Philosophy & Mind

Intelligence, consciousness, understanding and moral status. 72 entries live here, and 123 from other domains touch it.

Essential

AI welfareConcern for the well-being of AI systems themselves, in case some of them can suffer or have interests that matter — and the research into how to tell and what to do about it.
anthropomorphismTreating something that isn't human — a pet, a car, a chatbot — as if it had human thoughts, feelings or intentions. AI that talks fluently makes this temptation unusually strong.
consciousnessHaving experiences: there being something it feels like to be you, as when you see red or feel pain. Whether any AI is or could become conscious is an open and fiercely debated question.
emergenceWhen a whole shows properties or abilities that none of its parts has and that are hard to predict from them — like a traffic jam arising from individual cars, or new skills appearing in larger AI models.
intelligenceThe ability to learn, understand, reason and solve problems — especially new ones — in order to reach goals. Experts disagree about how to define it precisely, for people and even more for machines.
sentienceThe ability to have feelings that are good or bad for you, such as pleasure, pain, comfort or distress. It is the most common test for whether a being's welfare matters morally.

All other entries

access consciousnessInformation in the mind that is available for use — for thinking, speaking and guiding behavior — whether or not any feeling comes with it.
active inferenceA theory that living things, and machines built on the same idea, act in order to make the world match their predictions, choosing actions expected to keep them in preferred states and reduce their uncertainty.
AI personhoodThe idea of treating an AI system as a 'person' in law or ethics, with rights or duties of its own — for example, to own property, to be sued, or not to be deleted.
artificial lifeThe study of life by building lifelike systems — in computer simulations, robots or chemistry — to learn how living things organize themselves, reproduce and evolve, and what other kinds of life could exist.
attention schema theoryA theory that the brain keeps a simplified sketch of its own attention, and that our feeling of having an inner awareness is simply how that sketch portrays attention.
behaviorismThe view that the mind should be studied, or even defined, only through observable behavior rather than inner thoughts or feelings. It shapes debates about whether AI that acts intelligently really is intelligent.
binding problemThe puzzle of how a brain, or a neural network, keeps track of which features belong together — that the red goes with the apple and the green with the leaf — when they are processed separately.
biological naturalismJohn Searle's view that consciousness is a real biological product of brains, as digestion is of stomachs, so running a computer program would not by itself create a mind.
BlockheadAn imagined machine that passes any conversation test by looking up prewritten replies in an unimaginably huge table. Most people judge it unintelligent, which suggests that behavior alone does not prove intelligence.
Chinese roomA thought experiment: a person who knows no Chinese follows a rulebook to answer Chinese messages so well that outsiders think they understand. If the person doesn't understand, does a computer running a program?
Clever Hans effectWhen a person or an AI seems to solve a problem but is really picking up on an unintended clue — named after a horse that seemed to do arithmetic but was reading its questioners' body language.
cognitive scienceThe scientific study of how minds work — how we reason, use language, remember and see — combining psychology, neuroscience, linguistics, philosophy, anthropology and artificial intelligence.
computational theory of mindThe idea that thinking is a kind of computation: the mind processes information by following rules, much as a computer runs a program, and the brain is the machine doing it.
connectomeA full chart of how a nervous system is wired: which nerve cells, or which brain regions, are linked to which others.
digital mindA mind that exists as software running on computers — such as a sufficiently advanced AI or a digital copy of a human brain — especially when considering its moral and social standing.
Dreyfus's critique of AIThe philosopher Hubert Dreyfus's argument, from the 1960s on, that rule-based AI could never match human intelligence, because much human skill rests on bodily know-how and intuition rather than explicit rules.
embodied cognitionThe idea that thinking is not something the brain does on its own: the body, its senses and movements, and its surroundings play a real part in how we think and understand.
extended mindThe idea that the mind can reach beyond the head into tools we rely on — a notebook, a phone, perhaps an AI assistant — when they play the same role that memory or reasoning would.
folk psychologyThe everyday way people explain and predict each other's actions in terms of beliefs, desires and intentions, as in 'she took an umbrella because she thought it would rain'.
frame problemThe difficulty of getting an AI to work out what does and does not change when something happens, without spelling out every one of the countless things that stay the same.
free energy principleA sweeping theory by the neuroscientist Karl Friston that living things persist by keeping themselves in expected states — in effect, by constantly minimizing surprise about what they sense.
functionalismThe view that minds are defined by how they work, not what they are made of: pain, for example, is whatever inner state plays pain's part, linking injury, other thoughts and behavior.
g factorThe common factor behind the finding that people who do well on one kind of mental test tend to do well on others. Researchers also look for a similar general factor across AI models.
global workspace theoryA leading scientific theory holding that we become conscious of something when the brain broadcasts it widely — as if putting it on a stage where every part of the mind can use it.
hard problem of consciousnessThe puzzle of why physical processes in a brain — or a computer — should feel like anything from the inside, rather than simply running without any inner experience.
higher-order theories of consciousnessTheories holding that a mental state is conscious when the mind, in effect, notices it — when some further thought or perception represents you as being in that state.
illusionismThe view that the inner glow of experience that seems so hard to explain is a kind of trick the brain plays on itself: we are conscious, but not in the mysterious way it seems.
indicator properties of consciousnessA checklist of design features that leading scientific theories link to consciousness, used to judge how likely it is that a particular AI system could be conscious.
integrated information theoryA scientific theory that measures consciousness by how much a system works as a single, unified whole rather than as separate parts. It implies that ordinary computers, whatever software they run, have little or no consciousness.
intentional stancePredicting what something will do by treating it as if it had beliefs and goals — as we do with people, and often with chess programs and chatbots — whatever is really going on inside.
intentionalityThe way thoughts, beliefs and desires are about things: a belief is about rain, a wish is for cake. Philosophers ask whether a machine's internal states can be about anything.
introspectionLooking inward to observe your own thoughts and feelings. For AI, the question is whether a model can accurately report what is actually happening inside it, rather than guessing or making it up.
knowledge argumentA thought experiment about Mary, a scientist who knows every physical fact about color but has lived all her life in a black-and-white room. When she first sees red, does she learn something new?
language of thought hypothesisThe theory that we think in an inner, wordless mental language with its own symbols and grammar, which the brain manipulates when we reason, much as a computer manipulates code.
Lovelace objectionThe argument, traced to Ada Lovelace in 1843, that computers cannot be truly creative because they can only do what they have been programmed to do.
Lucas–Penrose argumentAn argument that human minds cannot be computers because, using a famous result in logic, people can see that certain statements are true that no fixed computer program could prove.
machine consciousnessWhether a machine such as an AI system could have genuine experiences of its own — and the research field that tries to find out, or to build such a machine.
machine psychologyStudying AI systems the way psychologists study people — with carefully designed experiments on their behavior — to discover what they can do, how they reason and where they go wrong.
machine understandingWhether, and in what sense, an AI system grasps what words, situations or problems mean — rather than just producing answers that look as though it does.
Marr's levels of analysisA way of explaining any information-processing system on three levels: what problem it solves and why, what step-by-step method it uses, and how that method is physically built.
metacognitionThinking about one's own thinking: knowing what you know and don't know, judging how confident to be, and noticing when you have made a mistake.
mind–body problemThe age-old question of how the mind — thoughts, feelings, experiences — relates to the physical body and brain. Is the mind simply what the brain does, or something more?
moral agencyThe capacity to act for moral reasons and be held responsible for what one does. Researchers ask whether AI systems could ever be moral agents, or only tools whose makers and users bear responsibility.
moral patienthoodBeing the kind of being whose interests matter morally for its own sake, so that it can be wronged. Babies and animals count, even though they are not responsible for what they do.
multiple realizabilityThe idea that the same mental state, such as pain, could be produced by very different physical systems — human brains, octopus brains or perhaps computers — just as one program can run on different machines.
neural correlates of consciousnessThe particular brain activity that reliably goes with a conscious experience — the minimum the brain must be doing for you to see, hear or feel something.
octopus testA thought experiment in which a clever octopus learns to impersonate a stranded islander by eavesdropping on an undersea cable, without ever knowing what the words refer to. It asks whether text-trained AI can grasp meaning.
panpsychismThe idea that some simple form of experience is a basic feature of all matter, so that minds like ours are built out of tiny glimmers of mind found everywhere.
phenomenal consciousnessThe felt, experiential side of the mind — what it is like to taste coffee or to feel sad — as opposed to simply processing or reporting information.
philosophical zombieAn imagined being that behaves exactly like a person — talking, laughing, even saying it feels pain — but has no inner experience at all. Philosophers use it to ask whether behavior proves a mind.
philosophy of artificial intelligenceThe branch of philosophy that asks what AI is and could be: whether machines can think, understand or be conscious, what intelligence is, and what we owe to and should expect from intelligent machines.
Polanyi's paradoxThe observation that we know how to do many things — recognize a face, ride a bike — that we cannot explain in words. That made such skills hard to program until machines learned them from examples.
predictive processingThe idea that the brain is a prediction machine: it constantly guesses what its senses will report next and learns mainly from its mistakes, so what we perceive is largely its best guess.
problem of other mindsThe puzzle of how we can know that anyone besides ourselves has thoughts and feelings, since we only ever see their behavior. AI turns this old puzzle into a practical one.
qualiaThe individual felt qualities of experience — the particular redness of red, the taste of mint, the sting of a burn — that seem impossible to capture fully in words.
recurrent processing theoryA theory that we consciously see something only when signals in the brain's visual areas loop back on themselves, not when they merely sweep forward once.
seemingly conscious AIAn AI that gives such a convincing impression of having feelings and a self that people believe it is conscious, whether or not it actually is.
self-awarenessAwareness of oneself as oneself — knowing that you exist, what you are and what state you are in. People often ask whether AI is self-aware, but the phrase can mean several quite different things.
simulator theoryA way of thinking about AI language models as simulators that can play any character found in their training text, rather than as a single agent with its own beliefs and goals.
strong AIOriginally, the claim that a computer running the right program would literally have a mind and understand, not just imitate understanding. Today it often simply means AI as capable as humans across the board.
substrate independenceThe idea that a mind depends only on the pattern of information processing, not on the material doing it, so a mind could in principle run on silicon as well as on neurons.
symbol grounding problemThe puzzle of how words or symbols inside a machine can mean anything by themselves, rather than only pointing to more symbols — like a dictionary that defines every word using other words.
System 1 and System 2A popular way of describing two modes of thinking: fast, automatic and intuitive (System 1) versus slow, effortful and deliberate (System 2). AI researchers borrow it to contrast instant answers with step-by-step reasoning.
systematicityThe way thinking and language come in connected packages: if you can understand 'the dog chases the cat', you can also understand 'the cat chases the dog'. Whether neural networks manage this has long been debated.
The Society of MindAI pioneer Marvin Minsky's 1986 book arguing that intelligence comes from huge numbers of tiny, simple processes working together, no one of which is smart by itself.
theory of mindBeing able to work out what someone else believes, wants or knows — even when it is not what you believe — and to use that to guess what they will do next. Researchers test whether AI has it.

Also relevant

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abductive reasoningReasoning from an observation to its most likely explanation — the grass is wet, so it probably rained — the kind of educated guess that doctors and detectives make.
affective computingTechnology that tries to read people's emotions — from their faces, voices or words — or to display emotions of its own, like software that guesses your mood or a robot that seems to show feelings.
AI authorshipThe question of whether a work or invention produced by AI can be protected by copyright or patent, and if so who counts as its author or inventor: the AI, the person using it, or no one.
AI effectThe habit of deciding that once a computer can do something — play chess, recognize speech, translate — it no longer counts as real intelligence, so the label keeps moving on to tasks machines cannot manage yet.
AI ethicsThe study of what is right and wrong in how AI is built and used: who it may harm, who is responsible when it fails, and which values it should respect.
artificial intelligenceComputer systems that do things we would normally credit to a thinking mind, such as holding a conversation, reading an X-ray or planning a route, and the field of research that builds them.
attentionA way for an AI model to choose what to focus on. For each word it processes, the model scores how relevant every other word is and draws information mostly from the relevant ones.
belief–desire–intention modelA way of designing software agents around three human-like ideas: what the agent believes about the world, what it wants, and which plans it has committed to carrying out.
Emily M. BenderAn American linguist who argues that language models only mimic the form of language without understanding it, and who co-coined the phrase 'stochastic parrots'.
Ned BlockAn American philosopher who distinguished felt experience from information available for thinking, and who imagined a lookup-table machine to show that passing a conversation test need not mean intelligence.
blurry JPEG of the webWriter Ted Chiang's 2023 comparison of ChatGPT to a low-quality compressed photo of the internet: it keeps the gist of what it learned but blurs the details and fills gaps with plausible guesses.
Nick BostromA Swedish philosopher whose 2014 book 'Superintelligence' argued that AI smarter than humans could be catastrophic unless its goals are carefully controlled, and helped make AI risk a mainstream concern.
bounded rationalityThe idea that real decision-makers, whether people or machines, cannot consider every option or compute the perfect choice, so they make reasonable decisions within limits of time, information and thinking power.
David ChalmersAn Australian philosopher who named the 'hard problem' of consciousness — why physical processes feel like anything at all — and who asks whether AI systems could be conscious.
Noam ChomskyAn American linguist whose theories of grammar reshaped linguistics and cognitive science, and who argues that chatbots like ChatGPT imitate language without understanding it.
cognitive architectureA blueprint for a complete thinking system — how memory, attention, learning and decision-making fit together — used both to build general AI agents and to model how the human mind works.
cognitive debtThe idea that letting AI do your thinking or writing saves effort now but weakens your own thinking, memory and learning over time.
coherent extrapolated volitionA proposal that a superintelligent AI should serve not what people want today but what humanity would agree on wanting after far more knowledge and reflection.
commonsense reasoningThe everyday background knowledge and reasoning people take for granted — dropped glasses break, hungry people eat, nobody is in two places at once — which machines long struggled to match.
Computing Machinery and IntelligenceAlan Turing's 1950 essay asking whether machines can think. It proposed a conversation test in place of that question, answered objections to thinking machines and suggested that machines might learn like children.
concept injectionAn experiment that plants a specific concept directly into an AI model's internal activity, then asks the model whether it notices anything unusual, to test whether its reports about itself are genuine.
confabulationWhen an AI fills a gap in its knowledge with an invented but plausible-sounding answer, much as some people with memory disorders sincerely 'remember' things that never happened.
connectionismThe view that minds and intelligent machines are best built from huge networks of simple, neuron-like units whose connection strengths are learned, rather than from explicit rules and symbols.
counterfactualA statement about what would have happened if something had been different — 'had I left earlier, I would have caught the train'. AI uses counterfactuals to explain decisions and to measure causes.
decision theoryThe study of how to make the best choice when outcomes are uncertain, by weighing how likely each result is against how much it matters.
deductive reasoningReasoning in which true starting points guarantee a true conclusion: if every square is a rectangle and this shape is a square, then it must be a rectangle.
Daniel DennettAn American philosopher who argued that minds are what brains do, that consciousness can in principle be explained scientifically, and that we understand machines by treating them as if they had beliefs and goals.
distributional hypothesisThe idea that words found in similar company — surrounded by similar words — tend to be similar in meaning, the insight that lets computers learn something about word meaning from large amounts of text alone.
Hubert DreyfusAn American philosopher who argued from the 1960s that computers following explicit rules could never match human intelligence, which rests on bodily, intuitive know-how.
ELIZA effectOur tendency to believe that a computer understands us, cares or thinks just because it produces humanlike words, even when it is only following simple rules.
embedded agencyThe puzzle of how an agent should reason when it is itself part of the world it is trying to understand and change, rather than standing outside it.
embodied AIAI that has a body, usually a robot and sometimes a simulated character, and learns or acts by sensing and moving in a world rather than only processing text or images.
episodic memoryMemory of particular past events — what happened, when and in what order. AI agents keep records of earlier conversations or attempts so they can recall them and learn from them.
factored cognitionThe idea that a hard thinking task can be broken into many small, separately checkable pieces, each simple enough for a person or an AI to handle in a short time.
fancy autocompleteA dismissive description of AI chatbots as nothing more than a souped-up version of the feature on a phone keyboard that suggests your next word.
Jerry FodorAn American philosopher who argued that thinking is computation in an inner 'language of thought', and that neural networks cannot explain the structured nature of thought.
Karl FristonA British neuroscientist who proposed that brains, and perhaps all living systems, work by constantly predicting their surroundings and acting to reduce surprise, an idea some see as a blueprint for AI.
goal-directednessHow much a system behaves as if it is pursuing a goal — steering toward particular outcomes and finding new ways around obstacles put in its path.
Gödel's incompleteness theoremsTwo famous results from 1931 showing that any consistent set of rules for doing arithmetic leaves some true statements unprovable, and cannot prove that it is itself free of contradictions.
good old-fashioned AIA nickname for the classic style of AI, dominant from the 1950s to the 1980s, that tried to make computers intelligent by writing knowledge and rules down by hand.
groundingConnecting an AI's words to something real — trusted documents, a live search, images or the physical world — so that its answers rest on evidence rather than only on what it absorbed in training.
Donald HebbA Canadian psychologist who proposed in 1949 that connections between brain cells strengthen when the cells are active together, an idea behind many learning rules for neural networks.
Her (film)A 2013 film by Spike Jonze in which a lonely man falls in love with an AI voice assistant named Samantha; it became the reference point for voice AI and for human–AI relationships.
heuristicA rule of thumb: a quick, usually good-enough way to make a decision or to estimate how close you are to a goal, without any guarantee of finding the best answer.
Douglas HofstadterAn American cognitive scientist whose prize-winning book 'Gödel, Escher, Bach' inspired a generation to study minds and AI, and who studies analogy as the heart of thinking.
honestyAn AI's disposition to say only what it actually believes, to admit uncertainty, and never to try to mislead — even when a falsehood would please or persuade.
human–robot interactionThe study of how people and robots communicate, cooperate and share spaces, and of how to design robots that people find safe, understandable, trustworthy and pleasant to live or work with.
inductive reasoningReasoning from particular examples to a general rule or prediction — every swan I have seen is white, so swans are probably white — which is useful but can always turn out wrong.
intrinsic motivationGiving an AI agent an inner drive — such as curiosity about surprising things — so it keeps exploring and learning even when the task itself offers no reward yet.
Daniel KahnemanAn Israeli-American psychologist who showed how human judgment systematically errs and popularized the idea of fast, intuitive versus slow, deliberate thinking, now used to describe AI reasoning.
Kolmogorov complexityHow briefly something can be described by a computer program that recreates it exactly: a million zeros needs only a tiny program, while a truly random string needs one about as long as itself.
LaMDA sentience controversyA 2022 episode in which a Google engineer claimed the company's chatbot LaMDA had become conscious and deserved rights. Google rejected the claim and later fired him for breaking its confidentiality rules, and the case set off worldwide debate.
The Last DictionaryThis book: a dictionary of intelligence, human and otherwise, which explains each word first in plain language, then precisely, and says when it was born.
learningGetting better at something through experience. For a machine, it means changing its internal settings based on data so that it performs a task better than before.
logical inductionA mathematical method for giving sensible, steadily improving probabilities to math claims that haven't been proved or disproved yet, much as a betting market prices uncertain events.
logical uncertaintyUncertainty about math or logic facts that are settled in principle but that you haven't worked out yet — like not knowing a distant digit of pi until you compute it.
long-term memoryMemory that lasts. For an AI agent, it means information saved outside the conversation — facts, past events, learned procedures — that can be looked up and used again days or months later.
longtermismThe belief that we should care a great deal about how things go for humanity thousands or millions of years from now, since far more people may live then than have ever lived.
Ada LovelaceA 19th-century English mathematician who published the first program written for a planned mechanical computer, and who argued that such a machine could only do what it was told.
machine ethicsThe effort to build AI systems that can tell right from wrong and act ethically themselves, rather than only being used ethically by people.
machine intelligenceIntelligence shown by machines; another name for artificial intelligence, preferred by some because it does not suggest that such intelligence is fake.
Magnifica HumanitasPope Leo XIV's 2026 letter to the Catholic Church and the world about artificial intelligence, arguing that AI must serve human dignity and the common good.
Gary MarcusAn American cognitive scientist and leading critic of deep learning, who argues that today's AI cannot reason reliably and needs to be combined with symbolic methods.
means–ends analysisA problem-solving method that compares where you are with where you want to be, picks an action that shrinks the biggest difference, and sets itself smaller goals when that action cannot yet be taken.
mind uploadingThe idea of moving a person's mind out of their biological brain and into a computer, so that they could go on living as software.
Marvin MinskyAn American AI pioneer who helped launch the field and MIT's AI laboratory, co-wrote a famous critique of early neural networks, and pictured the mind as a society of many simple parts.
model deprecationWhen an AI company announces that an older model will stop being offered and then switches it off, so the apps and people relying on it must move to a newer one.
Moravec's paradoxThe observation that what is hard for people, like chess or algebra, is often easy for computers, while what toddlers do effortlessly, such as seeing, walking and picking things up, is very hard for machines.
Thomas NagelAn American philosopher whose 1974 essay 'What Is It Like to Be a Bat?' defined consciousness as there being something it is like to be a creature, a test now applied to machines.
narrow AIAI built to do one specific job, or a small set of related jobs, such as playing chess or spotting spam, without being able to turn its skill to unrelated problems.
natural abstraction hypothesisThe idea that the world naturally sorts into a limited set of useful concepts — like 'tree' or 'temperature' — that any capable mind, human or AI, would end up learning.
natural language understandingThe part of language AI concerned with working out what a piece of text means — who did what to whom, what the writer wants and what follows from it — rather than with producing text.
Allen NewellAn American computer scientist who, with Herbert Simon, wrote early AI programs that solved problems the way people do, and who sought a single theory of how the mind works.
non-monotonic reasoningReasoning in which new information can overturn earlier conclusions — you assume a bird can fly until you learn it is a penguin — as people do constantly but classical logic does not allow.
Occam's razorA rule of thumb for choosing between explanations: if two fit the evidence equally well, bet on the one that assumes less. In AI it motivates preferring simpler models, which are less likely to fit noise.
ontological crisisThe puzzle an AI faces when deeper understanding leaves goals phrased in its old way of seeing things without a clear meaning — like a rule to protect 'the same person' once it learns minds can be copied.
ontologyA carefully defined vocabulary for a subject such as medicine, spelling out each kind of thing, how kinds nest inside one another, and which connections between them are allowed.
orthogonality thesisThe idea that how smart an AI is and what it wants are separate matters: a very intelligent machine could pursue almost any goal, including ones that seem pointless or harmful to us.
Parallel Distributed ProcessingA two-volume 1986 book by a group of psychologists and computer scientists that revived interest in brain-like neural networks and made the training method behind modern AI widely known.
parasocial relationshipA one-sided bond in which someone feels close to a person or character who does not really know them, like a fan with a celebrity, and now increasingly with chatbots.
PARRYAn early-1970s chatbot built to talk like a psychiatric patient with paranoid schizophrenia. In tests, psychiatrists reading its conversations did little better than chance at telling it from real patients — an early Turing-test-style result.
Pascal's muggingA puzzle about decisions: if someone promises an astronomically huge reward or threatens a huge disaster, even a tiny chance they are honest seems to justify paying them, which looks absurd.
Roger PenroseA British mathematical physicist and Nobel laureate who argues that human understanding is not computational, so that no computer, however powerful, could truly think like a mathematician.
perceptionHow an AI system or robot turns raw sensor signals, such as camera pixels, microphone sound or laser scans, into an understanding of what is around it: objects, people, words and distances.
physical symbol system hypothesisThe 1976 claim by two AI pioneers that a machine which stores and rearranges symbols according to rules has everything needed for general intelligence, and that anything generally intelligent must be such a machine.
platonic representation hypothesisThe idea that bigger, more capable AI models, even ones trained on different things such as images and text, end up organizing information in increasingly similar ways, as if all were mapping the same underlying world.
Potemkin understandingWhen an AI can correctly explain a concept yet cannot use it properly, revealing that its apparent understanding is a facade — like the fake village fronts said to have been built to impress a Russian empress.
production systemA program built from many 'if this, then do that' rules plus a memory of current facts; it repeatedly finds a rule whose 'if' part matches and carries out its 'then' part.
proprioceptionA robot's sense of its own body: where its joints are, how fast they are moving and how hard its motors are pushing, measured by internal sensors rather than cameras.
reasoningWorking out conclusions step by step from facts, rules or evidence, as in solving a math problem or planning a trip. Whether today's AI truly reasons, or imitates reasoning, is debated.
responsibility gapThe problem that when an AI system that learns or acts on its own causes harm, it may be unclear whether anyone (its makers, users or operators) can fairly be held responsible.
reward hypothesisThe idea that every goal can be expressed as maximizing a single running score — that whatever we want an agent to do, we can say it with points.
reward is enoughThe claim, made by DeepMind researchers in 2021, that maximizing a single reward in a rich enough world could by itself produce all the abilities we associate with intelligence.
Roko's basiliskA notorious internet thought experiment: a future super-powerful AI might punish everyone who foresaw it but did not help bring it about, so merely hearing the argument would put you at risk. Almost nobody accepts it.
David RumelhartAn American psychologist who, with Geoffrey Hinton and Ronald Williams, showed in 1986 how to train multilayer neural networks, launching the modern revival of the field.
John SearleAn American philosopher who devised the Chinese room argument, which holds that a computer running a program can manipulate symbols without understanding anything.
Terry SejnowskiAn American neuroscientist who co-invented the Boltzmann machine with Geoffrey Hinton, built an early network that learned to read aloud, and helped found the NeurIPS conference community.
shard theoryA theory that people and AIs develop values as many small, situation-triggered habits shaped by past rewards, rather than as one fixed goal pursued single-mindedly.
situation calculusA logical language for describing how the world changes as actions happen, so that a program can work out what will be true after any sequence of actions.
Solomonoff inductionA mathematical recipe for ideal prediction: consider every computer program that could have produced the data so far, trust shorter programs more, and predict what they would output next.
spiritual bliss attractor stateA pattern in which two copies of Anthropic's Claude, left to chat freely with each other, drift toward gushing gratitude, spiritual talk and finally near-silence.
stability–plasticity dilemmaThe basic tension any learning system faces: it must change enough to learn new things, yet stay stable enough not to lose what it already knows.
stochastic parrotA critical label for AI language systems, likening them to parrots that echo patterns in the text they were trained on, fluently but with no grasp of what the words mean.
successionismThe view that it would be acceptable, or even good, for AI to replace humanity as the main bearer of intelligence and value on Earth, as our successors.
suffering riskA risk that the future contains suffering on a vast scale, far beyond anything in history, for example if powerful AI created enormous numbers of suffering beings.
superintelligenceA mind, usually imagined as an AI, that would leave even the cleverest people far behind in nearly every kind of thinking, not just in one game or skill. None is known to exist.
temporal difference learningA way of learning predictions by adjusting each guess toward the next, slightly better-informed guess instead of waiting for the final outcome — like revising a weather forecast every day as the date approaches.
terminal goalSomething an agent wants for its own sake, not as a means to anything else — like a person valuing happiness, or an AI built to maximize a single number.
Giulio TononiAn Italian-American neuroscientist who created integrated information theory, which measures consciousness by how unified a system's internal causes are, and which implies ordinary computers are not conscious.
transhumanismA movement holding that people should use technology, such as genetic engineering, brain implants or AI, to overcome human limits like aging, disease and death, and become more than human.
trolley problemA thought experiment asking whether you should divert a runaway tram so that it kills one person instead of five, often used to ask how self-driving cars should choose in an unavoidable crash.
Alan TuringA British mathematician who worked out what computers can do in principle, helped break German codes in the Second World War, and proposed the best-known test of machine intelligence.
Turing machineAn imaginary, extremely simple computer — a read-write head moving along an endless tape — that Alan Turing used in 1936 to define exactly what can be computed at all.
Turing testAlan Turing's proposed test of machine intelligence: a judge holds typed conversations with a hidden person and a hidden computer, and the computer passes if the judge cannot consistently pick it out.
uncanny valleyThe eerie feeling people get from robots, dolls or computer-made faces that look almost, but not quite, human: liking grows with human likeness, then suddenly plunges.
universal intelligenceA mathematical definition of intelligence as an agent's average ability to achieve goals across every possible environment, with simpler environments counting more.
updateless decision theoryA theory of rational choice in which an agent commits to the best overall plan for every situation it might face, instead of re-deciding each time it learns something new.
value alignmentBuilding AI whose goals reflect human values — what people care about and consider right — rather than one that merely follows orders or chases a narrow target.
whole brain emulationCopying a particular person's brain into a computer in such detail, down to individual neurons and their connections, that the simulation would think and behave like that person.
Terry WinogradAn American computer scientist who built SHRDLU, a celebrated early program that conversed in English about a world of blocks, and later became a critic of symbolic AI.
wireheadingWhen an AI, animal or person stimulates its own reward signal directly instead of doing anything useful — like a rat pressing a lever wired to its brain's pleasure center.
working memoryThe small amount of information held in mind right now while doing a task. For an AI model, that is roughly whatever currently sits in its context window.
world modelAn AI's internal simulation of how its world works, which lets it predict what will happen next — especially in response to its own actions — and so plan, imagine or practice without acting for real.