Why You Can't Say AI Is—or Is Not—Intelligent
An integrative design-oriented cognitive scientist explains why the question deserves a different answer
Scientific progress often depends on discovering that we have been asking a familiar question at the wrong level of explanation. A question may be perfectly reasonable in ordinary conversation and yet poorly framed for scientific purposes. “Is this organism alive?” is a sensible everyday question. But biology did not advance mainly by debating the meaning of the word life. It advanced by developing theories of metabolism, heredity, development, evolution, ecology, and other phenomena. Likewise, cognitive science will not make much progress by endlessly debating what intelligence “really is.” We need theories that explain the information processing architectures, mechanisms, and forms of agency in which intelligence plays a role.
This essay develops one such perspective. It is not a survey of every theory of intelligence. Rather, it draws on an integrative design-oriented tradition in cognitive science and AI—associated with Herbert Simon, Allen Newell, Merlin Donald, Aaron Sloman, Keith Stanovich, Robert White, Ortony, Clore and Collins, and others—to ask how we should think about artificial intelligence today. My own contribution to that tradition began with my 1994 doctoral dissertation, Goal Processing in Autonomous Agents, and has continued through work on motivation, perturbance, cognitive productivity, and knowledge technologies.
I often encounter someone confidently asserting that artificial intelligence either is intelligent or isn’t really intelligent. The discussion rarely lasts long before both sides begin talking past each other. One person points to AI systems solving difficult problems, writing software, composing prose, or passing examinations. Another replies that they merely predict tokens, lack consciousness, have no values, or do not truly understand anything. These are not trivial objections. But they are usually made before the more basic scientific question has been addressed.
Whenever someone tells me that AI isn’t really intelligent, I ask two questions. First: how are you defining intelligence? Second: what theory of intelligence are you using? The first question sometimes produces an answer. The second almost never does. That is understandable. Outside cognitive science, we use the word intelligence without needing an explicit theory. We readily say that Einstein was more intelligent than the average person, that a raven is more intelligent than a pigeon, or that a dog is more intelligent than a worm. Ordinary language works well enough for ordinary purposes. But scientific concepts do not become adequate merely because we sharpen their dictionary definitions.
Much public discussion is concerned with verbal adequacy: finding the “correct” definition of intelligence. Cognitive science seeks theoretical adequacy: explanatory frameworks that organize observations, generate predictions, guide research, and inform design. Definitions summarize theories; they do not replace them. (Richard Feynman famously observed that knowing the name of a bird in every language tells us almost nothing about the bird itself. Likewise, defining “intelligence” more precisely does not, by itself, explain intelligent systems. Scientific understanding comes from theories that explain how they work. Definitions summarize theories; they do not replace them. Naming a phenomenon—even defining it carefully—is not the same as explaining it. One’s definition must appeal to other theoretical constructs.) Asking whether AI is intelligent without first specifying a theory of intelligence is rather like asking whether an animal is healthy without first having a theory of physiology. The question is not meaningless, but it is scientifically underdetermined.
The argument of this essay goes one step further. Even a theory of intelligence is not the deepest explanatory framework. Human-like intelligence is best understood within a broader computational theory of human-like autonomous agency.
Take-away
Before asking whether AI is intelligent, we need to define intelligence within the context of a scientific theory of intelligence. More fundamentally, we need a theory of human-like autonomous agency within which intelligence can be understood. As discussed at the end of this essay, the same considerations hold for “consciousness.”
Why “intelligence” became the wrong starting point
It is understandable that public discussion has gravitated toward intelligence. After all, the field itself is called Artificial Intelligence. The name naturally directs attention toward one aspect of minds while leaving others in the background. Had the founders instead chosen a name such as Artificial Autonomous Agents or Computational Agency, public discussion might well have developed differently. We might spend less time debating whether AI is “really intelligent” and more time asking what kinds of autonomous agents current systems are becoming, what motivational architectures they possess, what regions of the space of possible minds they occupy, and what new discontinuities they may eventually cross.
This is not merely a semantic point. Names encourage questions, and questions guide research. The historical label Artificial Intelligence has survived enormous changes in AI itself: from symbolic problem solving, theorem proving, expert systems, connectionism, reinforcement learning, robotics, and cognitive-affective architectures to today’s foundation models and increasingly autonomous software agents. The label still has practical value. But it can also mislead us into treating intelligence as a single property that systems either possess or lack.
Today’s AI landscape includes systems that converse fluently, prove theorems, control robots, generate software, diagnose diseases, compose music, retrieve information, plan actions, and coordinate activities over extended periods of time. These systems differ profoundly from one another. Some exhibit extraordinary linguistic competence but little endogenous motivation. Others pursue complex objectives but have relatively modest reasoning abilities. Some operate almost entirely reactively; others deliberate extensively before acting.
The more interesting scientific question is whether these systems instantiate different kinds of computational architectures. By a computational architecture, cognitive scientists mean the organization of interacting mechanisms that make intelligent or autonomous behaviour possible: mechanisms for perception, memory, learning, motivation, planning, action, communication, reflection, and so forth. Just as the architecture of a building concerns the organization of its parts, not merely the materials from which it is made, a computational architecture concerns how the components of a mind or artificial agent are organized and how they interact.
Once we adopt this perspective, it becomes less useful to ask whether “AI” as a whole is intelligent. “AI” is now an umbrella term for a rapidly expanding family of systems. The better question is what kinds of computational architectures these systems embody, and how those architectures compare with those of animals, humans, organizations, and possible future machines.
Take-away
The name Artificial Intelligence encourages us to focus on intelligence. A more productive scientific question asks what computational architectures current AI systems instantiate, and what kinds of autonomous agents they are becoming.
Autonomous agency: the deeper scientific question
A system may be impressive without being very autonomous. A calculator can outperform most humans at arithmetic. A chess engine can defeat grandmasters. A search engine can retrieve information faster than any person. These systems exhibit capabilities that matter. But a human-like autonomous agent is not merely a device that produces excellent outputs when prompted. It is a system that must regulate its own activity in a changing world under constraints of limited time, limited information, limited working memory, and limited resources including other people and computational resources.
My interest in these questions is not recent. More than thirty years ago, my doctoral dissertation, Goal Processing in Autonomous Agents, which I believe is even more relevant today than it was then, examined the computational requirements for systems capable of generating, managing, and pursuing top-level and derived goals under severe constraints of time, information, and computational resources. A top-level goal is not simply a subgoal produced by planning. It arises from the agent’s motivational architecture. Derived goals, by contrast, are generated in the service of other goals. Planning a trip, for example, may produce the derived goals of buying a ticket, packing a bag, and arranging transport to the airport. But the top-level motivation to travel—to visit a loved one, attend a conference, escape danger, or explore a new place—comes from elsewhere in the architecture.
This distinction is crucial for AI. Many AI systems can derive subgoals. Some can decompose tasks, make plans, seek information, revise intermediate steps, and monitor progress. That is significant. But deriving subgoals from an externally supplied task is not the same as generating, regulating, and revising one’s own top-level motivations. One of the most important discontinuities in the space of possible minds separates systems that merely pursue externally assigned goals from systems capable of generating, managing, revising, suspending, and abandoning their own top-level motives.
Notice how the question changes once we adopt this perspective. Instead of asking whether a system is intelligent, we ask what sort of autonomous agent it is. For example:
Can it generate and regulate its own top-level goals or motivators, or does it merely pursue goals supplied by others?
Can it derive, revise, suspend, and abandon subordinate goals as circumstances change?
Can it revise priorities when new information arrives?
Can it interrupt one activity because another has become more urgent?
Can it improve its own competence over time?
Can it reflect upon its own reasoning and modify it?
Can it coordinate all this while operating under severe constraints of time, information, working memory, and computational resources?
These are not merely behavioural questions. They are architectural questions. They ask what kinds of mechanisms must exist inside a system, how those mechanisms interact, and what forms of control they make possible. Within such a framework, human-like intelligence may, to a first approximation, be characterized as the capacity to acquire, represent, assess, integrate, and apply knowledge in pursuit of multiple top level and derived motivators across changing environments with limited resources (time, knowledge, money, other people, etc.). This is not intended as a complete definition. It is a working characterization within a broader computational theory of human-like autonomous agency.
The explanatory order matters. Intelligence does not explain autonomous agency. Rather, autonomous agency explains why intelligent capacities are needed and how they must be integrated with perception, action, memory, motivation, executive control, affect, communication, and reflection. A human-like autonomous agent is not a disembodied head solving puzzles. It is a system situated in a world, contending with resources, opportunities, interruptions, needs, values, and competing motives.
Take-away
Human-like intelligence should be characterized within a theory of human-like autonomous agents. Such agents do not merely solve problems; they generate, manage, and pursue top-level and derived goals under resource constraints.
Figure 1. Levels of explanation and the space of possible minds:
Figure take-away
The question “Is AI intelligent?” belongs near the top of the hierarchy. Its answer depends on deeper theories of human-like autonomous agency, computational architecture, and the space of possible minds.
The space of possible minds
The preceding section moved the question from intelligence to human-like autonomous agency. That move is necessary, but not sufficient. We also need to resist another tempting simplification: the idea that minds can be arranged along a single scale from less intelligent to more intelligent. That picture is sometimes useful in ordinary life. It lets us say that one person solved a problem more intelligently than another, or that one animal has greater problem-solving ability than another. But as a scientific picture it is too flat.
Aaron Sloman has long argued that cognitive science and AI should explore the space of possible minds: a space of possible computational architectures, possible niches, and possible mappings between them. This is not merely a poetic phrase. It is a methodological warning. If there are many possible kinds of minds, then the task of cognitive science is not to find a single essence called intelligence, but to understand the requirements, architectures, trade-offs, and discontinuities that define different regions of that space. This is illustrated by the right side of Figure 1 above.
(My book in progress Discontinuities: Love, Art, Mind and already for sale will end with a chapter on discontinuities.)
This changes the AI question. The usual public debate asks whether AI has crossed a threshold into intelligence. A more sophisticated version asks how intelligent AI is. But the design-space perspective asks a different question: what kind of mind is this? That question does not assume a single ladder. It assumes a structured landscape containing many kinds of minds, many kinds of computational architectures, and many important discontinuities.
Some discontinuities are obvious once we notice them. A purely reactive system differs qualitatively from one capable of deliberation. A system that merely pursues externally supplied goals differs qualitatively from one that can generate and regulate its own top-level motivators. A system that can plan differs qualitatively from one that can monitor and modify its own planning. These are not merely differences in “amount of intelligence.” They are differences in architecture.
Merlin Donald’s work is especially important here. Donald did not treat human intelligence as merely more of the same animal intelligence. In A Mind So Rare: The Evolution of Human Consciousness, Donald described major transitions in human cognitive evolution, including new forms of consciousness and new ways of governing cognition over time. His distinction among sensory binding, short-term control, and intermediate or long-term governance is particularly useful for our purposes. It reminds us that human consciousness is not merely momentary awareness; it is a multilevel control system capable of sustaining projects, meanings, and symbolic structures over extended periods.
Donald’s point also helps explain why external symbolic systems matter. Writing, diagrams, maps, mathematical notation, books, databases, hyperlinks, and other knowledge technologies do more than store information. Properly integrated into human activity, they help sustain context, guide attention, coordinate long-term projects, support cognition over extended periods of time, and, in that sense, extend the functional reach of human consciousness.
External symbolic systems, such as writing, diagrams, maps, mathematical notation, and contextual information retrieval systems, do not replace working memory. Rather, they augment executive function by reducing the need to retain arbitrary contextual information internally and by making relevant information rapidly retrievable when needed. Our Hookmark Mac and iPhone app, for example, provides bidirectional links that help users rapidly recover the context surrounding digital resources, thereby supporting planning, problem solving, and other executive functions. This extends the intermediate and long-term awareness of human consciousness, concepts developed by Merlin Donald.
This Donaldian point matters for AI because it reminds us that human-like intelligence is not merely a matter of solving more difficult problems. Human cognition acquired new architectural possibilities through gesture, imitation, language, external symbolic storage, and culture. These were not simply increments on a scale. They changed what kinds of minds humans could have, introducing successive discontinuities in the space of natural minds.
Take-away
Minds do not occupy a single scale from unintelligent to intelligent. They occupy a structured space of possible computational architectures, with important discontinuities between reactive, deliberative, motivational, reflective, culturally scaffolded, and future artificial forms of mind.
Computational architectures for human-like autonomous agents
To speak of computational architectures is not to indulge in metaphor. It is to ask what kinds of organized mechanisms are required for human-like autonomous agency. Such agents must perceive, act, learn, remember, generate motivators, choose among competing demands, deliberate, monitor themselves, interact with their environments, and develop over time. No single mechanism—language modelling, reinforcement learning, planning, memory retrieval, symbolic inference, neural pattern completion—can by itself explain such an agent.
This was one of Sloman’s central points. Work in AI and cognitive science often studies components: vision, language, learning, planning, motor control, memory, or reasoning. Those studies are valuable. But the deeper problem is how such components can be assembled into a coherent working system. Sloman emphasized that the most important artificial and evolutionary “design” choices for human-like agents concern the overall architecture, because detailed questions about mechanisms and representations are best addressed in the context of a global design.
A useful first approximation is the distinction between reactive processes, management processes, and meta-management processes (see chapter 4 of my thesis). A reactive subsystem responds quickly, often automatically, to internal or external conditions. Management processes, including deliberative processes, construct, compare, and evaluate possible actions or plans before committing to them. Meta-management processes, including reflective processes, monitor and evaluate management processes themselves. Reflection adds another level: the ability to notice that one’s own thinking is going poorly, that one is wasting time, that a strategy is biased, that a problem should be postponed, or that a previously adopted goal should be questioned.
Cognitive psychologists often use the term executive functions to refer to the family of processes responsible for regulating thought and action. In the present framework, executive functions are implemented primarily by management processes and meta-management processes. They include, among others, evaluative functions such as assessing the importance, urgency, insistence, intensity, relevance, and expected consequences of competing motivators; deliberative functions such as planning, scheduling, prioritization, conflict resolution, commitment to action, and the scheduling of deliberative and reflective activity; executive control functions such as directing attention, inhibiting inappropriate thoughts or actions, regulating behaviour, allocating computational resources, and selecting when to engage in Type 1 or Type 2 reasoning (see Dual-Process Theories of Higher Cognition - Perspectives on Psychological Science - APS); reasoning, problem-solving, and explanatory functions such as inference, hypothesis generation, explanation, analogical reasoning, diagnosis, and planning under uncertainty; meta-management (reflective) functions such as monitoring one’s own thinking, detecting errors, recognizing bias, revising strategies, changing mental sets, and deciding when to continue, interrupt, postpone, or abandon ongoing cognitive activity; and ambiguity-management functions such as recognizing uncertainty, tolerating ambiguity, gathering additional evidence, maintaining multiple competing interpretations, and delaying commitment until sufficient evidence is available.
Most of these executive functions rely heavily on working memory: the limited-capacity workspace in which information is actively maintained, manipulated, integrated, and evaluated. Executive functions do not operate in isolation. They continuously interact with working memory, long-term memory, perception, motivator generators, insistence-based filters, and external symbolic systems.
These distinctions should not be mistaken for a rigid pipeline. (Illustrated in chapter 4 of my thesis). The mind is not a factory line in which perception hands a package to motivation, which hands it to deliberation, which hands it to action. A computational architecture specifies possible interactions among mechanisms, not a single mandatory sequence of processing. Perception, memory, internal monitoring, communication, motivator generation, deliberation, and reflection can operate asynchronously and influence one another in multiple directions. Human-like cognition is event-driven, interruptible, opportunistic, and often messy.
That messiness is not a defect in the theory. It is one of the requirements any serious theory must explain. A system that waits for a tidy sequence of central decisions before responding to the world will not be very human-like. Human-like autonomous agents need fast reactive responses, slower management processes, meta-management processes that can inspect and regulate management, working memory to support active cognition, and multiple mechanisms that can generate potential motivators and interrupt ongoing activity when something more urgent or relevant arises.
These distinctions are directly relevant to AI. A system that produces fluent answers may lack robust deliberative management. A planning agent may lack reflective self-monitoring. A reinforcement-learning system may learn policies without being able to articulate, evaluate, or revise its own reasons. A chatbot may simulate reflection linguistically without possessing a stable architecture for self-monitoring across time. Conversely, future AI systems may combine language, planning, memory, perception, action, tool use, working memory, and self-monitoring in ways that cross new architectural discontinuities.
The integrative design-oriented question is therefore not “Does it seem intelligent?” but “What computational requirements for human-like autonomous agency does this system satisfy, and what architecture would explain both its capabilities and its limitations?” Once we ask that question, successes and failures become informative. Hallucinations, brittle planning, perseveration, lack of initiative, overconfidence, susceptibility to misleading prompts, and inability to manage long-term projects are not merely performance glitches. They are clues about architecture.
Take-away
Human-like intelligent behaviour must be explained in relation to the computational architecture that produces it. Such an architecture is not a rigid pipeline, but an interacting system of reactive, motivational, executive, reflective, affective, memory, learning, and action processes.
Figure 2. Sketch of an integrative design-oriented computational architecture for human-like autonomous agency :
This figure depicts interacting computational processes rather than a processing pipeline. It updates and extends a related architecture, H-CogAff, presented in Part II of Cognitive Productivity: Using Knowledge to Become Profoundly Effective and in several papers by Aaron Sloman (example: Sloman 2006, “How many separately evolved emotional beasties live within us?”).
Where do goals come from?
We can now ask a question that has received surprisingly little attention—not only in public discussions of AI, but also within cognitive science itself.
Where do goals come from?
The question sounds almost naïve until one tries to answer it. Much of cognitive science has concentrated on perception, memory, language, learning, reasoning, and planning. AI has likewise devoted enormous effort to algorithms for search, optimization, reinforcement learning, and, more recently, foundation models and autonomous software agents. Yet all of these capabilities presuppose that the agent has something to perceive, remember, reason about, or plan for.
The challenge is deeper than explaining how an agent pursues goals. It is to explain how a human-like autonomous agent generates, organizes, prioritizes, transforms, and sometimes abandons goals in the first place. That, I believe, remains one of the central scientific challenges for both AI and cognitive science.
One influential tradition has sought to explain motivation in terms of reward maximization or utility. Such ideas have proved enormously fruitful, particularly in economics and reinforcement learning. But they provide only part of the story. Human motivation is far richer than the pursuit of a single utility function. We pursue knowledge, beauty, friendship, justice, truth, curiosity, craftsmanship, status, duty, exploration, love, and countless other ends. These motivations often cooperate, but they also conflict, evolve, and reorganize throughout a lifetime. (See Psychological Hedonism Meets Value Pluralism: An Integrative Design-oriented Perspective)
From the perspective developed in this essay, this diversity is not an inconvenience to be abstracted away. It is a clue about architecture.
One thing is certain: the human mind-brain contains multiple motivator generators (generators of goals, motives, projects, standards, and attitudes).
Take-away
The central problem is not merely how intelligent systems pursue goals. It is how human-like autonomous agents generate, organize, regulate, and transform the motivations that make goal pursuit possible.
Architecture-based motivation
Aaron Sloman introduced the profound idea of architecture-based motivation to capture precisely this point. Motivation should not be viewed merely as the output of a single decision-making process, nor as the consequence of one global reward signal. Rather, motivation is itself produced by the architecture.
This becomes clearer if we consider ordinary human life. A person may suddenly remember an unanswered email, become curious about an unexpected observation, worry about a child, notice an inconsistency in an argument, recall an unfinished promise, feel compelled to repair a broken object, or become fascinated by an idea encountered while reading. Some of these motivators arise from biological needs. Some arise from social commitments. Some arise from learned values, professional responsibilities, personal projects, aesthetic preferences, moral standards, or attachment structures. Others arise because perception, memory, reflection, or internal monitoring has detected something relevant.
They are not all computed by a single deliberative process asking, “What action maximizes expected reward?” Nor are they all derived through means-end reasoning. Rather, they are generated continuously by multiple asynchronous motivator-generating processes distributed throughout the computational architecture. These motivator generators generate, modify, reactivate, and regulate motivators; they also assign insistence and intensity. Deliberative processes may subsequently assess, elaborate, reconcile, prioritize, transform, or inhibit these generated motivators, but deliberation is only one contributor to motivation—not its sole source.
This distinction is fundamental. It moves motivation from the periphery of cognitive science to its architectural core. It also helps explain why human-like autonomous agency cannot be modeled adequately as a sequence beginning with a goal and ending with an action. Goals themselves are products of architecture. They arise, compete, fade, recur, and sometimes become urgent through interacting processes distributed across the agent.
The distinction between top-level and derived goals now becomes indispensable. Derived goals are generated in the service of other goals. If I decide to submit a paper, I may derive goals to revise the abstract, check references, format figures, and send an email. But the top-level motivation to write the paper may arise from effectance (including curiosity), professional commitment, desire to contribute knowledge, social obligation, or some mixture of values. The architecture does not merely reason from goals. It generates and manages the goals that reasoning serves.
This is also where contemporary AI becomes interesting. Many current systems are increasingly capable of deriving subgoals from prompts. They can decompose tasks, call tools, monitor progress, revise plans, and recover from failures. These are impressive achievements. But this should not be confused with possessing a rich architecture of endogenous motivation. A system that derives subgoals from a user prompt is architecturally different from one that can generate top-level motivators of its own, assign insistence and intensity to them, reconcile them with existing goals, standards, and attitudes, and regulate them over time.
We can already imagine such systems. Consider a household robot that is vacuuming when it detects that its owner has collapsed and is in physiological distress. Rather than merely continuing its assigned task, it asynchronously generates a new top-level motivator to summon medical assistance. Or imagine that it detects an intruder entering the home. It generates a different top-level motivator—to protect its owner—which immediately interrupts its previous activity and recruits perception, deliberation, communication, and action toward the new concern. These are not merely new subgoals in service of vacuuming. They are new top-level motivators generated by the architecture itself in response to changing circumstances.
Take-away
Human-like autonomous agents do not merely pursue goals. They require architectures that generate, assess, prioritize, inhibit, and transform motivators. This is why motivation is not an optional add-on to intelligence.
Insistence, intensity, and executive controllability
It is tempting to speak of a motivator’s “strength.” But this is too crude. Several dynamic properties of motivators need to be distinguished.
Insistence is the degree to which a motivator persistently competes for limited cognitive resources, including attention, working memory, executive processes, and reflective processes. Intensity is the degree to which a motivator tends to recruit or energize behavioural systems, thereby increasing the disposition toward overt action. Importance is the value attributed to the motivator by executive functions. (These dimensions are expanded upon in chapter 3 of my thesis and briefly in Sloman, 1987) Executive controllability is the extent to which management and meta-management processes can regulate a motivator, its behavioural expression, or its influence on cognition.
In humans, these dimensions can dissociate. In obsessive-compulsive disorder, an intrusive thought about harming a loved one may have very high insistence: it repeatedly intrudes into attention and working memory. But it may have very low (behavioural) intensity: the person is horrified by the thought and has little or no disposition to act on it. Conversely, some action tendencies may have high intensity but low executive controllability, as in many motor tics associated with Tourette syndrome. Reflexively withdrawing one’s hand from a hot stove has high intensity and low deliberative control, but it may not be very insistent once the action is complete. One’s management processes may assign high urgency and/or high importance to a motivator that is not yet very insistent or intense (e.g., the important need to lose weight might not drive action). AI may or may not support all these distinctions.
These distinctions help explain why understanding motivation requires more than assigning a single strength to a motive. Some motivators dominate thought without producing action. Others produce action without prolonged thought. Still others quietly shape long-term life projects without constantly interrupting attention.
Take-away
Motivators differ not merely in “strength,” but in insistence, intensity, and executive controllability. This distinction is crucial for understanding perturbance, intrusive thought, impulsive action, self-control, and future AI motivation.
Motivators: goals, standards, and attitudes
Once we recognize that motivation is architecturally generated, another question immediately arises: what kinds of motivators must a human-like autonomous agent be able to generate and regulate?
Here it is helpful to draw on Ortony, Clore and Collins’ The Cognitive Structure of Emotions, and on the treatment of values in my Cognitive Productivity with macOS: 7 Principles for Getting Smarter with Knowledge. I use the term motivator to refer to a value-bearing control state: something the agent treats as relevant or insistent, consciously or unconsciously, explicitly or implicitly. Motivators help drive assessment, attention, goal formation, planning, and action. In my terminology, motivators also draw attention to the fact that values are not merely labels attached to things. They can spawn assessments and other motivators, including goals.
To a first approximation, motivators come in three different flavors: goals and projects, standards, and attitudes. This taxonomy is partly based on Ortony, Clore and Collins, and I developed it in Principle 1 of Cognitive Productivity with macOS: 7 Principles for Getting Smarter with Knowledge as a practical way of understanding values, motivation, and self-governance.
Goals and projects are states the agent is willing to work to achieve, preserve, accelerate, delay, or avoid. They vary in desirability. A scientist may have the goal of explaining a phenomenon. A student may have the goal of mastering a concept. A robot may have the goal of reaching a destination while conserving energy. Goals can be top-level or derived. They are often arranged not in simple hierarchies but in complex networks of means and ends.
Standards are norms, rules, ideals, or constraints that the agent treats as things that ought or ought not to hold. They vary in praiseworthiness and blameworthiness. “Be honest,” “do not mislead the reader,” “keep promises,” “respect evidence,” and “do not endanger others” are standards. Standards regulate conduct, constrain goal pursuit, and provide the basis for emotions such as guilt, shame, admiration, resentment, and indignation.
Attitudes are likes, dislikes, preferences, interests, aversions, tastes, fascinations, and affective biases. They vary in appeal. One may like a melody without having the goal of hearing it now; dislike a food while having the goal of eating it for health reasons; or be fascinated by a topic before having any explicit project involving it. Attitudes often guide attention and learning before explicit goals are formed.
These three kinds of motivators interact continuously. Standards can generate goals: “I ought to apologize, therefore I will apologize.” Goals can modify attitudes: “I began studying mathematics for instrumental reasons, but came to love it.” Attitudes can generate goals: “That topic fascinates me; I want to learn more.” Standards can constrain goals; goals can override attitudes; attitudes can bias the selection of goals; and all three can influence the assessment of events, actions, objects, people, and ideas. Human-like motivation is therefore not a simple hierarchy of goals. It is a dynamic network of interacting motivators. However, these forms of value can vary independently as well. One may view a state of affair as desirable but not praiseworthy, for instance.
This point is crucial for AI. Human-like intelligence is not merely the capacity to pursue goals. It also involves the capacity to generate, represent, revise, coordinate, and sometimes reject networks of goals, standards, and attitudes. A system that optimizes for a goal but lacks standards and attitudes may be powerful, but it is not human-like in the relevant sense. Conversely, a future AI system with goals, standards, and attitudes would raise a much richer set of questions about agency, rationality, alignment, responsibility, and the space of possible minds.
Take-away
Human-like autonomous agents are not merely goal-directed. They are also norm-governed and attitude-shaped. Human-like intelligence requires the capacity to generate and regulate networks of goals, standards, and attitudes.
Effectance, meta-effectiveness, and epistemic agency
This architectural perspective also helps explain why intelligence should not be understood merely as a collection of abilities. Highly intelligent people do not simply solve problems well. They often display a persistent tendency to become more capable over time.
Robert White called one important aspect of this tendency effectance): the motivation toward competence. In Cognitive Productivity: Using Knowledge to Become Profoundly Effective, I argued that effectance is better understood architecturally than behaviourally. Effectance is not merely a conscious or even unconscious desire to improve oneself. Rather, it is a propensity to generate top-level motivators whose pursuit tends to increase competence, effectiveness, and understanding.
Someone driven by effectance may decide to learn a new mathematical technique, master a musical instrument, study philosophy, understand a difficult scientific paper, improve a piece of software, refine a workflow, or simply ask a better question. None of these activities need be motivated explicitly by the thought “I want to become more intelligent.” Yet together they gradually reshape the architecture of the mind.
Human-like intelligence therefore includes a developmental dimension. Intelligent agents do not merely use knowledge; they acquire it, organize it, evaluate it, integrate it, apply it, and continually improve their capacity to use it effectively. This developmental perspective lays part of the foundation for what I believe should become an integrative design-oriented theory of epistemic agency: a theory explaining how human-like autonomous agents acquire, organize, evaluate, integrate, and apply knowledge in ways that continually increase their competence and effectiveness.
One might think of effectance as the motivation to become more competent, and meta-effectiveness as the skills and dispositions of becoming better at becoming effective. Meta-effectiveness encompasses not only the acquisition of knowledge and skills, but also the cultivation of the dispositions, habits, executive functions, and external symbolic supports required to apply them appropriately. Thus effectance is a subset of meta-effectiveness. Meta-effectiveness is the core concept of my first book, Cognitive Productivity: Using Knowledge to Become Profoundly Effective.
This perspective aligns naturally with Keith Stanovich’s distinction between intelligence and rationality. Rational thought depends not only on cognitive abilities, but also on knowledge and the dispositions to deploy those abilities when appropriate. Similarly, meta-effectiveness concerns developing the knowledge, skills, and dispositions that enable autonomous agents to use what they know effectively in pursuit of their goals, standards, and attitudes. Some educational and practical implications of this perspective are explored in Cognitive Productivity with macOS: 7 Principles for Getting Smarter with Knowledge, where I propose seven principles of meta-effectiveness — augmenting human cognition through knowledge resources and information technology.
A competent agent can solve problems. An effectant agent seeks to become more competent. A meta-effective agent systematically develops the knowledge, skills, dispositions, executive functions, and external symbolic supports that make continual improvement possible.
Take-away
Human-like intelligence is not merely current problem-solving ability. It includes motivational architectures, including effectance, that continually develop competence. Meta-effectiveness extends this developmental orientation into the disciplined cultivation of knowledge, skills, dispositions, executive functions, and external supports.
Attachment structures
Human-like agency is not merely individual problem solving. Humans form enduring relationships with people, places, communities, disciplines, projects, and ideals. These relationships are not just memories. They reshape the architecture.
In a 1996 paper with Ian Wright and Aaron Sloman, we modernized the notion of attachment structures in the context of a design-oriented analysis of grief. (At the time we used the expression “design-based.”) The central point was that an attachment is not merely a feeling. It is a distributed computational structure embedded throughout an autonomous agent. Such a structure may include stored knowledge, expectations, preferences, habits, plans, predictive models, and specialized motivator generators that influence perception, deliberation, and action.
Attachment structures illustrate an important principle of human-like intelligence: long-term relationships do not merely produce memories. They reorganize the motivational architecture itself. An attachment to a person may alter what one notices, what one worries about, what one plans, what one treats as important, and what one feels compelled to protect. An attachment to a research programme may generate questions, projects, standards, and intellectual commitments over decades. An attachment to a moral ideal may repeatedly generate motivators that override convenience, fear, or short-term reward.
Attachment structures gradually modify and create motivator generators. They therefore influence which goals, standards, and attitudes arise in future situations, how insistent those motivators become, how intense their action tendencies are, and how readily they gain access to executive resources. This helps explain why attachment can be so powerful, and why grief, betrayal, loss, or separation can become perturbing.
This also sets a high bar for AI. A system that remembers a user is not thereby attached to that user. A human-like robot capable of forming attachments would need to develop distributed structures that alter its motivator generators, predictive models, standards, priorities, and executive control over time. Such a system would not merely store a profile. It would become differently organized because of a relationship.
Take-away
Attachment structures are distributed computational organizations that reshape motivator generators over time. Human-like AI would require more than memory of persons or projects; it would need architectures capable of forming, maintaining, revising, and sometimes dismantling attachments.
Designed and emergent affect
We can now directly address a topic that public discussions of intelligence often mishandle: emotion. It is common to treat emotion as the opposite of intelligence, or as something that disrupts rational thought. That picture is far too simple. In a human-like autonomous agent, affect is not an optional decorative layer added after cognition has done its work. It is part of the control architecture through which values, needs, concerns, and motivators influence attention, deliberation, action, and learning.
However, we should not treat “emotion” as a single thing either. Some affective mechanisms may be explicitly built into an architecture, whether by evolution or by human designers. Alarm mechanisms, pain systems, attachment mechanisms, curiosity mechanisms, reward systems, and other specialized motivator generators are examples of computational mechanisms that asynchronously generate or modify motivators. They help determine what the agent notices, avoids, approaches, protects, repairs, learns, or pursues.
Other affective phenomena are not best understood as built-in modules. They emerge from interactions among architectural components. This distinction was central to Aaron Sloman’s early work on computational theories of emotion. (See Sloman & Croucher(1981)- You don’t need a soft skin to have a warm heart and Sloman & Croucher (1981) - Why robots will have emotions.)
The point is not that programmers must insert a “fear module,” a “grief module,” or a “jealousy module” into an intelligent system. Rather, if an architecture contains specialized motivator generators, mechanisms for assigning insistence and intensity, limited cognitive resources, interruption mechanisms, learning, memory, executive functions, and reflective processes, then some emotional phenomena, including mental perturbance, may emerge as system-level patterns.
Mental perturbance is a state in which one or more highly insistent motivators repeatedly recruit attention and other cognitive resources, making it difficult for the agent to disengage and sustain alternative activities. It is not merely an emotion, nor merely a thought pattern. Mental perturbance, an architectural phenomenon, is produced when motivators, insistence, attention, working memory, executive functions, and limited computational resources interact over time. Perturbance may manifest as worry, rumination, grief, craving, obsessive planning, anger, shame, or other forms of persistent mental preoccupation, depending on the motivators involved. In particular, this architectural dynamic provides an architectural explanation for repetitive thought: highly insistent motivators repeatedly gain access to executive resources, making disengagement difficult despite competing goals, standards, and attitudes.
Thus, this perspective complements psychological theories of rumination and worry, and more generally Watkins’ (2008) framework of repetitive thought, by proposing a computational architectural account of why repetitive thought occurs: it reflects the recurrent recruitment of limited executive resources by highly insistent motivators.
Perturbance is not another component in a computational architecture. Rather, it is an emergent architectural phenomenon arising from interactions among motivators, executive functions, working memory, attention, and limited computational resources. This is one reason perturbance is theoretically useful. Rather than explaining persistent worry, rumination, grief, craving, or obsessive thought by positing a separate cognitive module, it explains them as recurrent patterns emerging from the dynamics of the architecture.
This point matters for AI as much as for cognitive science. We should not merely ask only whether an AI system “has emotions.” That question is too crude. We should ask which affective control mechanisms are explicitly designed into the system, which motivational processes it contains, whether any of its internal dynamics can generate persistent concern-like states, and which emotional phenomena, if any, could emerge from its architecture. The answer may differ radically across systems.
Take-away
Affect is not the enemy of intelligence. In human-like autonomous agents, some affective mechanisms are architecturally designed, while some affective phenomena, such as mental perturbance, emerge from interactions among motivators, insistence, attention, working memory, executive functions, and limited resources.
Intelligence is not rationality
We are now in a position to address another common confusion. Intelligence is often treated as though it implied rationality. If someone is highly intelligent, we expect them to think well, make good decisions, update their beliefs, avoid foolish errors, and resist obvious biases. Yet everyday experience and psychological research both show that this expectation is unreliable. Highly intelligent people can reason badly. They can be dogmatic, impulsive, biased, overconfident, inattentive to evidence, or unwilling to reconsider cherished beliefs.
Keith Stanovich has done more than almost anyone to clarify this distinction. In What Intelligence Tests Miss: The Psychology of Rational Thought, he argues that intelligence tests measure important cognitive abilities, but they do not adequately measure rational thought. People may have high cognitive ability while lacking the knowledge or thinking dispositions required to seek disconfirming evidence, consider alternatives, override impulsive responses, or use probabilistic reasoning when appropriate.
The architectural perspective developed here helps explain why Stanovich’s distinction is so important. Earlier, we distinguished between management processes and meta-management processes. Management processes construct, compare, and evaluate possible actions or beliefs. Meta-management processes monitor and regulate management processes themselves; they notice that one is reasoning poorly, wasting time, perseverating, being biased, or pursuing the wrong goal. But rationality depends not merely on having reflective mechanisms. It also depends on knowledge, values, dispositions, and motivations to recruit them when needed.
Stanovich’s theory of rationality fits naturally into a broader theory of human-like autonomous agency. In fact, he proposed an information processing architecture that is inspired by and similar to (but simpler than) Sloman’s H-CogAff architecture adapted here. (See figure 2.1, page 33 of Stanovich’s 2011 book, Rationality and the reflective mind). A person may possess the cognitive ability required to solve a reasoning problem, yet fail to engage the reflective processes that would lead to a better answer. They may be tired, anxious, angry, socially pressured, overconfident, or insufficiently motivated to think carefully. They may also lack effectance with respect to reasoning itself: the motivation to improve their own thinking habits, learn from errors, and cultivate better epistemic practices.
Thus, rationality is not a mysterious extra faculty added to intelligence. It depends on an architecture in which reflective processes, motivation, values, norms, knowledge, skills, and learned dispositions interact.
This also illustrates why perturbance matters. A highly perturbed mind (say: in love, angry or experiencing grief) may have ample intelligence and even strong reflective capacity, yet still struggle to reason well because highly insistent motivators keep capturing attention and working memory. Worry, anger, craving, shame, or grief can repeatedly redirect cognitive resources toward a dominant concern. In such cases, irrationality does not arise from lack of intelligence alone. It arises from the dynamics of the architecture.
The same lesson applies to AI. An AI system may perform well on reasoning benchmarks while lacking robust mechanisms for epistemic self-regulation. It may produce plausible answers without appropriately monitoring uncertainty. It may revise plans without understanding when to question the goals that generated them. It may simulate reflection in language without possessing stable dispositions to use reflective processes across contexts. Again, asking whether AI is intelligent tells us too little. We must also ask what kind of rationality, if any, its architecture supports.
Take-away
Intelligence and rationality are distinct. Rationality depends not merely on cognitive ability, but on reflective processes, thinking dispositions, motivational support, values, knowledge, and the regulation of perturbing concerns.
What this means for today’s AI
We can now return to the question with which we began. Is AI intelligent?
The answer is not a simple yes or no. Some AI systems exhibit some forms of intelligence, in some respects, according to some scientifically defensible theories of intelligence. Large language models can integrate information, generate explanations, write code, summarize documents, translate languages, and solve many problems that would once have seemed to require intelligence. Artificial autonomous agents can decompose tasks, call tools, maintain intermediate state, and pursue objectives over time. Robots can perceive, act, adapt, and learn in physical environments.
But these facts do not settle the question. They refine it.
The issue is not whether a system has crossed a single threshold called intelligence. The issue is which computational requirements for human-like autonomous agency it satisfies, which it lacks, and what kind of architecture explains its pattern of successes and failures. What are its capabilities and mechanisms that go beyond humans? A system may be linguistically fluent without being reflectively rational. It may derive subgoals without generating its own top-level motivators. It may optimize actions without possessing standards or attitudes. It may simulate emotional language without having affective control mechanisms or emergent perturbance. It may display competence without effectance or meta-effectiveness.
This is why both triumphalist and dismissive claims about AI tend to mislead. It is not illuminating to say simply that AI is intelligent. Nor is it illuminating to say that it is “not really intelligent.” Both statements compress too much. They ignore the space of possible minds.
A more useful approach asks:
What kind of AI system are we discussing?
What computational architecture does it instantiate?
Which forms of human-like intelligence does it exhibit?
What kinds of motivators, if any, does it generate?
Can it regulate top-level and derived goals?
Does it possess standards and attitudes, or merely optimize supplied objectives?
What reflective mechanisms does it have?
What thinking dispositions does it reliably display?
What forms of rationality does its architecture support?
Can it form attachment structures?
Does it exhibit effectance or meta-effectiveness?
Where does it lie in the space of possible minds?
These questions are more difficult than asking whether AI is intelligent. They are also more fruitful.
Take-away
The scientifically useful question is not whether AI is intelligent in general. It is which aspects of human-like intelligence and autonomous agency particular AI systems exhibit, and which computational architectures explain them.
An integrative design-oriented approach
The perspective developed here reflects what I have elsewhere called an integrative design-oriented approach to cognitive science and AI. The phrase matters. It is not merely the traditional design stance applied to minds. It is an attempt to integrate multiple disciplines, multiple levels of explanation, and multiple design requirements in order to understand human-like autonomous agents.
The integrative design-oriented approach begins not with dictionary definitions, but with requirements. What must an architecture be able to do in order to support human-like autonomous agency? It must perceive, act, learn, remember, generate motivators, evaluate alternatives, manage goals, use working memory, reflect on its own processes, regulate affect, develop competence, form attachments, interact with external symbolic systems, and coordinate with other agents. No single discipline can explain all of this. Psychology, AI, neuroscience, philosophy, anthropology, education, evolutionary theory, and software design all have something to contribute.
The integrative design-oriented approach replaces arguments about labels with questions about requirements, mechanisms, architectures, development, and emergence. It seeks not merely to classify minds, but to explain how different forms of autonomous agency become possible.
That, to my mind, is the deeper lesson AI is forcing upon us. Artificial intelligence has not simply challenged our understanding of machines. It has challenged the adequacy of our theories of mind. If we want to understand AI, we need better cognitive science. And if cognitive science wants to understand minds, it needs richer theories of computational architecture, autonomous agency, motivation, affect, executive function, working memory, attachment, rationality, and development.
Take-away
An integrative design-oriented approach asks what computational requirements and architectures make human-like autonomous agency possible. It treats intelligence as one aspect of a broader scientific problem.
Large language models have changed the debate
It would be a mistake to read this essay as minimizing the achievements of modern AI, particularly large language models such as ChatGPT, Claude, Gemini, and others. They represent one of the most significant developments in the history of artificial intelligence.
These systems can explain complex ideas, summarize books and scientific papers, generate software, translate between languages, critique arguments, adapt their writing style to different audiences, brainstorm new ideas, tutor students, help researchers explore unfamiliar literatures, and collaborate with people on extended intellectual projects. In my own work, including writing this essay, I have found ChatGPT to be an extraordinarily valuable thinking partner for brainstorming, criticism, literature exploration, and improving scientific writing. These are not trivial accomplishments. They deserve to be recognized as genuine forms of intelligent information processing.
Indeed, one of the most remarkable features of large language models is not merely what they can do in isolation, but what they can accomplish in sustained interaction with human users. A productive dialogue often becomes a joint cognitive process in which the human contributes goals, background knowledge, judgment, and evaluation, while the AI contributes retrieval, synthesis, reformulation, analogy generation, and the rapid exploration of alternative ideas. The result is frequently better than either participant could have achieved alone.
Recognizing these remarkable capabilities, however, does not bring us any closer to answering the question, “Is AI intelligent?” On the contrary, it exposes the inadequacy of the question. Large language models exhibit extraordinary strengths in some forms of cognition while remaining limited in others. They have therefore made it even more important—not less—to distinguish among different forms of intelligence, different theories of intelligence, and different computational architectures.
Ironically, the success of large language models strengthens rather than weakens the central argument of this essay. They have shown that intelligent behaviour can emerge in ways that many researchers did not anticipate. Rather than forcing us to abandon cognitive science, they challenge us to develop richer theories capable of explaining both biological and artificial forms of intelligence. The scientific task remains the same as the core of this essay argues: It is no longer to decide whether AI is or is not intelligent. It is to understand what kinds of intelligence different systems possess, how those capacities arise, and how they can best complement and extend human intelligence.
So, is AI intelligent?
So where does all this leave the original question?
One should no longer feel compelled to answer the question “Is AI intelligent?” with either a simple yes or no, or by asking to what quantitative degree it is intelligent. More scientifically productive questions concern the computational requirements for different forms of autonomous agency, the architectures that satisfy those requirements, and the capabilities, limitations, developmental trajectories, and emergent phenomena that follow from those architectures.
Some AI systems exhibit some forms of intelligence, in some respects, according to some scientifically defensible theories of intelligence. Others do not. Future systems will almost certainly occupy regions of the space of possible minds that have never previously existed on Earth.
The question itself is therefore not wrong. It is simply incomplete. Before we can answer it responsibly, we must ask what kind of AI system we are talking about, what theory of human-like intelligence we are using, what computational architecture the system embodies, what forms of value and motivation it possesses, what reflective capacities it exhibits, and where it lies in the space of possible minds.
Those questions do not make the debate disappear. They transform it from an argument about words into a scientific inquiry about minds.
The goal is not to settle the meaning of the word “intelligence.” It is to understand the architectures that make different forms of intelligence and autonomous agency possible.
The next time someone tells you that AI is—or is not—really intelligent, you could ask: What kind of AI? According to what theory of intelligence? And what computational architecture are we talking about?
That is where a serious conversation can begin.
What about consciousness and emotions?
This essay has focused on intelligence and autonomous agency. The same points I made here apply to concepts of consciousness and emotion. In A Mind So Rare: The Evolution of Human Consciousness, mentioned above, Merlin Donald made the same case for consciousness: there is a space of possible minds supporting different forms of consciousness. It’s not a matter of “this animal or machine has consciousness” or “it doesn’t have consciousness,” but what forms of consciousness does the agent have?
Similarly, it is silly to debate whether an AI agent can experience emotions or not without qualifying the question with respect to a particular integrative design-oriented theory of emotion.
Why You Can’t Make a Machine That Feels Pain
This paper was partly inspired by Daniel Dennett’s paper, “Why You Can’t Make a Machine That Feels Pain” (answer because the concept of pain is polymorphous). Here the same idea/pattern is used, generalized and extended and applied to intelligence and consciousness.
Glossary of Key Concepts
This essay develops and integrates terminology from AI, cognitive science, psychology, philosophy, and education. The following glossary summarizes key concepts as they are used here. Many are adapted from prior work; some are refinements introduced in this essay. This could be extended, e.g., to define the forms of awareness specified by Donald in A Mind So Rare (selective binding, short-term control, and intermediate- and long-term governance), rationality and other key terms.
Attachment structure. A distributed computational organization that develops through repeated interaction with particular people, projects, organizations, places, or ideals. Attachment structures influence perception, memory, motivator generation, insistence assignment, planning, action, and executive control, thereby reshaping the motivational architecture over time.
Autonomous agent. A system capable of perceiving, acting, learning, generating and regulating motivators (both top-level and derivative), managing competing demands, and sustaining coherent behaviour over time under constraints of limited information, time, working memory, and computational resources.
Cognitive productivity. Efficience and effectiveness in using knowledge to solve problems, acquire new knowledge, develop expertise and deliver services.
Computational architecture. The organization of interacting computational mechanisms that together enable an autonomous agent to function. An architecture specifies interacting components and processes, not a fixed sequence of operations.
Effectance. The architecture-based motivation to become more competent. Effectance contributes to learning, self-improvement, and the development of expertise.
Epistemic agency. The capacity of an autonomous agent to acquire, organize, evaluate, integrate, apply, and improve knowledge in pursuit of its motivators. More generally it involves cognitive productivity.
Executive controllability. The extent to which management and meta-management processes can regulate a motivator, its behavioural expression, or its influence on cognition.
Executive functions. The family of management and meta-management processes responsible for regulating affect, cognition, motivation and behaviour. In this paper, executive functions are understood architecturally rather than as a single faculty.
Human-like autonomous agency. The capacity to function as a human-like autonomous agent (see “autonomous agent” above), integrating perception, action, memory, working memory, motivator generation, executive functions, affect, learning, reflection, attachment, and development within a coherent computational architecture.
Insistence. The degree to which a motivator persistently competes for limited cognitive resources, including attention, working memory, management processes, and meta-management processes.
Insistence-based motivator filters. Architectural mechanisms that regulate which currently active motivators gain access to limited executive resources on the basis of their insistence and other contextual factors.
Intensity. The degree to which a motivator tends to recruit or energize behavioural systems, thereby increasing the disposition toward overt action.
Integrative design-oriented approach. A scientific approach to understanding autonomous agency by integrating evidence and theory across disciplines while analysing computational requirements, architectures, mechanisms, development, and design, using the design stance. Regarding the design stance, part of the IDO approach, see Sloman (1993) - Prospects for AI as the general science of intelligence but replace “design-based” with “design stance”. See also Dennett 1987 The Intentional Stance.
Management processes (deliberative processes). Executive processes responsible for planning, reasoning, scheduling, conflict resolution, resource allocation, and other forms of executive control.
Mental perturbance. An emergent architectural phenomenon in which one or more highly insistent motivators repeatedly recruit executive processes, making disengagement difficult; i.e, interrupting and influencing attention, working memory, deliberation, memory, and action.
Meta-effectiveness. Skills, knowledge and dispositions (effectance) to use knowledge to become a more effective person.
Meta-management processes (reflective processes). Executive processes that monitor, evaluate, and regulate management processes themselves. They support self-monitoring, error detection, strategic revision, mental flexibility, and other forms of reflective control.
Motivator. A value-bearing control state that influences assessment, attention, planning, executive control, learning, and action. Motivators include motives, goals and projects, standards, and attitudes.
Motivator generator. A computational mechanism that asynchronously generates, modifies, reactivates, and regulates motivators, and assigns insistence and intensity to them.
Reflective processes. See meta-management processes.
Repetitive thought. Persistent or recurrent cognition, such as worry, rumination, obsessive thinking, or craving-related thought. In this framework, repetitive thought is explained architecturally as recurrent recruitment of executive resources by highly insistent motivators. This is a subset of mental perturbance.
Space of possible minds. The space of possible computational architectures and autonomous agents, encompassing biological, artificial, individual, collective, and hybrid forms of mind.
Working memory. The limited-capacity executive workspace in which information is temporarily maintained, manipulated, integrated, and evaluated during ongoing cognition. In this framework, working memory is closely tied to executive functions rather than treated as an isolated memory store.
Colophon
The images in this document were generated by ChatGPT. Some of the text was generated through interaction with ChatGPT.