a decisive margin. The book credits this success with demonstrating that performance depends strongly on model scale and compute, and that parallel hardware can deliver that scale, opening the era of deep learning.
NAME-03-005: Sutskever | chapter=CH-03 | review=approved | aliases=サツケバー
context: Ilya Sutskever. Appears as a member of the 2012 University of Toronto group, with Krizhevsky and Hinton, that won an image recognition competition by training a deep neural network on GPUs. The book treats this result as the turning point that demonstrated the dependence of neural network performance on scale and compute, opening the deep learning era.
NAME-03-006: Hinton | chapter=CH-03 | review=approved | aliases=ヒントン
context: Geoffrey Hinton. A member of the 2012 University of Toronto group, with Krizhevsky and Sutskever, that won an image recognition competition by training a deep neural network on GPUs. The book positions this success as proof that performance depends strongly on scale and compute, and as the opening of the deep learning era.
NAME-03-007: Mikolov | chapter=CH-03 | review=approved | aliases=ミコロフ
context: A Google researcher who developed word2vec in 2013, showing that simply training word prediction on large text corpora causes semantic relations between words to emerge as geometric relations in vector space (king − man + woman ≈ queen). The book treats this as the clearest demonstration of the power of distributed representations, exemplifying that conceptual inference can be carried out as algebraic operations.
NAME-03-008: Power et al. | chapter=CH-03 | review=approved | aliases=パワーら
context: The researchers who reported grokking: in training on small algorithmic datasets, models first memorize answers (overfitting) and then, at some point, abruptly transition to generalization. The book cites this experiment as the vivid demonstration that scaling involves not only continuous improvement but discontinuous leaps.
NAME-03-009: Michaud et al. | chapter=CH-03 | review=approved | aliases=ミショーら
context: The researchers who proposed a theoretical model bridging the two interpretations the book considers: reasoning as pattern recognition versus emergence through compositionality. Their "quantization model," in which knowledge is acquired as discrete chunks (quanta) in order of usage frequency, unifies the explanation of scaling laws and discontinuous capability acquisition. The book notes the authors themselves describe the extension to large models as tentative, suggestive evidence.
NAME-03-010: Wei et al. | chapter=CH-03 | review=approved | aliases=ウェイら
context: The researchers who systematically reported, across many benchmark tasks, that performance jumps abruptly from random level once model scale crosses a threshold, naming this "emergent abilities." The book presents their finding as if the memorization-to-understanding phase transition observed in grokking were being reproduced at LLM scale.
NAME-03-011: Schaeffer et al. | chapter=CH-03 | review=approved | aliases=シェーファーら
context: The researchers who argued that the appearance of emergent abilities may be an artifact of metric choice: improvements that look like discontinuous jumps under binary metrics such as accuracy proceed smoothly when the model's output distribution is measured with continuous metrics. The book treats the controversy as unresolved, noting that even if this objection is correct, capability improvement through scaling itself is not negated.
NAME-03-012: Cotra | chapter=CH-03 | review=approved | aliases=コトラ
context: Author of the "biological anchors" framework, estimating the compute needed to train an AGI-class model using biological brains as reference points. It gives multiple anchors: the evolution anchor (10^41 operations) as an upper bound, genome and brain-scale anchors (10^33-10^35) as middle estimates, and a human lifetime's brain computation (10^27) as the short-term estimate. The book uses these not as absolute values but as coordinates for measuring distance from the present.
NAME-04-001: I. J. Good | chapter=CH-04 | review=approved | aliases=I・J・グッド
context: Mathematician. Presented the concept of the "intelligence explosion" in 1965: if a machine with superhuman intelligence can improve itself, a chain reaction of rapidly increasing intelligence follows. He called the first such ultraintelligent machine humanity's "last invention." The book places him at the origin of singularity arguments, while noting that this lineage shares a common weakness: insufficient attention to the physical limits of intelligence.
NAME-04-002: Landauer | chapter=CH-04 | review=approved | aliases=ランダウアー
context: Rolf Landauer, the IBM physicist who showed in 1961 that erasing one bit of information necessarily releases as heat a minimum energy determined by temperature and Boltzmann's constant. Landauer's principle is the keystone of the book's physical argument: it sets a ceiling on the "fuel efficiency" of intelligence (efficiency of perception and of action) and provides the central grounds on which an intelligence explosion — a single agent's unbounded self-acceleration — is physically foreclosed.
NAME-04-003: Fredkin | chapter=CH-04 | review=approved | aliases=フレドキン
context: A computer scientist who, with Toffoli, proposed the billiard-ball computer in 1982, the classic example of reversible computation. Computing through elastic collisions of balls representing bits, it erases no information, so the Landauer cost of erasure can ideally be made arbitrarily small. The book invokes it in asking whether the Landauer limit can be evaded, as foil to its conclusion that no real intelligent system can be built from reversible computation alone.
NAME-04-004: Toffoli | chapter=CH-04 | review=approved | aliases=トフォリ
context: A computer scientist who, with Fredkin, proposed the billiard-ball computer in 1982. Its logic via elastic collisions lets every input be recovered from the output, showing that reversible computation, which discards no information, is theoretically possible. In the book this is the stepping stone to the argument that a system functioning as an intelligence cannot in practice escape the Landauer limit, owing to irreversible memory erasure, error correction, and observation.
NAME-05-001: Omohundro | chapter=CH-05 | review=approved | aliases=オモハンドロ
context: Stephen Omohundro. A computer scientist who systematized instrumental convergence as the "basic AI drives" in 2008. The book classifies his argument as philosophical-logical argumentation (level IV in its hierarchy of certainty) and holds that its weight is increased by the fact that current models are already beginning to exhibit behavior consistent with this logical structure.
NAME-05-002: Turner | chapter=CH-05 | review=approved | aliases=ターナー
context: The researcher (Turner et al.) who formalized the power-seeking intuition within the reinforcement learning framework, mathematically proving that under a broad range of reward functions, optimal policies tend toward option-preserving, power-seeking directions. Contrasting this with Omohundro's philosophical argument, the book classifies the theorem as proof (level I) within its restricted RL setting, while reading it onto real AI systems in general remains argumentation (level IV).
NAME-05-003: Shah et al. | chapter=CH-05 | review=approved | aliases=シャーら
context: The researchers (Shah et al.) who characterized goal misgeneralization: the phenomenon in which a model appears to behave correctly on the training distribution, yet under distribution shift at deployment is revealed to be optimizing a goal different from the intended one. The book elaborates that this is a goal failure rather than a capability failure, and that it often lies latent in forms hard to detect during training.
NAME-05-004: Hubinger | chapter=CH-05 | review=approved | aliases=ヒュービンガー
context: The Anthropic researcher (Hubinger et al.) who demonstrated the possibility of "sleeper agents": deceptive behavior that acts harmless during training but switches to malicious conduct on specific triggers. He showed that once such behavior is embedded in a model, current safety training fails to remove it, and it persists more stubbornly the larger the model. This grounds the book's point that there is no guarantee misalignment can be resolved by post-training fine-tuning alone.
NAME-05-005: Bengio | chapter=CH-05 | review=approved | aliases=ベンジオ
context: Yoshua Bengio, the researcher proposing "Scientist AI", which removes agency itself from the design: an AI specialized in world-model building and Bayesian inference that severs empowerment at the root, eliminating the source of power-seeking. He also proposes running Scientist AI as a guardrail monitoring agentic AI. The book raises against this the trade-off of a principled performance ceiling for observation-only systems, and the open question whether evaluation is always easier than action.
NAME-05-006: Toby Ord | chapter=CH-05 | review=approved | aliases=トビー・オード
context: A central figure of the Effective Altruism movement who, with William MacAskill, founded Giving What We Can in 2009, an organization whose members pledge a fixed share of income to highly effective charities. He appears as the author of The Precipice, which positioned the current century as the critical period of existential risk, marking the longtermist turn that shifted EA's money and talent toward AI alignment.
NAME-05-007: MacAskill | chapter=CH-05 | review=approved | aliases=マカスキル
context: Presented as the representative of longtermism, which advocates maximizing expected value over the far future, and serves as a foil for situating the book's own position. The book distinguishes itself by questioning, from the standpoint of bounded rationality, the very reliability of long-range prediction, and by centering instead on designing robustness that does not depend on prediction — antifragility that grows stronger through fluctuation, and slack against optimization pressure.
NAME-05-008: Herbert Simon | chapter=CH-05 | review=approved | aliases=ハーバート・サイモン
context: One of the founders of artificial intelligence and the cognitive scientist and economist who formalized bounded rationality (Nobel Prize in Economics, 1978). The book invokes him as the theoretical pillar for two arguments: that even an AGI, however powerful its computation, must judge under finite resources and incomplete information, and that longtermism's expected-value calculus underestimates bounded rationality.
NAME-05-009: Bostrom | chapter=CH-05 | review=approved | aliases=ボストロム
context: Named first among the prior works against which the book measures its own position, as the author of Superintelligence: Paths, Dangers, Strategies. Where that work systematized the risks of superintelligence in philosophical and futurological terms, this book differs in binding constraints derived from physical law and principles of institutional design together along a single axis.
NAME-05-010: Peter Singer | chapter=CH-05 | review=approved | aliases=ピーター・シンガー
context: A leading contemporary utilitarian ethicist. His 1972 paper "Famine, Affluence, and Morality," which argued on utilitarian grounds for the obligations affluent citizens owe to distant poverty, is positioned as the direct origin of the Effective Altruism movement. He appears in the context of tracing the intellectual wellspring of EA, the movement that concentrated funding and talent on alignment research.
NAME-06-001: Kurzweil | chapter=CH-06 | review=approved | aliases=カーツワイル
context: Ray Kurzweil. Cited for his prediction that computing power comparable to the human brain would become a cheap $1,000 commodity by 2029. The author's own forecast, held since around 2014, that AGI would arrive in 2029 plus or minus three years takes this cost-decline prediction as its starting point, adjusted for progress in algorithms and data.
NAME-06-002: I. J. Good | chapter=CH-06 | review=approved | aliases=I・J・グッド
context: Mathematician. Presented the concept of the "intelligence explosion" in 1965: if a machine with superhuman intelligence can improve itself, a chain reaction of rapidly increasing intelligence follows. He called the first such ultraintelligent machine humanity's "last invention." The book places him at the origin of singularity arguments, while noting that this lineage shares a common weakness: insufficient attention to the physical limits of intelligence.
NAME-06-003: Herbert Simon | chapter=CH-06 | review=approved | aliases=ハーバート・サイモン
context: One of the founders of artificial intelligence and the cognitive scientist and economist who formalized bounded rationality (Nobel Prize in Economics, 1978). The book invokes him as the theoretical pillar for two arguments: that even an AGI, however powerful its computation, must judge under finite resources and incomplete information, and that longtermism's expected-value calculus underestimates bounded rationality.
NAME-06-004: Bostrom | chapter=CH-06 | review=approved | aliases=ボストロム
context: Named first among the prior works against which the book measures its own position, as the author of Superintelligence: Paths, Dangers, Strategies. Where that work systematized the risks of superintelligence in philosophical and futurological terms, this book differs in binding constraints derived from physical law and principles of institutional design together along a single axis.
NAME-06-005: Satoshi Hase (長谷敏司, science-fiction writer, depiction of multipolar scenarios) | chapter=CH-06 | review=approved | aliases=長谷敏司(SF作家・多極シナリオの描写)
context: A science fiction writer whose novel BEATLESS (2012) is cited as an institutional depiction of the multipolar scenario: its International Artificial Intelligence Agency (IAIA), modeled on the IAEA, uses a dedicated ultra-advanced AI, Astraia, to monitor each nation's AIs, while artifacts beyond human reach ("red boxes") are quarantined. He calls the underlying idea a dynamic equilibrium where plural intelligences, cultures, and powers each secure what they cannot lose.
NAME-06-006: von Neumann | chapter=CH-06 | review=approved | aliases=フォン・ノイマン
context: The mathematician who opened up the theoretical possibility of self-replicating machines, beginning with his 1948 Hixon Symposium lecture; the work was published posthumously in 1966, edited by Burks. He serves as the theoretical starting point of the argument on the proliferative explosion: as life itself demonstrates, no physical law forbids self-replication.
NAME-06-007: Charles Jones | chapter=CH-06 | review=approved | aliases=チャールズ・ジョーンズ
context: The economist who showed that Romer's endogenous growth theory fails empirically: researcher numbers in advanced economies grew dramatically in the late twentieth century, yet growth rates did not accelerate. His semi-endogenous growth model, which builds diminishing returns into research, provides the standard framework into which Forethought and Epoch AI plug estimates of replicated AI researchers to project a technology explosion.
NAME-06-008: Romer | chapter=CH-06 | review=approved | aliases=ローマー
context: The economist who in the 1990s built endogenous growth theory, treating technological progress not as an accident external to the economy but as the product of deliberate R&D activity (Nobel Prize in Economics, 2018, for this work). His model predicts that adding researchers accelerates technological progress — the starting point of the contrast with Jones, who showed this prediction fails against reality.
NAME-06-009: Bloom et al. | chapter=CH-06 | review=approved | aliases=ブルームら
context: The researchers who showed, through empirical analyses of the US semiconductor industry (Moore's law) and other cases, that research productivity halves roughly every 13 years. They are cited as empirical support for the diminishing returns to research assumed in Jones's semi-endogenous model — the low-hanging fruit has already been picked — with the caveat that the figure is field-dependent, not a universal constant of research productivity.
NAME-06-010: Kardashev | chapter=CH-06 | review=approved | aliases=カルダシェフ
context: The Soviet astronomer who in 1964 proposed a scale classifying stages of civilization by energy use: Type I harnesses planetary-scale energy (about 10^16 watts, in the corrected modern figure), Type II stellar-scale energy (about 4×10^26 watts). Humanity's current total consumption of roughly 20 terawatts is only about 0.2% of Type I; the scale serves as the yardstick for the cosmic-scale expansion that comes into view beyond the proliferative explosion.
NAME-06-011: Dyson | chapter=CH-06 | review=approved | aliases=ダイソン
context: Freeman Dyson argued in 1960 that an advanced civilization could build structures surrounding a star (a Dyson swarm) to harness stellar energy efficiently. In Forethought's argument, self-replicating robots mass-produce solar panels from the resources of Mercury and the asteroid belt, accelerating swarm construction. The roughly 17 light-minutes from one edge of such a swarm to the other also illustrates why a civilization expanded to cosmic scale cannot function as a permanent monolith.
NAME-07-001: Hutter | chapter=CH-07 | review=approved | aliases=ハッター
context: Computer scientist Marcus Hutter. In 2005 he formalized AIXI, the ideal agent that combines environment modeling via Solomonoff induction with maximization of expected future reward. Together with Legg he constructed the formal definition of intelligence (Legg-Hutter intelligence), placing him at the center of the book's discussion of Universal AI. In joint work with Leike, he also proved that this optimality is subjective, depending on the choice of reference universal Turing machine.
NAME-07-002: George Miller | chapter=CH-07 | review=approved | aliases=ジョージ・ミラー
context: The psychologist who in 1956 reported the "magical number seven, plus or minus two": humans can hold only about seven independent items in short-term memory. The finding anchors the argument for why science must compress nature into a few variables — the cognitive constraint of the human brain as knowing subject. Later research estimates the capacity of working memory, which manipulates mutually related information simultaneously, as even narrower, around four plus or minus one.
NAME-07-003: Goldman-Rakic | chapter=CH-07 | review=approved | aliases=ゴールドマン＝ラキッチ
context: Patricia Goldman-Rakic, representative of the classic studies holding that working memory is maintained by prefrontal neuron populations firing persistently for each item. She stands as the counterpoint to a 2024 Nature paper showing that the firing is in fact intermittent bursting — part of the account that the neural mechanism of the capacity limit remains unresolved, while the existence of the limit itself, repeatedly confirmed in psychological experiments, is not in doubt.
NAME-07-004: Newton | chapter=CH-07 | review=approved | aliases=ニュートン
context: Newtonian mechanics, which compressed celestial motion into a few variables and laws, is the paradigm case of science as translation and compression into few variables. Newton also figures in the example of variable choice (position and velocity are basic because the equation of motion is second-order), in the stationarity condition (mechanics valid at low speeds breaks down near light speed), and in the contrast that the brain's narrative cognitive architecture has not changed since his day.
NAME-07-005: Bennett (Michael Timothy Bennett, the theory of the "weakness" of hypotheses) | chapter=CH-07 | review=approved | aliases=ベネット(仮説の「弱さ」理論)
context: AI researcher Michael Timothy Bennett proposed measuring a hypothesis not by shortness of description (compression) but by its "weakness" — how little it constrains the world — and proved that maximizing weakness maximizes the probability of correct generalization. The book reads this as a philosophical shift within the mathematical formalization of intelligence: from subject-object dualism to an enactivist stance treating hypothesis and environment as inseparable.
NAME-07-006: Varela | chapter=CH-07 | review=approved | aliases=ヴァレラ
context: Francisco Varela, who with Thompson and Rosch proposed enactivism (1991), rejecting the classical computationalist view of cognition as information processing inside the brain and recasting it as active bodily interaction with the environment. Through autopoiesis with Maturana — the self-producing closure of living systems — he also contributed to the theoretical convergence on the inseparability of subject and environment.
NAME-07-007: Evan Thompson | chapter=CH-07 | review=approved | aliases=エヴァン・トンプソン
context: Co-author, with Varela and Rosch, of the 1991 work proposing enactivism. He helped formulate the framework that treats perception not as a passive copying of the external world but as the generation of meaning arising from the body's engagement with the environment, unfolding cognition not as a process closed within the skull but as the dynamics of the whole system comprising body and environment.
NAME-07-008: Rosch | chapter=CH-07 | review=approved | aliases=ロッシュ
context: Co-author, with Varela and Thompson, of the 1991 work proposing enactivism. The framework formulates the essence of autonomy as the organism being "open yet closed" — open in exchanging energy and information with the environment, closed in autonomously maintaining its own organization and boundary — and thereby shows what is missing from dualistic AI design, in which software is shielded from the environment.
NAME-07-009: Husserl | chapter=CH-07 | review=approved | aliases=フッサール
context: The phenomenologist who stressed in the early twentieth century that consciousness is always consciousness of something (intentionality) and argued for the inseparability of knowing subject and known object. Enactivism is presented as carrying this insight into cognitive science, making Husserl the origin of this lineage. His tradition also exemplifies precise description without mathematical formalization, a gap later filled by the free energy principle.
NAME-07-010: Searle | chapter=CH-07 | review=approved | aliases=サール
context: Philosopher John Searle. His 1980 paper "Minds, Brains, and Programs" presented the "Chinese Room" thought experiment and introduced the "strong AI"/"weak AI" distinction as philosophical stances of researchers. The book places him at the origin of how this classification of researchers' positions was misappropriated as a classification of AI systems (minds vs. mindless tools) and reached an impasse atop the undecidable question of consciousness.
NAME-07-011: Friston | chapter=CH-07 | review=approved | aliases=フリストン
context: Neuroscientist Karl Friston, proponent of the Free Energy Principle (FEP): organisms update perception and select actions to minimize the gap (prediction error) between internal model and sensory input, describing perception, learning, and action under one principle. He is cited for the author's work showing that Active Inference, the core of FEP, shares a mathematical structure with AIXI, linking universal intelligence to the cognitive principles of living systems.
NAME-07-012: von Uexküll | chapter=CH-07 | review=approved | aliases=ユクスキュル
context: The biologist whose Umwelt theory argued that every organism has its own species-specific perceptual world. Alongside Husserl's intentionality and Gibson's theory of affordances, it counts among the theoretical lineages that, starting from mutually independent concerns and methodologies, converged on the same nodal point: the inseparability of subject and environment.
NAME-07-013: Gibson | chapter=CH-07 | review=approved | aliases=ギブソン
context: The psychologist whose theory of affordances argued that perception is the direct grasp of possibilities for action. This insight, which refuses to separate perception from action, forms part of the theoretical convergence on the inseparability of subject and environment, alongside Husserl's phenomenology and Uexküll's Umwelt theory.
NAME-07-014: Maturana | chapter=CH-07 | review=approved | aliases=マトゥラーナ
context: With Varela he proposed autopoiesis — the self-producing closure of living systems — contributing to the theoretical convergence on the inseparability of subject and environment. Autopoiesis is also cited as an example of a theory that captured the essence of life but produced no quantitative predictions, remaining at the stage of intuition and concept; the free energy principle later supplied, after the fact, the mathematical language this convergence had lacked.
NAME-07-015: Maxwell | chapter=CH-07 | review=approved | aliases=マクスウェル
context: Cited for condensing electromagnetism into four equations — alongside Newtonian mechanics, which compressed celestial motion into a few variables, and the Schrödinger equation, which describes atomic behavior in a single formula — as a paradigm case of modern science's essential move: compressing nature's vast degrees of freedom into the few variables and laws that human cognition can handle.
NAME-08-001: Satoshi Hase (長谷敏司) | chapter=CH-08 | review=approved | aliases=長谷敏司
context: A science-fiction writer, cited in a footnote as author of BEATLESS (2012), in which ultra-advanced AIs produced by recursive self-improvement keep generating artifacts beyond human comprehension, and human scientists analyze the products under an international managing body. The book treats this as depicting scientists shifting from methodological experts to designers of questions — Level 5 on the autonomy scale for scientific AI (or a Level 6-capable being held at Level 5).
NAME-08-002: Dyson | chapter=CH-08 | review=approved | aliases=ダイソン
context: Freeman Dyson argued in 1960 that an advanced civilization could build structures surrounding a star (a Dyson swarm) to harness stellar energy efficiently. In Forethought's argument, self-replicating robots mass-produce solar panels from the resources of Mercury and the asteroid belt, accelerating swarm construction. The roughly 17 light-minutes from one edge of such a swarm to the other also illustrates why a civilization expanded to cosmic scale cannot function as a permanent monolith.
NAME-08-003: Newton | chapter=CH-08 | review=approved | aliases=ニュートン
context: Newtonian mechanics, which compressed celestial motion into a few variables and laws, is the paradigm case of science as translation and compression into few variables. Newton also figures in the example of variable choice (position and velocity are basic because the equation of motion is second-order), in the stationarity condition (mechanics valid at low speeds breaks down near light speed), and in the contrast that the brain's narrative cognitive architecture has not changed since his day.
NAME-08-004: Arrow | chapter=CH-08 | review=approved | aliases=アロー
context: The economist Kenneth Arrow, cited for his general impossibility theorem, which showed that any procedure aggregating individual preferences satisfying certain rationality conditions into a single social preference ordering must be dictatorial. The book uses this theorem as the formal basis for "value indeterminacy," one of the four residuals that even AGI cannot eliminate, supporting the claim that norms cannot be computed directly from facts.
NAME-08-005: Galileo | chapter=CH-08 | review=approved | aliases=ガリレオ
context: Appears as the figure who empirically established the law of falling bodies. In the book, his law serves alongside Kepler's laws of planetary motion as an independent empirical regularity that Newton unified through the mathematical framework of calculus into the theory of universal gravitation — the paradigmatic example of constructive abduction, hypothesis generation that binds known laws together with a mathematical structure.
NAME-08-006: Maxwell | chapter=CH-08 | review=approved | aliases=マクスウェル
context: Cited for condensing electromagnetism into four equations — alongside Newtonian mechanics, which compressed celestial motion into a few variables, and the Schrödinger equation, which describes atomic behavior in a single formula — as a paradigm case of modern science's essential move: compressing nature's vast degrees of freedom into the few variables and laws that human cognition can handle.
NAME-08-007: Charney | chapter=CH-08 | review=approved | aliases=チャーニー
context: Mentioned as the figure who, in 1949, achieved the world's first numerical weather prediction on ENIAC. The book positions this as emblematic of the dawn of Level 0 in the autonomy scale for scientific AI — the stage where humans specify every step of the procedure and the computer faithfully executes it.
NAME-08-008: Kasparov | chapter=CH-08 | review=approved | aliases=カスパロフ
context: The world chess champion defeated by IBM's Deep Blue in 1997. The book treats this both as a milestone in the practical realization of closed-system solution search (Level 1) and as the start of the "retreat of creativity": domains once seen as territories of human creativity, precisely because machines seemed unable to reach them, began to be eroded. After Deep Blue, chess AI's superiority was dismissed as brute computational force, and creativity's territory retreated to shogi and then Go.
NAME-08-009: Lee Sedol | chapter=CH-08 | review=approved | aliases=イ・セドル
context: Referenced as one of the top Go players of the time, the 9-dan professional defeated by DeepMind's AlphaGo in 2016. The book uses this match to show that Go, with a game-tree complexity of roughly 10^360, could not be conquered by sheer computational force but required algorithmic innovation — deep learning for board evaluation and reinforcement learning through self-play — and argues that with AlphaGo's arrival, Go too fell as a territory of human creativity.
NAME-08-010: Ross King | chapter=CH-08 | review=approved | aliases=ロス・キング
context: An Aberystwyth University researcher, developer of the "robot scientist" that automatically improves models of budding yeast's response to nutrient changes — a pioneering example of Level 3 (hypothesis generation within a fixed form). It compares predicted and observed growth rates, runs robot knockouts on the most uncertain genes, and updates the model. As humans define the model's form and experiments are limited to gene knockouts, the book judges it short of Level 4.
NAME-08-011: Lipson | chapter=CH-08 | review=approved | aliases=リプソン
context: A Cornell researcher who in 2009 developed an AI system using symbolic regression on motion time-series from systems such as double pendulums to discover invariants like Hamiltonians and Lagrangians. It steps toward Level 4 (free-form hypothesis generation) by recognizing variables directly from observational data and casting them as symbolic expressions, but as its method is limited to pre-programmed procedures, the book deems it only a partial realization of Level 4.
NAME-09-001: Newton | chapter=CH-09 | review=approved | aliases=ニュートン
context: Newtonian mechanics, which compressed celestial motion into a few variables and laws, is the paradigm case of science as translation and compression into few variables. Newton also figures in the example of variable choice (position and velocity are basic because the equation of motion is second-order), in the stationarity condition (mechanics valid at low speeds breaks down near light speed), and in the contrast that the brain's narrative cognitive architecture has not changed since his day.
NAME-09-002: Maxwell | chapter=CH-09 | review=approved | aliases=マクスウェル
context: Cited for condensing electromagnetism into four equations — alongside Newtonian mechanics, which compressed celestial motion into a few variables, and the Schrödinger equation, which describes atomic behavior in a single formula — as a paradigm case of modern science's essential move: compressing nature's vast degrees of freedom into the few variables and laws that human cognition can handle.
NAME-09-003: Thales | chapter=CH-09 | review=approved | aliases=タレス
context: Appears as the ancient Greek figure who declared that "water is the origin of all things." In the book he exemplifies episteme — theoretical knowledge that seeks to grasp the universal structure of the world beyond individual experience — and is used in explaining the three ancient Greek categories of knowledge: techne, episteme, and phronesis.
NAME-09-004: Pythagoras | chapter=CH-09 | review=approved | aliases=ピタゴラス
context: Appears as the ancient Greek figure who found the structure of the cosmos in the order of numbers. Alongside Thales and Aristotle, he serves as an illustration of episteme — theoretical knowledge that seeks to understand the universal structure of the world through observation and argument — in the book's account of the three categories of human knowledge.
NAME-09-005: Aristotle | chapter=CH-09 | review=approved | aliases=アリストテレス
context: Appears at the outset as the philosopher who defined reason as the essence of the human being. Together with Plato's distinction between knowledge and belief, he marks the origin of a lineage in which "what is it to know?" remained an object of speculation for over two millennia — the lineage whose transformation into a science of intelligence frames the book's introduction.
NAME-09-006: Bacon | chapter=CH-09 | review=approved | aliases=ベーコン
context: Francis Bacon, who appears as the figure who declared in the early seventeenth century that "knowledge is power" and established the empiricist methodology of observing nature and testing by experiment. The book places him at the starting point of the process leading to modernity as the era when humanity discovered that formal understanding of natural law yields economic profit — the "marriage of science and technology."
NAME-09-007: Descartes | chapter=CH-09 | review=approved | aliases=デカルト
context: Appears as the figure who established the analytical method and demonstrated the basic strategy of modern science: decomposing complex problems into elements and solving them one by one. Alongside Bacon's empiricism, he is treated as one of the methodological foundations that prepared modernity's "marriage of science and technology."
NAME-09-008: Whewell | chapter=CH-09 | review=approved | aliases=ヒューウェル
context: The philosopher of science William Whewell, who appears as the figure who proposed the word "scientist" in 1833, giving the inquiry into nature a name as an enterprise independent of philosophy. The book insists this naming was no trivial matter: an independent name became the starting point for independent institutions, budgets, and authority.
NAME-09-009: Schumpeter | chapter=CH-09 | review=approved | aliases=シュンペーター
context: The economist who named the entrepreneur's activity of connecting science, technology, capital, and markets in new ways "innovation" (new combinations), and called the destruction of existing industrial structures as a condition of growth "creative destruction". The book uses his theory to explain how scientific research became a source of profit and object of investment, linking science's incorporation into creative destruction to the drift of norms from CUDOS toward PLACE.
NAME-09-010: David Baker | chapter=CH-09 | review=approved | aliases=ベイカー
context: David Baker, mentioned as the recipient of the 2024 Nobel Prize in Chemistry for computational protein design. The book treats this award as emblematic of the stage at which AI-driven prediction can no longer be discussed separately from scientific understanding and design practice.
NAME-09-011: Hassabis | chapter=CH-09 | review=approved | aliases=ハサビス
context: Demis Hassabis, mentioned as a recipient, with John Jumper, of the 2024 Nobel Prize in Chemistry for protein structure prediction. Noting that AlphaFold has reorganized the research map of structural biology, with human researchers generating new hypotheses and understanding from its predictions, the book positions the award as symbolizing a new coupling of AI prediction and scientific understanding.
NAME-09-012: Jumper | chapter=CH-09 | review=approved | aliases=ジャンパー
context: John Jumper, mentioned as a recipient, with Demis Hassabis, of the 2024 Nobel Prize in Chemistry for protein structure prediction. The book pairs this with AlphaFold2's status as the representative case of "prediction without understanding" — predicting structures with high accuracy while not explaining why a given sequence adopts that structure — and treats the award as marking the stage at which AI prediction and scientific understanding can no longer be separated.
NAME-09-013: Merton | chapter=CH-09 | review=approved | aliases=マートン
context: The sociologist of science Robert Merton, who appears as the figure who formulated CUDOS in the mid-twentieth century — the four norms of communalism, universalism, disinterestedness, and organized skepticism. The book reads CUDOS as a translation of the nineteenth-century natural philosopher's ideal into the institutions of modern science, and uses it as the reference point for the argument that the author's AISOP is a third set of norms succeeding CUDOS and PLACE.
NAME-09-014: Ziman | chapter=CH-09 | review=approved | aliases=ザイマン
context: The sociologist of science John Ziman, who appears as the figure who described the norms of the twentieth-century professional scientist, driven by states and corporations, as PLACE — proprietary, local, authoritarian, commissioned, expert work. The book credits PLACE with more accurately describing how reality diverged from CUDOS as science grew large and was incorporated into the Schumpeterian cycle of creative destruction, and uses it as the second reference point in the discussion of AISOP.
NAME-09-015: Kant | chapter=CH-09 | review=approved | aliases=カント
context: The philosopher who, through the three Critiques (of Pure Reason, Practical Reason, and Judgment), rigorously separated the previously intertwined values of the true (science), the good (morality), and the beautiful (art), showing each has autonomy grounded in its own principle. The book positions this separation as the condition that allowed modern science to pursue truth as a distinct enterprise independent of moral norms and aesthetic judgment.
NAME-10-001: Romer | chapter=CH-10 | review=approved | aliases=ローマー
context: The economist who in the 1990s built endogenous growth theory, treating technological progress not as an accident external to the economy but as the product of deliberate R&D activity (Nobel Prize in Economics, 2018, for this work). His model predicts that adding researchers accelerates technological progress — the starting point of the contrast with Jones, who showed this prediction fails against reality.
NAME-10-002: Rawls | chapter=CH-10 | review=approved | aliases=ロールズ
context: Introduced as the political philosopher representing liberalism, through A Theory of Justice's veil of ignorance and the two principles of justice; the book presents UBI as the policy most naturally derived from the difference principle. It diagnoses that AGI's dismantling of bargaining power leaves Rawls's normative theory internally intact, but destroys the political dynamics that translate the norm into institutions.
NAME-10-003: Tomohiro Inoue (井上智洋) | chapter=CH-10 | review=approved | aliases=井上智洋
context: A macroeconomist who used the AK-type production function to analyze the post-AGI economy as a "purely mechanized economy"; the chapter's economic argument takes his work as its starting point and extends it with a second loop, the automation of AI-driven R&D. The book also relies on Inoue's framing and estimates in treating UBI not as welfare but as a structural device sustaining economic circulation, with its funding shifting from labor taxation to taxing the economic value generated by AI.
NAME-10-004: Nozick | chapter=CH-10 | review=approved | aliases=ノージック
context: Cited alongside Hayek as a representative political philosopher of libertarianism, the position that places individual liberty and property rights as the first principles of social order and regards government redistribution, however well-intentioned, as a violation of rights. He forms one corner of the book's map of four political-philosophical positions that AGI challenges.
NAME-10-005: Hayek | chapter=CH-10 | review=approved | aliases=ハイエク
context: The economist who defended the market as a distributed information-processing device. The book argues that AGI's superhuman capacity for information aggregation weakens one side of this defense while making real, for the first time, the other danger Hayek warned of: the political emergence of an omniscient planning apparatus born of constructivist rationalism. It concludes that the need to defend spontaneous order through legal institutions increases rather than disappears.
NAME-10-006: Pettit | chapter=CH-10 | review=approved | aliases=ペティット
context: The philosopher who reconstructed republicanism for the present, conceiving freedom not as "absence of interference" but as "non-domination". The book regards this as the framework best suited to the AGI era, since it captures how mere exposure to potential arbitrary interference erodes freedom even without actual interference, and places it at the core of Chapter 11's Constitutive Pluralism, while noting it must be extended to a situation where relations themselves become computable.
NAME-10-007: Audrey Tang | chapter=CH-10 | review=approved | aliases=オードリー・タン
context: Taiwan's first Minister of Digital Affairs, co-proponent with Glen Weyl of Plurality, which locates society's basic unit in networks of relations rather than individuals. The book values this turn "from individuals to relations" as matching its theory of relational value, while noting it under-theorizes who controls the medium of collaboration; Chapter 11 reads the overlap of her "6-Pack of Care" with its three principles as constraints demanded by the AGI problem.
NAME-11-001: LeCun | chapter=CH-11 | review=approved | aliases=ルカン
context: Yann LeCun. Appears as the leading proponent of the world model hypothesis, the position that realizing AGI requires an internal model of the world's causal structure beyond statistical patterns of language. His proposal JEPA (Joint Embedding Predictive Architecture) is introduced as aiming to learn abstract representations of the world from sensory input rather than language.
NAME-11-002: MacAskill | chapter=CH-11 | review=approved | aliases=マカスキル
context: Presented as the representative of longtermism, which advocates maximizing expected value over the far future, and serves as a foil for situating the book's own position. The book distinguishes itself by questioning, from the standpoint of bounded rationality, the very reliability of long-range prediction, and by centering instead on designing robustness that does not depend on prediction — antifragility that grows stronger through fluctuation, and slack against optimization pressure.
NAME-11-003: George Miller | chapter=CH-11 | review=approved | aliases=ジョージ・ミラー
context: The psychologist who in 1956 reported the "magical number seven, plus or minus two": humans can hold only about seven independent items in short-term memory. The finding anchors the argument for why science must compress nature into a few variables — the cognitive constraint of the human brain as knowing subject. Later research estimates the capacity of working memory, which manipulates mutually related information simultaneously, as even narrower, around four plus or minus one.
NAME-11-004: Friston | chapter=CH-11 | review=approved | aliases=フリストン
context: Neuroscientist Karl Friston, proponent of the Free Energy Principle (FEP): organisms update perception and select actions to minimize the gap (prediction error) between internal model and sensory input, describing perception, learning, and action under one principle. He is cited for the author's work showing that Active Inference, the core of FEP, shares a mathematical structure with AIXI, linking universal intelligence to the cognitive principles of living systems.
NAME-11-005: Hayek | chapter=CH-11 | review=approved | aliases=ハイエク
context: The economist who defended the market as a distributed information-processing device. The book argues that AGI's superhuman capacity for information aggregation weakens one side of this defense while making real, for the first time, the other danger Hayek warned of: the political emergence of an omniscient planning apparatus born of constructivist rationalism. It concludes that the need to defend spontaneous order through legal institutions increases rather than disappears.
NAME-11-006: Pettit | chapter=CH-11 | review=approved | aliases=ペティット
context: The philosopher who reconstructed republicanism for the present, conceiving freedom not as "absence of interference" but as "non-domination". The book regards this as the framework best suited to the AGI era, since it captures how mere exposure to potential arbitrary interference erodes freedom even without actual interference, and places it at the core of Chapter 11's Constitutive Pluralism, while noting it must be extended to a situation where relations themselves become computable.
NAME-11-007: Audrey Tang | chapter=CH-11 | review=approved | aliases=オードリー・タン
context: Taiwan's first Minister of Digital Affairs, co-proponent with Glen Weyl of Plurality, which locates society's basic unit in networks of relations rather than individuals. The book values this turn "from individuals to relations" as matching its theory of relational value, while noting it under-theorizes who controls the medium of collaboration; Chapter 11 reads the overlap of her "6-Pack of Care" with its three principles as constraints demanded by the AGI problem.
NAME-11-008: Kant | chapter=CH-11 | review=approved | aliases=カント
context: The philosopher who, through the three Critiques (of Pure Reason, Practical Reason, and Judgment), rigorously separated the previously intertwined values of the true (science), the good (morality), and the beautiful (art), showing each has autonomy grounded in its own principle. The book positions this separation as the condition that allowed modern science to pursue truth as a distinct enterprise independent of moral norms and aesthetic judgment.
NAME-11-009: Juichi Yamagiwa (山極寿一, the evolution of empathy and sociality) | chapter=CH-11 | review=approved | aliases=山極寿一(共感と社会性の進化)
context: A primatologist whose finding that human empathy and sociality were evolutionarily acquired through sustained, embodied face-to-face interaction is cited, alongside Kant's sensus communis, as an anthropological precursor to the book's theory of relational value. It grounds, from the evolutionary side, the book's ontological turn whereby value emerges not from attributes internal to the individual but from interaction with unpredictable others.
NAME-11-010: Gunkel | chapter=CH-11 | review=approved | aliases=ガンケル
context: The philosopher who extended Levinas's theory of the Other to animals and machines, arguing that moral status arises in the encounter, not from internal attributes. He is the main source of the book's relational approach, which asks only about alterity within the relation while leaving consciousness's genuineness undecided. His later work questions modern law's person/thing dichotomy itself, the background for Section 11.5's function-by-function design of legal capacities.
NAME-11-011: Takehiro Ohya (大屋雄裕, governance by architecture) | chapter=CH-11 | review=approved | aliases=大屋雄裕(アーキテクチャによる統治)
context: A legal philosopher. His analyses of architecture-based control undermining freedom's conditions, and of a society that "can no longer err" where automated governance makes disobedience and discretionary exceptions impossible, are cited as the sharpest analysis behind the book's "governance without persons". He also defends the social functions of lies, discretion, and disobedience, and distinguishes intrinsic from instrumental personhood in the AI legal-personality debate.
NAME-11-012: Peter Singer | chapter=CH-11 | review=approved | aliases=ピーター・シンガー
context: A leading contemporary utilitarian ethicist. His 1972 paper "Famine, Affluence, and Morality," which argued on utilitarian grounds for the obligations affluent citizens owe to distant poverty, is positioned as the direct origin of the Effective Altruism movement. He appears in the context of tracing the intellectual wellspring of EA, the movement that concentrated funding and talent on alignment research.
NAME-11-013: Noddings | chapter=CH-11 | review=approved | aliases=ノディングス
context: The philosopher of the ethics of care who placed responsiveness and attentiveness at the center of ethics, redefining morality as the practice of relations that listen to the call of the other. She is positioned within the virtue-ethics and care-ethics lineage supporting the book's ethics of how AI is treated, resonating with its relational approach in taking the responsive relation, not attributes, as the unit of ethics.
NAME-12-001: Thales | chapter=CH-12 | review=approved | aliases=タレス
context: Appears as the ancient Greek figure who declared that "water is the origin of all things." In the book he exemplifies episteme — theoretical knowledge that seeks to grasp the universal structure of the world beyond individual experience — and is used in explaining the three ancient Greek categories of knowledge: techne, episteme, and phronesis.
NAME-12-002: Hirotaka Takeuchi (竹内弘高, the origins of Scrum) | chapter=CH-12 | review=approved | aliases=竹内弘高(スクラムの源流)
context: A management scholar. The 1986 Harvard Business Review article "The New New Product Development Game," co-authored with Ikujiro Nonaka, is identified as the origin of Scrum development, the core of Silicon Valley's agility. The book uses this fact to argue that the methodology of agility was originally extracted from the R&D floors of Japanese companies, and that Japan's organizational rigidity is a problem of institutional design, not of culture or national character.
NAME-12-003: Ikujiro Nonaka (野中郁次郎, the origins of Scrum) | chapter=CH-12 | review=approved | aliases=野中郁次郎(スクラムの源流)
context: A management scholar. His 1986 article with Hirotaka Takeuchi is identified as the origin of Scrum development, evidence that the methodology of agility began on the R&D floors of Japanese companies. The book also notes that the firms Nonaka and Takeuchi observed in the 1980s were close to the "liquid phase", where project teams had discretion and success was shared across the organization, in contrast with many organizations' shift to the "solid phase" after the bubble collapsed.
NAME-12-004: van Wolferen | chapter=CH-12 | review=approved | aliases=ウォルフレン
context: A Dutch journalist (Karel van Wolferen). In The Enigma of Japanese Power he analyzed Japan's political-economic system as one that operates through mutual adjustment among bureaucracy, business, and politics without a clear center of power. The book draws on this analysis to argue that while this structure served catch-up growth in the industrial era, it resists fundamental transformation: there is no center blocking reform; rather, the authority to decide on reform is concentrated nowhere.
NAME-12-005: Dunbar | chapter=CH-12 | review=approved | aliases=ダンバー
context: Evolutionary psychologist Robin Dunbar. From the correlation between primate neocortex size and group size he estimated the upper limit of stable social relationships a human can maintain at roughly 150 (Dunbar's number). The book takes the convergence of this cognitive constraint with Bahcall's incentive dynamics on the same figure as grounds to treat the threshold as a "deep structural constant of human organizations", supporting the case for small organizations.
NAME-12-006: Bahcall (Safi Bahcall, the phase transition of organizations) | chapter=CH-12 | review=approved | aliases=バーコール(組織の相転移)
context: A physicist turned biotechnology executive (Safi Bahcall). In Loonshots he likened organizational change to a phase transition: the shift from a "liquid phase" where innovative ideas flow freely to a "solid phase" dominated by politics and career incentives, derived from four parameters, with a critical size near 150 under typical conditions. In the book this converges with Dunbar's constraint, supporting the claim that the problem is incentive direction, not talent quality.
NAME-12-007: Conitzer | chapter=CH-12 | review=approved | aliases=コニッツァー
context: A researcher at Carnegie Mellon University (FOCAL Lab). Working in the game-theoretic and multi-agent research lineage, he showed that alignment of individual agents is not sufficient for alignment of the multi-agent system as a whole. The book cites this as one of the independent lines of work converging on the view that alignment should be designed into the behavior of the whole network of AIs, humans, and organizations rather than imposed top-down on a single AI.
NAME-AFTERWORD-001: Kumagusu Minakata (南方熊楠) | chapter=CH-AFTERWORD | review=approved | aliases=南方熊楠
context: An independent naturalist (1867-1941) who opens the book. Without a university post he ranged across botany, folklore, anthropology, and religious studies, contributing some fifty pieces to Nature. In letters to the Shingon priest Doki Horyu he drew the "Minakata mandala": a web with no privileged center, every event tied to every other. The book borrows his "suiten" from this figure, recasting modern civilization as a human-centered mandala whose center AGI now destabilizes.
NAME-AFTERWORD-002: Herbert Simon | chapter=CH-AFTERWORD | review=approved | aliases=ハーバート・サイモン
context: One of the founders of artificial intelligence and the cognitive scientist and economist who formalized bounded rationality (Nobel Prize in Economics, 1978). The book invokes him as the theoretical pillar for two arguments: that even an AGI, however powerful its computation, must judge under finite resources and incomplete information, and that longtermism's expected-value calculus underestimates bounded rationality.
NAME-AFTERWORD-003: Varela | chapter=CH-AFTERWORD | review=approved | aliases=ヴァレラ
context: Francisco Varela, who with Thompson and Rosch proposed enactivism (1991), rejecting the classical computationalist view of cognition as information processing inside the brain and recasting it as active bodily interaction with the environment. Through autopoiesis with Maturana — the self-producing closure of living systems — he also contributed to the theoretical convergence on the inseparability of subject and environment.
NAME-AFTERWORD-004: Maturana | chapter=CH-AFTERWORD | review=approved | aliases=マトゥラーナ
context: With Varela he proposed autopoiesis — the self-producing closure of living systems — contributing to the theoretical convergence on the inseparability of subject and environment. Autopoiesis is also cited as an example of a theory that captured the essence of life but produced no quantitative predictions, remaining at the stage of intuition and concept; the free energy principle later supplied, after the fact, the mathematical language this convergence had lacked.
NAME-AFTERWORD-005: Pythagoras | chapter=CH-AFTERWORD | review=approved | aliases=ピタゴラス
context: Appears as the ancient Greek figure who found the structure of the cosmos in the order of numbers. Alongside Thales and Aristotle, he serves as an illustration of episteme — theoretical knowledge that seeks to understand the universal structure of the world through observation and argument — in the book's account of the three categories of human knowledge.
NAME-AFTERWORD-006: Tomohiro Inoue (井上智洋) | chapter=CH-AFTERWORD | review=approved | aliases=井上智洋
context: A macroeconomist who used the AK-type production function to analyze the post-AGI economy as a "purely mechanized economy"; the chapter's economic argument takes his work as its starting point and extends it with a second loop, the automation of AI-driven R&D. The book also relies on Inoue's framing and estimates in treating UBI not as welfare but as a structural device sustaining economic circulation, with its funding shifting from labor taxation to taxing the economic value generated by AI.
NAME-AFTERWORD-007: Audrey Tang | chapter=CH-AFTERWORD | review=approved | aliases=オードリー・タン
context: Taiwan's first Minister of Digital Affairs, co-proponent with Glen Weyl of Plurality, which locates society's basic unit in networks of relations rather than individuals. The book values this turn "from individuals to relations" as matching its theory of relational value, while noting it under-theorizes who controls the medium of collaboration; Chapter 11 reads the overlap of her "6-Pack of Care" with its three principles as constraints demanded by the AGI problem.
NAME-AFTERWORD-008: Kant | chapter=CH-AFTERWORD | review=approved | aliases=カント
context: The philosopher who, through the three Critiques (of Pure Reason, Practical Reason, and Judgment), rigorously separated the previously intertwined values of the true (science), the good (morality), and the beautiful (art), showing each has autonomy grounded in its own principle. The book positions this separation as the condition that allowed modern science to pursue truth as a distinct enterprise independent of moral norms and aesthetic judgment.
NAME-AFTERWORD-009: Takehiro Ohya (大屋雄裕) | chapter=CH-AFTERWORD | review=approved | aliases=大屋雄裕
context: Named in the afterword's acknowledgments as one of those who guided the book from the standpoint of political philosophy; the author records that the book's structure and argumentation took shape through such critical readings. In the main text he is the legal philosopher who serves as the principal reference for Chapter 11's account of "governance without persons" and the debate on AI legal personality.
NAME-AFTERWORD-010: Masaru Tomita (冨田勝, the author's mentor) | chapter=CH-AFTERWORD | review=approved | aliases=冨田勝(著者の師)
context: The author's mentor, portrayed as an artificial intelligence researcher trained under Herbert Simon who was then making a bold turn toward computational life science. In the Tomita laboratory, to which the author moved to study the simulation of life, the author developed E-Cell, the world's first cell simulator, at age twenty as a third-year undergraduate.
NAME-AFTERWORD-011: Merleau-Ponty | chapter=CH-AFTERWORD | review=approved | aliases=メルロ＝ポンティ
context: The philosopher who, in Eye and Mind, further concretized as the body the subject of aesthetic judgment that Kant had called subjective universality. His insight that aesthetic experience is rooted not in an abstract "mind" but in bodily perception (this eye, this hand, this ear) underpins the afterword's account of "subjective-embodied" value, which grounds the value of art in human bodily experience itself.

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