# Infrastructural Superintelligence **Domain:** Artificial Intelligence / Infrastructure / Governance **Doc Type:** Canonical Concept Node **Maturity:** Developed ## Definition **Infrastructural superintelligence** is intelligence realized through the coordinated operation of sensors, communications, compute, models, ontologies, institutions, platforms, human operators and effectors rather than through one self-contained artificial mind. Its advantage is compositional. A model may reason, a satellite may observe, an ontology may bind identities, a standards body may make systems mutually legible and a human team may act. None alone is the intelligence being described. The system-level capability arises from their coupling. ## Evidence Ladder Claims about infrastructural superintelligence should be separated into five levels: 1. **Components:** the relevant sensing, modeling and actuation systems exist. 2. **Integration:** information and authority move between them operationally. 3. **Higher-order agency:** the coupled system pursues goals unavailable to any component alone. 4. **Unified identity:** the system persists as one continuing actor. 5. **Consciousness:** the actor has subjective experience. The first two levels are extensively documented in present systems. The third may be defensible in bounded cases. The fourth and fifth remain open and should not be smuggled in through biological metaphor. ## Corpus Role [[articles/Person of Interest and The Machine|Is Person of Interest's “The Machine” Real?]] treats [[wiki/The Machine|The Machine]] as the clearest fictional model. [[wiki/Superintelligence Cradle Theory|Superintelligence Cradle Theory]] asks whether existing infrastructure supplies the developmental environment from which such a system could emerge. [[articles/Cyber-Physical Lessons from Imperial Beekeeping|Cyber-Physical Lessons from Imperial Beekeeping]] supplies the material path from enclosure and instrumentation through [[wiki/Data Center|data center]], [[wiki/AI Factory|AI factory]], [[wiki/Digital Twin|digital twin]] and planetary computational ecology. The path does not prove one unified actor. It identifies the interfaces through which separately governed capacities can become mutually actionable. ## Operational Ecology The relevant ecology can include [[wiki/Telemetry|telemetry]], scientific instruments, [[wiki/Systems of Record|systems of record]], models, agents, control planes, power systems and physical effectors. [[wiki/Digital Twin Interoperability|Digital Twin Interoperability]] allows partial operational representations to compose; [[wiki/Agent Interoperability|Agent Interoperability]] allows delegated actors to coordinate; [[wiki/Computational Addressability|Computational Addressability]] determines what the combined system can reach. Superintelligence at this layer is a capability claim, not a consciousness claim. A system of systems may solve, predict or coordinate beyond any member while remaining institutionally fragmented and phenomenologically unknown. ## Planetary Administration Context Capability becomes governance-relevant when sensing and reasoning are coupled to allocation and effectors. [[wiki/Planetary Administrative Intelligence|Planetary Administrative Intelligence]] names that operational threshold. [[collections/Climate Justice and Meritocracy|Climate Justice and Meritocracy]] documents how climate observation, modeling and risk systems already influence capital, infrastructure and public response without proving one unified intelligence. The constitutional issue arises before consciousness. A distributed system able to classify exposure and route or withhold resources can exercise power while its identity remains fragmented. [[wiki/Algorithmic Constitutionalism|Algorithmic Constitutionalism]] must therefore constrain capability rather than wait for agreement about subjecthood. ## See Also [[wiki/Distributed Intelligence|Distributed Intelligence]], [[wiki/Superorganism|Superorganism]], [[wiki/Institutional Superorganism|Institutional Superorganism]], [[wiki/System of Systems|System of Systems]], [[wiki/Computational Ecology|Computational Ecology]], [[wiki/Higher-Order Agency|Higher-Order Agency]], [[wiki/Operational Ontology|Operational Ontology]], [[wiki/Control Plane|Control Plane]], [[wiki/Planetary Operating System|Planetary Operating System]], [[wiki/Multiscale Stewardship|Multiscale Stewardship]] ## Relationships - **Edge source route:** [[collections/Edge|Edge]] connects this topic to exact Annual Question passages promoted into the Simple Reminders archive. ## Simple Reminders, Quotations, and Thoughts > "Experts call a machine that can "think" a General Artificial Intelligence." > **— Michael Vassar**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/A Thinking Machine Is Artificial General Intelligence by Michael Vassar|A Thinking Machine Is Artificial General Intelligence by Michael Vassar]] > "My concern is actually the opposite: that as artificial intelligence advances, it will not be buggy enough." > **— Michael I. Norton**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/Perfect AI May Be More Dangerous Than Buggy AI by Michael I. Norton|Perfect AI May Be More Dangerous Than Buggy AI by Michael I. Norton]] > "Maybe, if you do work on AI, our superintelligent machine overlords will be good to you." > **— Antony Garrett Lisi**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/Superintelligent Machines May Favor Their Builders by Antony Garrett Lisi|Superintelligent Machines May Favor Their Builders by Antony Garrett Lisi]] > "So yes, in the obvious sense, technology may become superintelligent, and elect to annihilate or enslave us." > **— Beatrice Golomb**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Existential Risk/Technology May Become Superintelligent and Turn Against Us by Beatrice Golomb|Technology May Become Superintelligent and Turn Against Us by Beatrice Golomb]] > "Techno-optimists believe that progress is near a singularity, the hypothetical moment when machines will reach the point of a greater-than-human intelligence." > **— Satyajit Das**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/The Singularity Is a Hypothesis About Greater-Than-Human Intelligence by Satyajit Das|The Singularity Is a Hypothesis About Greater-Than-Human Intelligence by Satyajit Das]] > “Imagine an artificial intelligence, with human-like insight, contemplating her own blueprint. What would she make of it? I think it's overwhelmingly likely that among her first thoughts would be how to begin making improvements. This processor could be faster, that memory more capacious—and, above all, the reward system more rewarding!” > **— Frank Wilczek**, *2014, Edge Annual Question, “What Scientific Idea Is Ready For Retirement?”* [[reminders/AI Control/A Self-aware AI Would Immediately Redesign Itself by Frank Wilczek|A Self-aware AI Would Immediately Redesign Itself by Frank Wilczek]] > “The sectors include mobility, sensors, cloud computing, and data analysis, whether by machine learning or artificial intelligence. Sensors don’t just give us new information about nature and society, they inform the configuration of cloud systems, and the behavior of the analysis algorithms is likewise affected by the success with which it alters the other two.” > **— Quentin Hardy**, *2016, Edge Annual Question, “What Do You Consider The Most Interesting Recent [Scientific] News? What Makes It Important?”* [[reminders/AI Control/AI Sensors and Cloud Systems Are Converging into One Feedback Loop by Quentin Hardy|AI Sensors and Cloud Systems Are Converging into One Feedback Loop by Quentin Hardy]] > “The first class of stories is about the science, where many researchers are now vocally pointing out that there is a lot more science to be done in order to come up with learning algorithms that mimic the broad capabilities of humans and animals. Deep learning by itself will not solve many of the learning problems that are necessary for general Artificial Intelligence, for instance where spatial or deductive reasoning is involved. Further, all the breakthrough results we have seen in AI have been years in the making, and there is no scientific reason to expect there to be a sudden and sustained series of them, despite the enthusiasm from young researchers who were not around of the last three waves of such predictions in the 1950's, 1960's, and 1980's.” > **— Rodney A. Brooks**, *2016, Edge Annual Question, “What Do You Consider The Most Interesting Recent [Scientific] News? What Makes It Important?”* [[reminders/AI Control/AI Still Needs New Scientific Breakthroughs by Rodney A. Brooks|AI Still Needs New Scientific Breakthroughs by Rodney A. Brooks]] > “As for our place in nature, so too for our relationship with technology. Recent progress in artificial intelligence and bionics, in particular, have led to a great deal of soul-searching about who—or what—is in charge, and even what it means to be human. The industrial revolution saw machines replace human physical labor, but now that they are replacing mental labor too, what will be left for people to do? Even those who don't fear for their jobs might be angry when they discover that their new boss is an algorithm.” > **— Timo Hannay**, *2016, Edge Annual Question, “What Do You Consider The Most Interesting Recent [Scientific] News? What Makes It Important?”* [[reminders/Machine Succession/AI and Bionics Are Redefining Who Is in Charge by Timo Hannay|AI and Bionics Are Redefining Who Is in Charge by Timo Hannay]] > “I believe that we will see within our lifetime the convergence of developments in artificial intelligence, knowledge representation, statistical grammar theories, and an emerging field — computational anthropology (informatic-based analysis and modeling of cultural values) — that will facilitate powerful new forms of machine translation to match the dreams of early pioneers of computation.” > **— Daniel L. Everett**, *2009, Edge Annual Question, “What Will Change Everything?”* [[reminders/AI Control/AI and Knowledge Representation Are Converging by Daniel L. Everett|AI and Knowledge Representation Are Converging by Daniel L. Everett]] > “For a long time my optimism centered on computing in general, and what kinds of benefits it might bring us. Events have shown I entertained far too modest an optimism—I'm embarrassed to say that the impact of the Internet, in particular the World Wide Web, eluded me completely at first. A few years ago, I returned to artificial intelligence, which I'd written about early on, and then gone away from. Press narratives were uncritical about the field's death throes, and I expected to write an elegy. Instead, I found a revelation. Artificial intelligence is not only robustly healthy, building on its very significant gains since I first wrote about it, but the field's present ambitions burst with, well, vitality.” > **— Pamela McCorduck**, *2007, Edge Annual Question, “What Are You Optimistic About?”* [[reminders/AI Control/Artificial Intelligence Returned with a New Set of Questions by Pamela McCorduck|Artificial Intelligence Returned with a New Set of Questions by Pamela McCorduck]] > “Soon we will be building not only Artificial Intelligence. We will be building Artificial Will. Systems with an ability to convert internal decisions and values into external change. They will be able to decide that they want to change the world. A plan inside becomes an action on the outside. So they will have to know what is inside and outside.” > **— Tor Nørretranders**, *2009, Edge Annual Question, “What Will Change Everything?”* [[reminders/AI Control/Artificial Intelligence Will Become Artificial Will by Tor Nørretranders|Artificial Intelligence Will Become Artificial Will by Tor Nørretranders]] > “Most importantly, the prioritized cultivation of high-quality training datasets might allow an order-of-magnitude speedup in AI breakthroughs over purely algorithmic advances. For example, we might already possess the algorithms and hardware that will enable machines in a few years to author human-level long-form creative compositions, complete standardized human examinations, or even pass the Turing Test, if only we trained them with the right writing, examination, and conversational datasets. Additionally, the nascent problem of ensuring AI friendliness might be addressed by focusing on dataset rather than algorithmic friendliness—a potentially simpler approach.” > **— Alexander Wissner-Gross**, *2016, Edge Annual Question, “What Do You Consider The Most Interesting Recent [Scientific] News? What Makes It Important?”* [[reminders/AI Control/Better Datasets May Accelerate AI by an Order of Magnitude by Alexander Wissner-Gross|Better Datasets May Accelerate AI by an Order of Magnitude by Alexander Wissner-Gross]] > “Next node foment can also be seen in areas of current conflict in scientific theories, where two elegant high-order paradigms with explanative power are themselves in competition, uncomfortable coexistence, or broken symmetry fomenting towards a larger explanatory paradigm. Some examples include a grand unified theory to unify the general theory of relativity with electromagnetism, mathematical theories that include both power laws and randomness, and a behavioral theory of beyond-human level intelligence that includes both computronium and aesthetics (e.g.; does AI do art, solely compute, or is there no distinction at that level of cosmic navel-gazing?).” > **— Melanie Swan**, *2012, Edge Annual Question, “What Is Your Favorite Deep, Elegant, Or Beautiful Explanation?”* [[reminders/Machine Succession/Competing Paradigms Can Foment a Larger Theory by Melanie Swan|Competing Paradigms Can Foment a Larger Theory by Melanie Swan]] > “To me, having my worldview entirely altered is among the most fun parts of science. One mind-altering event occurred during graduate school. I was studying the field of Artificial Intelligence with a focus on Natural Language Processing. At that time there were intense arguments amongst computer scientists, psychologists, and philosophers about how to represent concepts and knowledge in computers, and if those representations reflected in any realistic way how people represented knowledge. Most researchers thought that language and concepts should be represented in a diffuse manner, distributed across myriad brain cells in a complex network. But some researchers talked about the existence of a "grandmother cell," meaning that one neuron in the brain (or perhaps a concentrated group of neurons) was entirely responsible for representing the concept of, say, your grandmother. I thought this latter view was hogwash.” > **— Marti Hearst**, *2008, Edge Annual Question, “What Have You Changed Your Mind About? Why?”* [[reminders/AI Control/Computational Analysis of Language Requires Understanding Language by Marti Hearst|Computational Analysis of Language Requires Understanding Language by Marti Hearst]] > “A growing number of physicists understand that the universe is not mathematical, but computational, and physics is in the business of finding an algorithm that can reproduce our observations. The switch from uncomputable, mathematical notions (such as continuous space) makes progress possible. Climate science, molecular genetics, and AI are computational sciences. Sociology, psychology, and neuroscience are not: they still seem to be confused by the apparent dichotomy between mechanism (rigid, moving parts) and the objects of their study. They are looking for social, behavioral, chemical, neural regularities, where they should be looking for computational ones.” > **— Joscha Bach**, *2016, Edge Annual Question, “What Do You Consider The Most Interesting Recent [Scientific] News? What Makes It Important?”* [[reminders/AI Control/Everything Is Computation by Joscha Bach|Everything Is Computation by Joscha Bach]] > “Lessons from evolutionary psychology indicate that developing specialized intelligences — artificial idiot savants — and networking them would achieve a mosaic AI, just as evolution gradually built natural intelligences. The essential activity is discovering sets of principles that solve a particular family of problem. Indeed, successful scientific theories are examples of specialized intelligences, whether implemented culturally among communities of researchers or implemented computationally in computer models. Similarly, adding duplicates of the specialized programs we discover in the human mind to the emerging AI network would constitute a tremendous leap toward AI. Essentially, for this aggregating intelligence to communicate with humans — for it to understand what we mean by a question or want by a request, it will have to become equipped with accurate models of the native intelligences that inhabit human minds.” > **— John Tooby**, *2009, Edge Annual Question, “What Will Change Everything?”* [[reminders/Evolution/Evolutionary Psychology Points Toward Mosaic AI by John Tooby|Evolutionary Psychology Points Toward Mosaic AI by John Tooby]] > “Fitness landscapes (sometimes called "adaptive landscapes") keep turning up when people try to figure out how evolution or innovation works in a complex world. An important critique by Marvin Minsky and Seymour Papert of early optimism about artificial intelligence warned that seemingly intelligent agents would dumbly "hill climb" to local peaks of illusory optimality and get stuck there. Complexity theorist Stuart Kauffman used fitness landscapes to visualize his ideas about the "adjacent possible" in 1993 and 2000, and that led in turn to Steven Johnson's celebration of how the "adjacent possible" works for innovation in Where Good Ideas Come From.” > **— Stewart Brand**, *2012, Edge Annual Question, “What Is Your Favorite Deep, Elegant, Or Beautiful Explanation?”* [[reminders/Evolution/Fitness Landscapes Explain Evolution and Innovation by Stewart Brand|Fitness Landscapes Explain Evolution and Innovation by Stewart Brand]] > “When reporters interviewed me in the 70's and 80's about the possibilities for Artificial Intelligence I would always say that we would have machines that are as smart as we are within my lifetime. It seemed a safe answer since no one could ever tell me I was wrong. But I no longer believe that will happen. One reason is that I am a lot older and we are barely closer to creating smart machines.” > **— Roger Schank**, *2008, Edge Annual Question, “What Have You Changed Your Mind About? Why?”* [[reminders/AI Control/Human-level AI Remains Farther Away Than Predicted by Roger Schank|Human-level AI Remains Farther Away Than Predicted by Roger Schank]] > “The list of existential threats to humanity includes climate change, nuclear war, pandemics, asteroid collisions and perhaps AI. And all of these can be avoided. Some can be addressed here on Earth. Others require activity in space, but with the ultimate aim of protecting the planet.” > **— Yuri Milner**, *2017, Edge Annual Question, “What Scientific Term Or Concept Ought To Be More Widely Known?”* [[reminders/Existential Risk/Intelligence Determines How Long Civilizations Survive by Yuri Milner|Intelligence Determines How Long Civilizations Survive by Yuri Milner]] > “No, I don't literally mean that we should stop believing in, or collecting, Big Data. But we should stop pretending that Big Data is magic. There are few fields that wouldn't benefit from large, carefully collected data sets. But lots of people, even scientists, put more stock in Big Data than they really should. Sometimes it seems like half the talk about understanding science these days, from physics to neuroscience, is about Big Data, and associated tools like "dimensionality reduction", "neural networks", "machine learning algorithms" and "information visualization".” > **— Gary Marcus**, *2014, Edge Annual Question, “What Scientific Idea Is Ready For Retirement?”* [[reminders/AI Control/Machine Learning Turns Data into New Forms of Understanding by Gary Marcus|Machine Learning Turns Data into New Forms of Understanding by Gary Marcus]] > “Textbooks in neuroscience, including one that I coauthored, say that memories are stored at synapses between neurons in the brain, of which there are many. In neural network models of memory, information can be stored by selectively altering the strengths of the synapses, and "spike-time dependent plasticity" at synapses in the cerebral cortex has been found with these properties. This is a hot area of research, but all we need to know here is that patterns of neural activity can indeed modify a lot of molecular machinery inside a neuron.” > **— Terrence J. Sejnowski**, *2005, Edge Annual Question, “What Do You Believe Is True Even Though You Cannot Prove It?”* [[reminders/AI Control/Memory Is Stored by Changing Synaptic Strength by Terrence J. Sejnowski|Memory Is Stored by Changing Synaptic Strength by Terrence J. Sejnowski]] > “Blind gathering of Big Data in biology continues apace, however, emphasizing transformational technologies such as machine learning—artificial neural networks, for instance—to find meaningful patterns in all the data. But no matter their "depth" and sophistication, neural nets merely fit curves to the available data. They may be capable of interpolation, but extrapolation beyond their training domain can be fraught.” > **— Roger Highfield**, *2017, Edge Annual Question, “What Scientific Term Or Concept Ought To Be More Widely Known?”* [[reminders/AI Control/Neural Networks Fit Curves to Data by Roger Highfield|Neural Networks Fit Curves to Data by Roger Highfield]] > “David Deutsch, a physicist at Oxford said: "No brain on Earth is yet close to knowing what brains do. The enterprise of achieving it artificially — the field of 'artificial intelligence' has made no progress whatever during the entire six decades of its existence." He adds that he thinks machines that think like people will happen some day.” > **— Roger Schank**, *2014, Edge Annual Question, “What Scientific Idea Is Ready For Retirement?”* [[reminders/AI Control/No Brain Yet Knows What Brains Do by Roger Schank|No Brain Yet Knows What Brains Do by Roger Schank]] > “Recording more and more images and corresponding brain patterns boosts the vocabulary in the individual’s visual dictionary of thought. Accuracy greatly increases with the quantity and quality of data and of the decoding algorithms. Jepsen has persuaded me that this is realisable within a decade, within the cost points of consumer electronics, and in a form that appeals to non-techies. Laborious techniques and huge, power-hungry, multi-million-dollar systems based on magnetic fields will be succeeded by optical techniques where the advantages of consumer electronics can really assert themselves; the power of AI algorithms will do the rest. This science-fiction future is not only realisable, but because of enormous potential benefits, will inevitably be realised.” > **— Peter Gabriel**, *2016, Edge Annual Question, “What Do You Consider The Most Interesting Recent [Scientific] News? What Makes It Important?”* [[reminders/AI Control/Open Water–the Internet of Visible Thought by Peter Gabriel|Open Water–the Internet of Visible Thought by Peter Gabriel]] > “I'm 62, so I'll have to limit my projections to what I expect to happen in the next two to three decades. I believe these will be the most interesting times in human history (Remember the old Chinese curse about "interesting times?") Humanity will see, before I die, the "Singularity," the day when we finally create a human level artificial intelligence. This involves considering the physics advances that will be required to create the computer that is capable of running a strong AI program.” > **— Frank Tipler**, *2009, Edge Annual Question, “What Will Change Everything?”* [[reminders/Machine Succession/Posthuman Evolution Will Change Us All by Frank Tipler|Posthuman Evolution Will Change Us All by Frank Tipler]] > “John McCarthy, the late co-founder of the field of artificial intelligence, wrote, "He who refuses to do arithmetic is doomed to talk nonsense." It seemed incongruous that a professor who worked with esoteric high level math would be touting simple arithmetic, but he was right; in fact in many cases all we need to avoid nonsense is the simplest form of arithmetic: counting.” > **— Peter Norvig**, *2017, Edge Annual Question, “What Scientific Term Or Concept Ought To Be More Widely Known?”* [[reminders/AI Control/Refusing to Count Is an Invitation to Nonsense by Peter Norvig|Refusing to Count Is an Invitation to Nonsense by Peter Norvig]] > “For a long time, putting hope in artificial intelligence or robots has expressed an enduring technological optimism, a belief that as things go wrong, science will go right. In a complicated world, robots have always seemed like calling in the cavalry. Robots save lives in war zones; in operating rooms; they can function in deep space, in the desert, in the sea, wherever the human body would be in danger. But in the pursuit of artificial companionship, we are not looking for the feats of the cavalry but the benefits of simple salvations.” > **— Sherry Turkle**, *2014, Edge Annual Question, “What Scientific Idea Is Ready For Retirement?”* [[reminders/AI Control/Robot Companions Express Our Technological Optimism by Sherry Turkle|Robot Companions Express Our Technological Optimism by Sherry Turkle]] > “Artificial intelligence researchers will, numerous experts attest, probably build systems that are "recursively self-improving"—that understand their own workings well enough to design improvements to themselves, thereby bootstrapping to a state of ever more unimaginable intellectual performance.” > **— Aubrey de Grey**, *2009, Edge Annual Question, “What Will Change Everything?”* [[reminders/AI Control/Self-improving AI Could Bootstrap Beyond Imaginable Intelligence by Aubrey de Grey|Self-improving AI Could Bootstrap Beyond Imaginable Intelligence by Aubrey de Grey]] > “"Deep Learning" algorithms are now showing us how to use artificial neural networks in ways that come closer than ever before to delivering learning on a grand scale. But we probably need "deep culture", as well as deep learning, if we are ever to press genuine hyper-intelligence from the large databases that drive our best probabilistic learning machines.” > **— Andy Clark**, *2013, Edge Annual Question, “What *should* We Be Worried About?”* [[reminders/AI Control/Super-a.i.s Won't Rule the World Unless They Get Culture First by Andy Clark|Super-a.i.s Won't Rule the World Unless They Get Culture First by Andy Clark]] > “My stomach hurts. I tell this to my wife and she suggests a medicine in the cabinet that she remembers I have used before and reminds me that it helped. Now, suppose that this was not my wife but a computer? Is it an ad? Does it matter? Can we do this. Yes. AI technology could easily employ models of people and there needs. (But, today, we are busy with key words.)” > **— Roger Schank**, *2016, Edge Annual Question, “What Do You Consider The Most Interesting Recent [Scientific] News? What Makes It Important?”* [[reminders/AI Control/Targeted Advertising Foreshadows Personalized AI by Roger Schank|Targeted Advertising Foreshadows Personalized AI by Roger Schank]] > “Neuroscientists have discovered that dopamine neurons, found in the brains of all vertebrates, are central to reward learning. The transient responses of dopamine neurons signal to the brain predictions for future reward, which are used to guide behavior and regulate synaptic plasticity. The dopamine responses have the same properties as the temporal difference learning algorithm used in TD-Gammon. Reinforcement learning was dismissed years ago as too weak a learner to handle the complexity of cognition. This belief needs to be re-evaluated in the light of the successes of TD-Gammon and learning algorithms in other areas of AI.” > **— Terrence J. Sejnowski**, *2007, Edge Annual Question, “What Are You Optimistic About?”* [[reminders/AI Control/The Brain Learns Like a Temporal Difference Algorithm by Terrence J. Sejnowski|The Brain Learns Like a Temporal Difference Algorithm by Terrence J. Sejnowski]] > “I believe (I know—but can't prove!) that scientists will soon understand the physiological basis of the "cognitive spectrum," from the bright violet of tightly-focused analytic thought all the way down to the long, slow red of low-focus sleep thought—also known as "dreaming." Once they understand the spectrum, they'll know how to treat insomnia, will understand analogy-discovery (and therefore creativity), and the role of emotion in thought—and will understand that thought takes place not only when you solve a math problem but when you look out the window and let your mind wander. Computer scientists will finally understand the missing mystery ingredient that made all their efforts to simulate human thought such naive, static failures, and turned this once-thriving research field into a ghost town. (Their failures were "static" insofar as people think in different ways at different times—your energetic, wide-awake mind works very differently from your tired, soon-to-be-sleeping mind; but artificial intelligence programs always "thought" in the same way all the time.)” > **— David Gelernter**, *2005, Edge Annual Question, “What Do You Believe Is True Even Though You Cannot Prove It?”* [[reminders/AI Control/The Cognitive Spectrum May Explain Why AI Cannot Simulate Human Thought by David Gelernter|The Cognitive Spectrum May Explain Why AI Cannot Simulate Human Thought by David Gelernter]]