## Tags - Part of: [[Science]] - Related: - Includes: [[Artificial Intelligence]], [[Collective Intelligence]], [[General intelligence]], [[Artificial General Intelligence]], [[Theory of Everything in Intelligence]], [[Biological intelligence]], [[Artificial Intelligence x Biological Intelligence]], [[Artificial Intelligence x Biological Intelligence x Collective Intelligence]], [[Mathematics]] - Additional: ## Main resources - [Intelligence - Wikipedia](https://en.wikipedia.org/wiki/Intelligence) <iframe src="https://en.wikipedia.org/wiki/Intelligence" allow="fullscreen" allowfullscreen="" style="height:100%;width:100%; aspect-ratio: 16 / 5; "></iframe> ## Landscapes - By origin - [[Biological intelligence]] - [[Artificial Intelligence]] - [[Artificial General Intelligence]] - [[Superintelligence]] - [[Collective Intelligence]] - By type - [[General intelligence]] - [[Artificial General Intelligence]] - [[Superintelligence]] - [[Collective Intelligence]] - [[Omniintelligence]] - Comparisions - [[Artificial Intelligence x Biological Intelligence]] - [[Artificial Intelligence x Biological Intelligence x Collective Intelligence]] - [[Artificial Intelligence]] - [[Theory of Everything in Intelligence]] - [[Human intelligence amplification]] ## Definitions - There are different classes of definitions: behavioral definitions (treating the system as a black box, measuring its behavior and how its performing the tasks from the outside), and cognitivist definitions (defining, looking for and measuring (mathematical) patterns inside the system while its performing tasks). - What IQ tests try to measure: [[G factor]] [g factor (psychometrics) - Wikipedia](https://en.wikipedia.org/wiki/G_factor_(psychometrics)) - Intelligence is compression - [An Observation on Generalization Ilya Sutskever - YouTube](https://www.youtube.com/watch?v=AKMuA_TVz3A) - [\[2404.09937\] Compression Represents Intelligence Linearly](https://arxiv.org/abs/2404.09937) - “Intelligence is the ability to make models.” -[[Joscha Bach]] ([What Is Intelligence? | Joscha Bach - YouTube](https://www.youtube.com/watch?v=0tUdamQnh4w) , [A surprising new definition of intelligence - @DAIHeidelberg\_Official - YouTube](https://www.youtube.com/watch?v=aJCVnu6M2qQ)) - [\[1911.01547\] On the Measure of Intelligence by Francois Chollet](https://arxiv.org/abs/1911.01547) 1. Intelligence as a collection of task-specific skills: This view sees intelligence as a set of specific, relatively static abilities or programs that collectively implement "intelligence". It is exemplified by Marvin Minsky's perspective in "The Society of Mind" (1986), where intelligence is viewed as a wide collection of vertical, relatively static programs that collectively implement "intelligence". 2. Intelligence as a general learning ability: This view sees intelligence as the general ability to acquire new skills through learning - an ability that could be directed to a wide range of previously unknown problems. This perspective is aligned with the idea of the mind as a flexible, adaptable, highly general process that turns experience into behavior, knowledge, and skills. "These two characterizations map to Catell's 1971 theory of fluid and crystallized intelligence (Gf-Gc)], which has become one of the pillars of the dominant theory of human cognitive abilities, the Cattell-Horn-Caroll theory (CHC) They also relate closely to two opposing views of the nature of the human mind that have been deeply influential in cognitive science since the inception of the field: one view in which the mind is a relatively static assembly of special-purpose mechanisms developed by evolution, only capable of learning what is it programmed to acquire, and another view in which the mind is a general-purpose "blank slate" capable of turning arbitrary experience into knowledge and skills, and that could be directed at any problem." 3. Legg and Hutter's 2007 summary definition: "Intelligence measures an agent's ability to achieve goals in a wide range of environments." 4. Minsky's 1968 definition of AI: "AI is the science of making machines capable of performing tasks that would require intelligence if done by humans" 5. McCarthy's definition of AI (paraphrased): "AI is the science and engineering of making machines do tasks they have never seen and have not been prepared for beforehand" 6. Chollet's informal definition: "The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty." Chollet's formal definition: "Intelligence of system IS over scope (sufficient case): I^θT_IS,scope = Avg_T∈scope (ω_T · θ_T Σ_C∈Cur^θT_T (P_C · GD^θT_IS,T,C / (P^θT_IS,T + E^θT_IS,T,C)))" An informal restatement of the formal definition: "Intelligence is the rate at which a learner turns its experience and priors into new skills at valuable tasks that involve uncertainty and adaptation." 9. Binet's 1916 definition: "It seems to us that in intelligence there is a fundamental faculty, the alteration or the lack of which, is of the utmost importance for practical life. This faculty is the faculty of adapting one's self to circumstances." - [KARL FRISTON - INTELLIGENCE 3.0 - YouTube](https://www.youtube.com/watch?v=V_VXOdf1NMw) 1. Karl Friston's view: Intelligence is the capacity to accumulate evidence for a generative model of one's sensed world, also known as self-evidencing. 2. Shane Legg's definition: The ability of an agent to solve a variety of tasks in different environments. 3. Francois Chollet's definition: Efficiently creating generalizing abstractions from limited prior experience. 4. Pei Wang's definition: The adaptation efficiency over finite resources. 5. A general characterization: Intelligence involves the ability to plan, imagine scenarios, and have narratives that play out internally before selecting and committing to a course of action. 6. An information-theoretic view: Intelligence can be seen as a process of belief updating at different time scales. 7. A physics-based definition: Intelligence emerges from systems that minimize free energy and maximize model evidence. 8. A curiosity-driven view: Intelligence involves actively seeking out information to resolve uncertainty about the world. 9. An activist perspective: Intelligence requires embodiment and active engagement with the environment in a cybernetic loop. 10. A hierarchical view: Intelligence involves the ability to create and manipulate abstractions at various levels of complexity. 11. A social construct: Intelligence can be seen as the ability to infer and adapt to social norms and expectations. 12. A predictive processing view: Intelligence is the ability to minimize prediction errors about the world and oneself. - [\[0706.3639\] A Collection of Definitions of Intelligence](https://arxiv.org/abs/0706.3639) [\[0712.3329\] Universal Intelligence: A Definition of Machine Intelligence](https://arxiv.org/abs/0712.3329) 1. "Intelligence measures an agent's ability to achieve goals in a wide range of environments." - Legg and Hutter 2. "The ability to acquire and apply knowledge and skills." - Compact Oxford English Dictionary 3. "The capacity to acquire and apply knowledge." - The American Heritage Dictionary 4. "Individuals differ from one another in their ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought." - American Psychological Association 5. "Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience." - Common statement with 52 expert signatories 6. "The ability to learn, understand and make judgments or have opinions that are based on reason" - Cambridge Advanced Learner's Dictionary 7. "...ability to adapt effectively to the environment, either by making a change in oneself or by changing the environment or finding a new one ... intelligence is not a single mental process, but rather a combination of many mental processes directed toward effective adaptation to the environment." - Encyclopedia Britannica 8. "The general mental ability involved in calculating, reasoning, perceiving relationships and analogies, learning quickly, storing and retrieving information, using language fluently, classifying, generalizing, and adjusting to new situations." - Columbia Encyclopedia 9. "Capacity for learning, reasoning, understanding, and similar forms of mental activity; aptitude in grasping truths, relationships, facts, meanings, etc." - Random House Unabridged Dictionary 10. "The ability to learn, understand, and think about things." - Longman Dictionary of Contemporary English 11. "Intelligence A: the biological substrate of mental ability, the brains' neuroanatomy and physiology; Intelligence B: the manifestation of intelligence A, and everything that influences its expression in real life behavior; Intelligence C: the level of performance on psychometric tests of cognitive ability." - H. J. Eysenck 12. "An intelligence is the ability to solve problems, or to create products, that are valued within one or more cultural settings." - Howard Gardner 13. "Intelligence is the ability to learn, exercise judgment, and be imaginative." - J. Huarte 14. "A global concept that involves an individual's ability to act purposefully, think rationally, and deal effectively with the environment." - David Wechsler 15. "...the ability of a system to act appropriately in an uncertain environment, where appropriate action is that which increases the probability of success, and success is the achievement of behavioral subgoals that support the system's ultimate goal." - J. S. Albus 16. "Achieving complex goals in complex environments" - Ben Goertzel 17. "Intelligence is the ability to use optimally limited resources – including time – to achieve goals." - Ray Kurzweil 18. "Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines." - John McCarthy 19. "...the ability to solve hard problems." - Marvin Minsky 20. "Intelligence is the ability for an information processing system to adapt to its environment with insufficient knowledge and resources." - Pei Wang - Intelligence is thermodynamics: - [Beff Jezos](https://x.com/BasedBeffJezos/status/1759054407734534516) - [[Jeremy England]]: [No Turning Back: The Nonequilibrium Statistical Thermodynamics of becoming (and remaining) Life-Like - YouTube](https://www.youtube.com/watch?v=10cVVHKCRWw) - [Asking my followers](https://x.com/burny_tech/status/1840950943258439758) - There are different types of definitions of intelligence. These different types of definitions apply to different types of already existing systems, such as us, other organisms, machines by us such as AI software, robots, etc., that all use to eachother similar but different architectures, they learned using similar but different data forming similar but different representations of it. If we consider every possible definition of intelligence as a continuous dimension (Compressive intelligence! Agentic intelligence! General intelligence!), then each physical system is a discrete point in this high dimensional space. This diversity of different existing intelligences in our world will only grow. ## Idealizations - [[AIXI]] - ![[AIXI#Technical summaries]] - [[Godel machine]] - ![[Godel machine#Technical summaries]] ## Future - [[Computronium]] - From [The Singularity Is Nearer - Wikipedia](https://en.wikipedia.org/wiki/The_Singularity_Is_Nearer) by [[Ray Kurzweil]]: [[Images/4ee554bf075eb3a5879c61c1d14e1e51_MD5.jpeg|Open: Pasted image 20240919001041.png]] ![[Images/4ee554bf075eb3a5879c61c1d14e1e51_MD5.jpeg]] ## Deep dives - [[Theory of Everything in Intelligence#Definitions]] - ![[Theory of Everything in Intelligence#Definitions]] ## Brainstorming [[Thoughts intelligence 3]] [[Thoughts intelligence 2]] [[Thoughts intelligence]] [[Thoughts comparing AI and biological intelligence]] My current favorite definition of intelligence is: Intelligence is the ability to generalize, the ability to mine previous experience to make sense of future novel situations. Formalized by Chollet here. It seems that one of the main cruxes of the battle for definitions of intelligence stems from people asking: Is the human intelligence, which is shaped by evolution, a collection of special-purpose programs, or a general-purpose blank slate that can be filled with any computations, or combination of both, something in the middle, or something else? So I would say my favorite definition is definition of general intelligence. While there is also narrow intelligence. You could label all other definitions of intelligence as "x" intelligence depending on the definition. :D Compressive intelligence! Agentic intelligence! General intelligence! [\[1911.01547\] On the Measure of Intelligence](https://arxiv.org/abs/1911.01547) The fact that self-organizing particles with emergent molecules with emergent cells with emergent brains can think is mindblowing me daily. The fact that we made sand think, aka AI algorithms on digital computers, is mindblowing me daily. The intelligence of a system is the extent to which it avoids getting stuck in local minima Intelligence isn't being a stochastic parrot, but being a generalizing circuit grokking agent "Intelligence isn't the ability to remember and repeat, like they teach you in school. It is the ability to learn from experience, solve problems, and use our knowledge to adapt to new situations." https://x.com/ProfFeynman/status/1815772030270075304 Is the human intelligence, which is shaped by evolution, a collection of special-purpose programs, or a general-purpose blank slate that can be filled with any computations, or combination of both, something in the middle, or something else? Too many people "define" intelligence by just behavioral subjective vibes instead of defining it by some rigorous scientific mathematical engineering definition that you can measure and localize concretely like Chollet's that actually isn't worthless scientifically. Learning is unlocking nodes in a nested skill tree of crystalized intelligence Eat a lot of quality training data and scaling laws will apply to you ## Definitions 2 - [[Free energy principle]], [[Active Inference]] - [KARL FRISTON - INTELLIGENCE 3.0 - YouTube](https://youtu.be/V_VXOdf1NMw?si=YuVfcfc0R_jrjZqW) - [Active InferenceThe Free Energy Principle in Mind, Brain, and Behavior | Books Gateway | MIT Press](https://direct.mit.edu/books/oa-monograph/5299/Active-InferenceThe-Free-Energy-Principle-in-Mind) - Shane Legg: - [\[0706.3639\] A Collection of Definitions of Intelligence](https://arxiv.org/abs/0706.3639) - [\[0712.3329\] Universal Intelligence: A Definition of Machine Intelligence](https://arxiv.org/abs/0712.3329) - [Shane Legg (DeepMind Founder) - 2028 AGI, Superhuman Alignment, New Architectures - YouTube](https://www.youtube.com/watch?v=Kc1atfJkiJU) - [Definitions Intelligence](https://agisi.org/Defs_intelligence.html) - [[Generalization]] - [[Intelligence x Generalization]] - [[Intelligence as compression]] - [\[2404.09937\] Compression Represents Intelligence Linearly](https://arxiv.org/abs/2404.09937) - [Intelligence Via Information Compression | IEEE Computer Society](https://www.computer.org/publications/tech-news/community-voices/intelligence-via-compression-of-information) ## Resources [[Links intelligence]] [[Resources intelligence]] [[Links comparing AI and biological intelligence Ne]] ## Written by AI (may include factually incorrect information) # Comprehensive Topics in Intelligence ## Mathematical and Theoretical Foundations - **Algorithmic Complexity (Kolmogorov Complexity):** A formal measure of the information content of an object, defined as the length of the shortest computer program that can produce that object (reflecting the minimal computational resources needed to specify it) ([Kolmogorov complexity - Wikipedia](https://en.wikipedia.org/wiki/Kolmogorov_complexity#:~:text=mathematics%20,generalization%20of%20classical%20information%20theory)). - **Information Theory:** The mathematical study of information and communication, quantifying data in terms of entropy and other measures, and establishing limits for data compression and transmission (pioneered by Claude Shannon in the 1940s) ([Information theory - Wikipedia](https://en.wikipedia.org/wiki/Information_theory#:~:text=Information%20theory%20is%20the%20mathematical,3)). - **Bayesian Reasoning and Probabilistic Inference:** Methods of reasoning under uncertainty that use Bayes’ theorem and probability distributions to update beliefs or hypotheses as new evidence is observed, providing a principled framework for learning from data ([Bayesian Inference Definition | DeepAI](https://deepai.org/machine-learning-glossary-and-terms/bayesian-inference#:~:text=Bayesian%20inference%20is%20a%20method,the%20foundation%20for%20Bayesian%20statistics)). - **Decision Theory:** The study of optimal decision-making, modeling how rational agents should choose actions under uncertainty by maximizing expected utility and using probability to account for risk ([Decision theory - Wikipedia](https://en.wikipedia.org/wiki/Decision_theory#:~:text=Decision%20theory%20or%20the%20theory,the%20study%20of%20real%20human)). - **Game Theory:** The study of mathematical models of strategic conflict and cooperation between intelligent, rational decision-makers, analyzing how agents make decisions in multi-agent scenarios (with concepts like Nash equilibria to predict outcomes in games) ([Game theory | Semantic Scholar](https://www.semanticscholar.org/topic/Game-theory/17593#:~:text=Game%20theory%20is%20,makers.%22%20Game%20theory%20is%20mainly%E2%80%A6%C2%A0Expand)). - **Dynamical Systems (Complex Systems):** An approach that models intelligence and cognition as evolving processes over time, using differential or difference equations to describe how system states change, often revealing attractors, chaos, or other complex behaviors underlying cognitive processes ([Dynamical systems theory - Wikipedia](https://en.wikipedia.org/wiki/Dynamical_systems_theory#:~:text=Dynamical%20systems%20theory%20is%20an,When%20the%20time)) ([Dynamical systems theory - Wikipedia](https://en.wikipedia.org/wiki/Dynamical_systems_theory#:~:text=This%20theory%20deals%20with%20the,chaotic%20systems%20and%20bizarre%20systems)). - **Control Theory:** An interdisciplinary field that deals with the behavior of dynamical systems with inputs and uses feedback to modify outputs, developing algorithms (e.g. PID controllers, optimal control) to steer systems toward desired goals – foundational in engineering and adaptive intelligent systems ([](https://cosmosandhistory.org/index.php/journal/article/download/744/1274/3284#:~:text=Control%20theory%20deals%20with%20the,In%20his%20seminal%20work%2C%20Richard)). - **Symbolic Logic and Automated Reasoning:** The branch of logic that represents propositions and reasoning through formal symbols and rules, enabling rigorous proofs and inference; it underpins symbolic AI (e.g. theorem provers and knowledge representation) by providing formal languages (propositional, predicate logic) for intelligent reasoning ([Mathematical logic - Wikipedia](https://en.wikipedia.org/wiki/Mathematical_logic#:~:text=Mathematical%20logic%20emerged%20in%20the,5%20%5D%20Before)). ## Machine Learning and Artificial Intelligence - **Neural Networks (Deep Learning):** Computational models inspired by biological brains, composed of layers of interconnected “neurons” that adjust connection weights through learning; they excel at approximating complex functions and have achieved breakthroughs in pattern recognition and AI by learning from large data (modern deep neural networks with many layers) ([Neural network (machine learning) - Wikipedia](https://en.wikipedia.org/wiki/Neural_network_\(machine_learning\)#:~:text=In%20machine%20learning%2C%20a%20neural,2)). - **Reinforcement Learning:** An area of machine learning in which an agent learns to make sequential decisions by interacting with an environment and receiving feedback in the form of rewards or punishments, thereby optimizing its behavior to maximize cumulative reward over time ([Machine learning - Wikipedia](https://en.wikipedia.org/wiki/Machine_learning#:~:text=Reinforcement%20learning%20is%20an%20area,MDP)). - **Statistical Learning Theory:** A theoretical framework for machine learning that draws on statistics and functional analysis to analyze learning algorithms, providing guarantees about generalization (e.g. bounds on prediction error) and concepts like VC-dimension and PAC-learning to formally understand how algorithms learn from finite data ([Statistical learning theory - Wikipedia](https://en.wikipedia.org/wiki/Statistical_learning_theory#:~:text=Statistical%20learning%20theory%20is%20a,fields%20such%20as%20computer%20vision)). - **Evolutionary Computation (Genetic Algorithms):** A class of optimization and learning algorithms inspired by biological evolution, which simulate processes like natural selection, mutation, and recombination on a population of candidate solutions to evolve increasingly fit solutions to a problem over successive generations ([Evolutionary computation - Wikipedia](https://en.wikipedia.org/wiki/Evolutionary_computation#:~:text=Evolutionary%20computation%20from%20computer%20science,207%20or%20stochastic%20optimization%20character)). - **Swarm Intelligence:** Techniques inspired by the collective behavior of decentralized, self-organized systems in nature (such as ant colonies, bee swarms, or bird flocking) – these algorithms (e.g. ant colony optimization, particle swarm optimization) use simple agents whose local interactions produce emergent problem-solving capabilities for optimization and control ([How do Swarm Intelligence Work? By Harnessing the Magic of Collective Intelligence | by Poondru Prithvinath Reddy | Medium](https://medium.com/@ppreddy576/how-do-swarm-intelligence-work-by-harnessing-the-magic-of-collective-intelligence-7191c8796b9d#:~:text=Swarm%20intelligence%20is%20the%20collective,swarm%20intelligence%20in%20natural%20systems)). - **Artificial General Intelligence (AGI):** A hypothetical form of AI that would possess general-purpose intelligence at a human level (or beyond), meaning it could understand, learn, and apply knowledge to perform the full range of cognitive tasks across different domains, in contrast to narrow AI that is limited to specific tasks ([Artificial general intelligence - Wikipedia](https://en.wikipedia.org/wiki/Artificial_general_intelligence#:~:text=Artificial%20general%20intelligence%20,2)) ([Artificial general intelligence - Wikipedia](https://en.wikipedia.org/wiki/Artificial_general_intelligence#:~:text=Unlike%20artificial%20narrow%20intelligence%20,5)). ## Cognitive and Biological Intelligence - **Psychometrics and the _g_ Factor:** The field of study that measures human intelligence and cognitive abilities through standardized tests and statistical models; a key result is the general intelligence factor (_g_), a construct identified via factor analysis that accounts for the positive correlations among performance on diverse mental tasks (often quantified by IQ scores as an estimate of _g_) ([g factor (psychometrics) - Wikipedia](https://en.wikipedia.org/wiki/G_factor_\(psychometrics\)#:~:text=The%20g%20factor,individuals%27%20standing%20on%20the%20g)). - **Formal Models of Cognition (Cognitive Architectures):** Theoretical and computational models that represent human cognitive processes in a formal way (e.g. memory, attention, problem-solving), often implemented as cognitive architectures like ACT-R or Soar, which provide a structured framework for simulating and understanding how the mind performs tasks and integrates various cognitive functions ([Cognitive model - Wikipedia](https://en.wikipedia.org/wiki/Cognitive_model#:~:text=A%20cognitive%20model%20is%20a,3)) ([Cognitive model - Wikipedia](https://en.wikipedia.org/wiki/Cognitive_model#:~:text=Some%20of%20the%20most%20popular,LIDA%20%2C%20and%20%2080)). - **Theoretical Neuroscience (Computational Neuroscience):** An interdisciplinary field that uses mathematical models, analysis, and computer simulations to understand the principles of nervous system function – linking neural activity to cognition and behavior by modeling neurons and neural circuits, and explaining how learning and information processing emerge in the brain ([Computational neuroscience - Wikipedia](https://en.wikipedia.org/wiki/Computational_neuroscience#:~:text=Computational%20neuroscience%20,4)). - **Computational Psychiatry:** An emerging field that applies computational models and theories (from machine learning, decision theory, and neuroscience) to psychiatry, with the aim of understanding mental disorders in terms of aberrant computations – for example, modeling how learning or decision-making may differ in conditions like depression or schizophrenia, and using these models to improve diagnosis and treatment ([](https://consortium-psy.com/jour/article/viewFile/11244/pdf#:~:text=definition%20proposed%20by%20Montague%20et,Under%20this%20umbrella%2C%20%E2%80%98aspects)). - **Computational Linguistics (Natural Language Processing):** An interdisciplinary field at the intersection of linguistics and computer science that focuses on the computational modeling of natural language – enabling machines to parse, interpret, and generate human language using algorithms and formal grammars, driven both by theoretical insights into language structure and practical applications like machine translation, speech recognition, and information retrieval ([Computational Linguistics - Uppsala University]([https://www.uu.se/en/department/linguistics-and-philology/research/computational-linguistics.html#:~:text=Computational%20Linguistics%2C%20or%20Language%20Technology%2C,AI%20is%20reshaping%20the%20field](https://www.uu.se/en/department/linguistics-and-philology/research/computational-linguistics.html#:~:text=Computational%20Linguistics%2C%20or%20Language%20Technology%2C,AI%20is%20reshaping%20the%20field))). ## Philosophical and Advanced Theories - **Formal Models of Consciousness:** The development of theoretical frameworks that attempt to quantify or explain consciousness in formal terms, such as Integrated Information Theory (which proposes a mathematical quantity Φ to measure the integration of information in a system as an indicator of consciousness) and Global Workspace Theory (which models conscious awareness as a global sharing of information in the brain); these models seek to bridge the gap between physical processes and subjective experience with rigorous, testable measures ([Integrated information theory - Wikipedia](https://en.wikipedia.org/wiki/Integrated_information_theory#:~:text=Integrated%20information%20theory%20,3)). Each topic above is supported by foundational research and formal theories, reflecting the breadth of intelligence from abstract mathematical underpinnings to biological and artificial implementations. The references cited include seminal works and authoritative sources that have established these concepts in the study of intelligence. # Intelligence and Cognition: A Comprehensive Overview ## Human Intelligence and Cognition ### Theories and Frameworks of Human Intelligence - **General Intelligence (g factor)** – Spearman’s concept of an underlying general mental ability that is present in varying degrees across different cognitive tasks ([Charles E. Spearman | Statistical analysis, Intelligence testing, Factor analysis | Britannica](https://www.britannica.com/biography/Charles-E-Spearman#:~:text=Charles%20E,degrees%20in%20different%20human%20abilities)). This “g factor” implies a single, general intelligence influencing performance on all intellectual tasks. - **Fluid Intelligence** – The capacity to solve novel problems and reason abstractly, independent of acquired knowledge ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=match%20at%20L656%20Fluid%20Intelligence)). Fluid intelligence involves on-the-spot problem-solving and pattern recognition, peaking in early adulthood and tending to decline with age ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=intelligence,greater%20gains%20than%20crystallized%20abilities)). - **Crystallized Intelligence** – The accumulation of knowledge and skills over time, including vocabulary and factual information ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=Crystalized%20Intelligence)). In contrast to fluid abilities, crystallized intelligence relies on long-term memory and experience and typically grows or remains stable with age ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=intelligence,greater%20gains%20than%20crystallized%20abilities)). - **Multiple Intelligences** – Gardner’s theory that human intelligence is not a single general ability but comprises distinct modalities such as linguistic, logical-mathematical, spatial, musical, kinesthetic, interpersonal, and intrapersonal intelligences ([Theory of multiple intelligences - Wikipedia](https://en.wikipedia.org/wiki/Theory_of_multiple_intelligences#:~:text=ImageThe%20intelligence%20modalities)). Each type of intelligence represents a different way of processing information and excelling in particular domains. - **Triarchic Theory of Intelligence** – Sternberg’s framework defining intelligence in three components: **analytical intelligence** (problem-solving and academic reasoning skills), **creative intelligence** (ability to handle novel situations and generate new ideas), and **practical intelligence** (ability to adapt to one’s environment and “street smarts” in everyday situations) ([7.4 What Are Intelligence and Creativity? - Psychology 2e | OpenStax](https://openstax.org/books/psychology-2e/pages/7-4-what-are-intelligence-and-creativity#:~:text=Robert%20Sternberg%20developed%20another%20theory,12)). This theory posits that successful intelligence involves balancing these three aspects. - **Emotional Intelligence (EQ)** – The ability to perceive, use, understand, and manage emotions effectively ([Managing the Challenges of Life and Work With Emotional Intelligence](https://www.infectioncontroltoday.com/view/managing-challenges-life-work-emotional-intelligence#:~:text=Emotional%20intelligence%2C%20also%20known%20as,I%20used)). High emotional intelligence involves recognizing one’s own and others’ emotions, empathizing, and regulating emotional responses, which is crucial for social competency and leadership. - **Social Intelligence** – The capacity to understand and navigate social situations effectively, including awareness of social cues, empathy, and interpersonal skills ([Social intelligence - Wikipedia](https://en.wikipedia.org/wiki/Social_intelligence#:~:text=The%20original%20definition%20of%20social,74)) ([Social intelligence - Wikipedia](https://en.wikipedia.org/wiki/Social_intelligence#:~:text=Capacity%20to%20know%20oneself%20and,to%20know%20others)). Social intelligence enables one to “act wisely in human relations” (as described by Thorndike) by interpreting others’ behaviors and responding appropriately in social interactions ([Social intelligence - Wikipedia](https://en.wikipedia.org/wiki/Social_intelligence#:~:text=The%20original%20definition%20of%20social,74)). - **Theory of Mind** – The ability to attribute mental states (beliefs, intentions, desires, knowledge, etc.) to others and understand that others have perspectives and knowledge different from one’s own ([Theory of Mind in Different Dementia Profiles - Psychiatry Online](https://psychiatryonline.org/doi/10.1176/jnp.2009.21.1.100#:~:text=Online%20psychiatryonline,permits%20the%20understanding%20and)). This faculty is a key aspect of social cognition, allowing humans to predict and interpret others’ behavior; in AI, developing a theory of mind for agents is an ongoing research challenge in multi-agent systems and human-AI interaction. - **Creativity** – The capacity to produce work or ideas that are both novel (original, innovative) and appropriate (useful or relevant to the task at hand) ([Spontaneous and deliberate modes of creativity: Multitask eigen ...](https://pmc.ncbi.nlm.nih.gov/articles/PMC8497437/#:~:text=Spontaneous%20and%20deliberate%20modes%20of,Sternberg%20and%20Lubart%2C%201999)). Creativity is considered a facet of intelligence involving divergent thinking and problem-solving; it underlies creative problem-solving and is sometimes termed “creative intelligence” in broader theories of mind. ### Cognitive Processes and Abilities - **Perception** – The process of organizing, identifying, and interpreting sensory information to form an understanding of the environment ([Perception - Wikipedia](https://en.wikipedia.org/wiki/Perception#:~:text=Perception%20,416%20involves%20pressure%20waves)). Human perception integrates signals from the senses to create a coherent model of the world (e.g. visual perception interprets light patterns as objects). *(In AI, **computer vision** seeks to achieve analogous interpretation of images and videos by machines.)* - **Attention** – The cognitive process of selectively concentrating on certain information while ignoring other perceivable information ([Attention – Cognition](https://pressbooks.pub/cognition/chapter/attention/#:~:text=Focused%20attention)). Attention allows the mind to focus resources on relevant stimuli (enhancing perception and memory of those stimuli) and filter out distractions ([Attention – Cognition](https://pressbooks.pub/cognition/chapter/attention/#:~:text=Attention%20is%20the%20behavioral%20and,1)). It is closely linked to working memory and executive control in complex tasks. - **Short-Term Memory (STM)** – A limited-capacity store that holds a small amount of information (for on the order of seconds) without rehearsal ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=Short)). STM allows one to briefly retain and manipulate information, such as a phone number just heard, before it is either encoded into long-term memory or forgotten. - **Working Memory** – A framework for the active maintenance and manipulation of information over short periods, supporting complex cognitive tasks like reasoning and learning ([ A multi-component, adaptive Working Memory Assessment Battery (WoMAB): validation and norms in an Italian population sample - PMC ](https://pmc.ncbi.nlm.nih.gov/articles/PMC8789625/#:~:text=Working%20memory%20,2)). It is essentially an enhanced conceptualization of short-term memory that includes components for storing verbal information, visual/spatial information, and a central executive that allocates attention ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=Working%20Memory)) ([ A multi-component, adaptive Working Memory Assessment Battery (WoMAB): validation and norms in an Italian population sample - PMC ](https://pmc.ncbi.nlm.nih.gov/articles/PMC8789625/#:~:text=Working%20memory%20,2)). - **Long-Term Memory (LTM)** – The relatively permanent store of knowledge, skills, and experiences ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=Linguistic%20relativity)) ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=Long)). LTM is typically divided into subtypes: e.g., **episodic memory** (memory for personal events and experiences situated in time and place ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=Episodic%20Memory))), **semantic memory** (memory for general facts, concepts, and knowledge about the world ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=Semantic%20Memory))), and **procedural memory** (memory for skills and how to perform tasks, often unconscious or implicit ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=Procedural%20Memory))). - **Episodic Memory** – The ability to recall personally experienced events, complete with contextual details of time and place ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=Episodic%20Memory)). This type of memory, a subset of long-term memory, enables one to mentally relive past experiences (e.g. remembering one’s last birthday party). - **Semantic Memory** – Memory for general factual knowledge, concepts, and meanings – the store of information that is not tied to personal experience ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=Semantic%20Memory)). Semantic memory allows people to remember facts (like Paris is the capital of France) and the meanings of words, underpinning language and conceptual thinking. - **Procedural Memory** – Long-term memory for skills and procedures – the “how to” knowledge of performing tasks (such as riding a bicycle or typing) ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=Procedural%20Memory)). Procedural memory is demonstrated through performance rather than conscious recall and is considered a form of implicit memory (one can execute the skill without actively thinking about it). - **Learning (Human)** – The process of acquiring new knowledge or skills through experience, practice, or study, resulting in lasting changes in behavior or knowledge. In psychology, major forms of learning include **classical conditioning**, **operant conditioning**, and **observational learning**, among others: - **Classical Conditioning** – A form of associative learning in which a neutral stimulus becomes linked to an unconditioned stimulus that automatically elicits a response, so that eventually the neutral stimulus alone evokes a conditioned response ([Classical Conditioning: Examples and How It Works](https://www.verywellmind.com/classical-conditioning-2794859#:~:text=Discovered%20by%20Russian%20physiologist%20Ivan,before%20a%20naturally%20occurring%20reflex)). (Pavlov’s famous experiments – pairing a bell with food until the bell alone triggers salivation in dogs – exemplify classical conditioning.) - **Operant Conditioning** – A learning process in which behaviors are increased or decreased based on the consequences they produce (rewards or punishments) ([Classical Conditioning: Examples and How It Works](https://www.verywellmind.com/classical-conditioning-2794859#:~:text=Operant%20conditioning%20is%20a%20learning,or%20weaken%20those%20voluntary%20behaviors)). In operant conditioning, voluntary actions are strengthened when followed by reinforcement and weakened when followed by punishment, as formulated by B.F. Skinner. - **Observational Learning** – Learning that occurs by observing and imitating the behavior of others, without direct reinforcement ([Observational learning - Wikipedia](https://en.wikipedia.org/wiki/Observational_learning#:~:text=Observational%20learning%20is%20learning%20that,but%20instead%2C%20requires%20a%20social)). Also known as social learning (pioneered by Albert Bandura), observational learning demonstrates that individuals (especially children) can acquire new behaviors by watching models (e.g. the Bobo doll experiment showed children imitating aggressive acts they saw an adult perform). - **Language Acquisition and Processing** – The cognitive ability to learn and use language, including understanding spoken/written language and producing speech. Humans have an innate capacity for language acquisition (as evidenced by children universally learning language in early years), and the brain’s language processing involves specialized regions (e.g., Broca’s and Wernicke’s areas). *(The AI counterpart is **Natural Language Processing**, which enables computers to interpret and generate human language.)* - **Reasoning** – The process of drawing conclusions and making inferences from premises or evidence. Key forms include **deductive reasoning** (deriving logically certain conclusions from general rules or premises) and **inductive reasoning** (inferring general principles or predictions from specific examples or data). Human reasoning also involves **abductive reasoning** (inferring the most likely explanation). Strong reasoning ability is a core aspect of intelligence, enabling problem-solving and decision-making ([ Executive Functions - PMC ](https://pmc.ncbi.nlm.nih.gov/articles/PMC4084861/#:~:text=2000%20%29%3A%20inhibition%20,success%20in%20school%20and%20in)). - **Problem-Solving** – The cognitive process of devising a solution to a challenge or figuring out how to achieve a desired goal state from a given initial state ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=Problem%20solving)). Human problem-solving may involve applying known strategies, using heuristics (rules of thumb), or engaging in creative insight to overcome obstacles ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=The%20process%20by%20which%20individuals,involves%20engaging%20in%20behavior%20that)). (In AI, **automated planning** algorithms similarly generate sequences of actions to reach goals ([Socratica](https://learn.socratica.com/en/topic/computer-science/artificial-intelligence/planning-and-scheduling#:~:text=In%20the%20context%20of%20AI%2C,This%20involves%20several%20critical%20components)).) - **Decision-Making** – The process of evaluating options and selecting a course of action among alternatives. Human decision-making can be rational but is often influenced by cognitive biases and heuristics. It involves weighing probabilities, risks, and benefits to make choices – from simple everyday decisions to high-stakes judgments. - **Heuristics** – Mental shortcuts or rules of thumb that simplify decision-making and problem-solving, often at the cost of accuracy or optimality ([Glossary – Cognitive Psychology](https://nmoer.pressbooks.pub/cognitivepsychology/back-matter/322/#:~:text=Heuristic)). In cognition, heuristics (e.g. the availability heuristic or representativeness heuristic identified by Tversky & Kahneman) allow faster judgments under uncertainty, but they can lead to systematic errors or biases. - **Cognitive Biases** – Systematic patterns of deviation from rational judgment, whereby subjective perception or heuristics lead to misjudgments ([Cognitive bias - Wikiquote](https://en.wikiquote.org/wiki/Cognitive_bias#:~:text=A%20cognitive%20bias%20is%20a,their%20perception%20of%20the%20input)). Examples include confirmation bias (favoring information that confirms one’s beliefs) and anchoring bias (over-reliance on an initial piece of information). Cognitive biases illustrate the bounded rationality of human decision-making and are important in psychology and behavioral economics. - **Metacognition** – “Thinking about thinking,” or the ability to monitor and control one’s own cognitive processes. Metacognition includes awareness of one’s understanding and strategies (e.g., realizing that you need to double-check an answer, or adjusting study strategies upon recognizing what you haven’t mastered) ([ Executive Functions - PMC ](https://pmc.ncbi.nlm.nih.gov/articles/PMC4084861/#:~:text=Executive%20functions%20,22%20Miyake%20et%20al)). It is crucial for effective learning and self-regulation. - **Executive Functions (Cognitive Control)** – A set of high-level cognitive processes – including inhibitory control (self-control and interference suppression), working memory, and cognitive flexibility – that enable planning, goal-directed behavior, and adaptive responses to novel situations ([ Executive Functions - PMC ](https://pmc.ncbi.nlm.nih.gov/articles/PMC4084861/#:~:text=Executive%20functions%20,22%20Miyake%20et%20al)). These executive processes (associated with the prefrontal cortex) allow one to override impulsive responses, switch tasks, and orchestrate thoughts and actions in accordance with internal goals. (Executive dysfunction can lead to issues with attention and self-regulation, as seen in ADHD and other conditions.) ### Developmental and Biological Foundations - **Cognitive Development** – The study of how thinking and intelligence progress throughout the lifespan, particularly during childhood. Classic work by Jean Piaget described stages of cognitive development (sensorimotor, preoperational, concrete operational, formal operational), in which children’s abilities to understand concepts and solve problems qualitatively change at different ages ([Piaget's theory of cognitive development - Wikipedia](https://en.wikipedia.org/wiki/Piaget%27s_theory_of_cognitive_development#:~:text=He%20believed%20that%20children%20of,logically%2C%20and%20using%20%2095)) ([Piaget's theory of cognitive development - Wikipedia](https://en.wikipedia.org/wiki/Piaget%27s_theory_of_cognitive_development#:~:text=To%20Piaget%2C%20cognitive%20development%20was,work%20received%20the%20greatest%20attention)). Cognitive development results from a combination of biological maturation and environmental experience, with children actively constructing knowledge of the world ([Piaget's theory of cognitive development - Wikipedia](https://en.wikipedia.org/wiki/Piaget%27s_theory_of_cognitive_development#:~:text=To%20Piaget%2C%20cognitive%20development%20was,work%20received%20the%20greatest%20attention)). - **Neural Correlates of Intelligence** – The neuroscience of intelligence examines how brain structure and function underlie cognitive abilities. Evidence from neuroimaging indicates that a network of brain regions (particularly in frontal and parietal lobes) is associated with intellectual performance ([Parieto-Frontal Integration Theory (P-FIT) - A Neural Basis Of Intelligence | Science 2.0](https://www.science20.com/news_account/parieto_frontal_integration_theory_p_fit_a_neural_basis_of_intelligence#:~:text=human%20intelligence.%20Their%20Parieto,frontal%20and%20the%20parietal%20lobes)) ([Parieto-Frontal Integration Theory (P-FIT) - A Neural Basis Of Intelligence | Science 2.0](https://www.science20.com/news_account/parieto_frontal_integration_theory_p_fit_a_neural_basis_of_intelligence#:~:text=The%20data%20suggest%20that%20some,parietal%20networks%20process%20information)). The **Parieto-Frontal Integration Theory (P-FIT)**, for example, proposes that individual differences in intelligence relate to the efficiency of information processing across frontal and parietal brain areas ([Parieto-Frontal Integration Theory (P-FIT) - A Neural Basis Of Intelligence | Science 2.0](https://www.science20.com/news_account/parieto_frontal_integration_theory_p_fit_a_neural_basis_of_intelligence#:~:text=human%20intelligence.%20Their%20Parieto,frontal%20and%20the%20parietal%20lobes)) ([Parieto-Frontal Integration Theory (P-FIT) - A Neural Basis Of Intelligence | Science 2.0](https://www.science20.com/news_account/parieto_frontal_integration_theory_p_fit_a_neural_basis_of_intelligence#:~:text=The%20data%20suggest%20that%20some,parietal%20networks%20process%20information)). In general, greater neural efficiency (efficient information flow and adaptable networks) has been linked to higher intelligence. - **Genetics of Intelligence** – Intelligence has a significant heritable component, as shown by twin and adoption studies in behavior genetics. IQ (and general cognitive ability) is a **polygenic trait**, influenced by many genes each having small effects ([Heritability of IQ - Wikipedia](https://en.wikipedia.org/wiki/Heritability_of_IQ#:~:text=began%20in%20the%20late%20nineteenth,6)). In adults, approximately 50–80% of the variance in IQ scores can be attributed to genetic differences, under typical environmental conditions ([Heritability of IQ - Wikipedia](https://en.wikipedia.org/wiki/Heritability_of_IQ#:~:text=Early%20twin%20studies%20of%20adult,0)). However, gene–environment interactions are important: for example, heritability of IQ is lower in early childhood or in disadvantaged environments ([Heritability of IQ - Wikipedia](https://en.wikipedia.org/wiki/Heritability_of_IQ#:~:text=Early%20twin%20studies%20of%20adult,0)). Modern genomic studies have identified hundreds of genetic loci associated with intelligence, reflecting its complex genetic basis ([Heritability of IQ - Wikipedia](https://en.wikipedia.org/wiki/Heritability_of_IQ#:~:text=began%20in%20the%20late%20nineteenth,6)). - **Intelligence Testing (IQ and Psychometrics)** – **Intelligence Quotient (IQ)** is a numeric index of cognitive ability derived from standardized tests, with a population mean set to 100 ([Solved: Listen What is an intelligence quotient? a measure of ...](https://www.gauthmath.com/solution/1814032290061365/Listen-What-is-an-intelligence-quotient-a-measure-of-intelligence-now-derived-fr#:~:text=,that%20IQ%20is%20a)). IQ tests (such as the Stanford-Binet and Wechsler scales) measure a variety of cognitive skills (like verbal comprehension, working memory, spatial reasoning, processing speed) to produce an overall score. **Psychometrics** is the field concerned with the theory and technique of psychological measurement, and in the context of intelligence it involves test design, validation, and factor analysis of cognitive abilities. IQ scores have been found to correlate with academic and occupational outcomes, though they capture only certain aspects of “intelligence” and can be influenced by socio-cultural factors. (The **Flynn effect** – a steady rise in average IQ scores over generations – is an important observation in psychometrics, indicating the influence of environment on measured intelligence.) ## Artificial Intelligence and Machine Learning ### Fundamental Concepts and Techniques - **Artificial Intelligence (AI)** – The broad field of computer science aimed at creating machines or software that exhibit intelligent behavior – in other words, perform tasks that typically require human intelligence. AI involves the simulation of human intelligence processes by machines (especially computer systems), including the abilities to learn from experience, reason through problems, perceive environments, and understand language ([What is AI? Artificial Intelligence Explained | Definition from TechTarget](https://www.techtarget.com/searchenterpriseai/definition/AI-Artificial-Intelligence#:~:text=Artificial%20intelligence%20,recognition%20%20and%20%2039)). Modern AI encompasses a range of approaches, from symbolic reasoning systems to data-driven machine learning methods. - **Machine Learning (ML)** – A subfield of AI focused on algorithms and statistical techniques that enable computer systems to **learn** from data and improve their performance on tasks with experience. Formally, machine learning is the study of computer algorithms that improve automatically through experience and by using data ([ Machine learning ](https://www.wartsila.com/encyclopedia/term/machine-learning#:~:text=energy)). Rather than being explicitly programmed to solve a problem, an ML system “learns” a model from example data. Machine learning includes various paradigms: - **Supervised Learning** – The paradigm where an algorithm learns from labeled examples (input-output pairs) to predict outputs for new inputs. In supervised learning, the goal is to learn a function mapping inputs to outputs based on example input–output pairs ([Supervised learning](https://www.dcs.gla.ac.uk/~bjorn/sem20172018/ae2public/Supervised_learning.html#:~:text=Supervised%20learning%20is%20the%20machine,correctly%20determine%20the%20class%20labels)) (for instance, predicting house prices from features, given many examples of houses with known prices). - **Unsupervised Learning** – The paradigm of learning patterns or structure from unlabeled data. Unsupervised learning algorithms try to discover hidden structure, groupings, or distributions in data without any explicit correct answers given ([Unsupervised Learning | Definition & Examples - Branch](https://www.branchdev.io/glossary/unsupervised-learning#:~:text=Definition%3A%20,existing%20labels)) (e.g., clustering customers into segments based on purchasing behavior with no prior labels). This approach is useful for exploratory analysis and feature learning. - **Reinforcement Learning (RL)** – An area of machine learning in which an **agent** learns to make sequences of decisions by interacting with an environment and receiving feedback in the form of rewards or penalties ([A brief introduction to reinforcement learning: Q-learning | JFrog ML](https://www.qwak.com/post/a-brief-introduction-to-reinforcement-learning-q-learning#:~:text=Reinforcement%20learning%20is%20an%20area,the%20notion%20of%20cumulative%20reward)). The agent’s objective is to discover a policy (strategy) that maximizes cumulative reward over time. Reinforcement learning is inspired by behavioral conditioning (trial-and-error learning with reinforcement) and has been applied to game playing, robotics, and decision-making tasks (famously, RL algorithms have learned to play complex games like Go at super-human levels by reward feedback). ([A brief introduction to reinforcement learning: Q-learning | JFrog ML](https://www.qwak.com/post/a-brief-introduction-to-reinforcement-learning-q-learning#:~:text=Reinforcement%20learning%20is%20an%20area,the%20notion%20of%20cumulative%20reward)) - **Artificial Neural Networks (ANNs)** – Computing systems inspired by biological neural networks in the brain ([Deep learning - Wikipedia](https://en.wikipedia.org/wiki/Deep_learning#:~:text=Artificial%20neural%20networks%20,computer%20algorithm%20using%20%20378)). An ANN consists of layers of interconnected units or “neurons” that transmit signals to each other. Through a learning process (adjusting connection weights via algorithms like backpropagation), neural networks can learn to approximate functions and recognize patterns from data ([Deep learning - Wikipedia](https://en.wikipedia.org/wiki/Deep_learning#:~:text=Artificial%20neural%20networks%20,computer%20algorithm%20using%20%20378)). They have been extremely successful in tasks like image recognition and speech recognition. *(ANNs were originally inspired by neuroscience; conversely, modern deep learning models now offer computational insights that might inform cognitive neuroscience, showing an interesting overlap between AI and human cognition.)* - **Deep Learning** – A subfield of machine learning (and a development of neural networks) characterized by models with **multiple layers** of artificial neurons (hence “deep” networks). Deep learning uses multilayered neural networks to automatically learn representations of data with multiple levels of abstraction ([What Is Deep Learning? | IBM](https://www.ibm.com/think/topics/deep-learning#:~:text=What%20is%20deep%20learning%3F)). Such deep neural networks have achieved state-of-the-art results in many domains by learning complex features directly from raw data (for example, learning hierarchical visual features from pixels in computer vision). Deep learning has driven recent breakthroughs in AI, powering many modern AI applications from natural language chatbots to autonomous vehicles ([What Is Deep Learning? | IBM](https://www.ibm.com/think/topics/deep-learning#:~:text=What%20is%20deep%20learning%3F)). - **Knowledge Representation and Reasoning (KRR)** – A branch of AI dedicated to how knowledge can be formally represented in a machine-readable form and how reasoning algorithms can draw logical conclusions from that knowledge. KRR studies ways to encode information about the world (facts, concepts, rules) in structures like logic frameworks, semantic networks, or ontologies, so that a computer system can use this information to solve complex problems and infer new knowledge ([What is knowledge representation and reasoning?](https://www.scribbr.com/frequently-asked-questions/what-is-knowledge-representation-and-reasoning/#:~:text=Knowledge%20representation%20and%20reasoning%20,AI%29%20research)). In essence, it bridges human-like symbolic reasoning with computable formats, enabling tasks such as automated theorem proving, commonsense reasoning, and expert systems. - **Automated Reasoning and Logic** – The aspect of AI that deals with enabling computers to reason through logical formulations automatically. Automated reasoning systems use algorithms (often based on formal logic and mathematics) to prove theorems, verify correctness, or infer new statements from given premises ([Automated Reasoning - Stanford Encyclopedia of Philosophy](https://plato.stanford.edu/entries/reasoning-automated/#:~:text=Automated%20Reasoning%20,systems%20that%20automate%20this%20process)). For example, an **automated theorem prover** can take axioms and a hypothesis in formal logic and attempt to produce a proof. This field has yielded tools that can verify software (proving properties of programs) and solve puzzles and logical problems. *(Automated reasoning connects to cognitive science insofar as it models aspects of logical reasoning, though human reasoning is often less formally logical.)* - **Search and Planning Algorithms** – Fundamental AI techniques for problem-solving by exploring state spaces. **Search algorithms** (like breadth-first search, depth-first search, or heuristic-informed search such as A*) systematically explore possible states or solutions to find a path to a goal or an optimal solution. **Automated planning** involves generating a sequence of actions that will achieve a specified goal from an initial state ([Socratica](https://learn.socratica.com/en/topic/computer-science/artificial-intelligence/planning-and-scheduling#:~:text=In%20the%20context%20of%20AI%2C,This%20involves%20several%20critical%20components)). Planning algorithms treat problem-solving as a search in the space of possible action sequences, often employing heuristics to navigate large state spaces efficiently. These methods enable AI systems to perform tasks like route planning, game move calculation, or scheduling sequences of robot actions. - **Evolutionary Algorithms** – A family of optimization and learning techniques inspired by the process of natural evolution. Evolutionary algorithms use mechanisms such as **selection**, **mutation**, and **crossover (recombination)** to iteratively improve a population of candidate solutions ([Evolutionary Algorithms: Techniques & Examples](https://www.vaia.com/en-us/explanations/engineering/artificial-intelligence-engineering/evolutionary-algorithms/#:~:text=Evolutionary%20algorithms%20use%20mechanisms%20inspired,algorithms%20to%20efficiently%20handle%20complex)). Over successive “generations,” solutions that perform better (according to a defined fitness function) are more likely to be retained and varied, leading to increasingly optimal solutions. Examples include genetic algorithms and genetic programming. Such algorithms are effective for optimization problems where traditional methods struggle, and they demonstrate how principles of Darwinian evolution can be harnessed for machine intelligence. ### AI Subfields and Applications - **Natural Language Processing (NLP)** – A subfield of AI and linguistics focused on enabling computers to understand, interpret, and generate human language. NLP encompasses tasks like speech recognition, language understanding, translation, and text generation. It is concerned with the interactions between computers and human (natural) languages ([File (1) (pdf) - Course Sidekick](https://www.coursesidekick.com/computer-science/13374093#:~:text=File%20%281%29%20%28pdf%29%20,computers%20and%20human%20language)) – for example, parsing the syntax and semantics of sentences, or having a dialogue with a user. Modern NLP combines linguistic rules with machine learning (including deep learning on large text corpora) to achieve tasks such as machine translation, sentiment analysis, and conversational agents. - **Computer Vision** – An interdisciplinary field in which AI systems are designed to gain high-level understanding from digital images or videos – essentially, to **see** and interpret visual data much as humans do. Computer vision algorithms enable tasks like object detection, face recognition, and scene understanding by analyzing pixel data from cameras or sensors ([Everything You Need To Know About Computer Vision: A Complete ...](https://www.classicinformatics.com/blog/computer-vision#:~:text=,%E2%80%9D)). Techniques in computer vision include image processing filters, feature extraction methods, and deep neural networks (convolutional neural networks) that learn visual patterns. This allows applications such as autonomous vehicle vision (identifying pedestrians, roads, signs) and medical image analysis. - **Robotics and Embodied AI** – Robotics is the branch of technology that involves the design, construction, and operation of robots – physical machines capable of carrying out tasks in the world. In AI, robotics overlaps by providing the **embodiment** for intelligent agents: AI algorithms for perception, planning, and control are used to enable robots to navigate and manipulate their environment autonomously ([Socratica](https://learn.socratica.com/en/topic/computer-science/artificial-intelligence/planning-and-scheduling#:~:text=In%20the%20context%20of%20AI%2C,This%20involves%20several%20critical%20components)) ([Socratica](https://learn.socratica.com/en/topic/computer-science/artificial-intelligence/planning-and-scheduling#:~:text=Scheduling%2C%20on%20the%20other%20hand%2C,Key%20considerations%20in%20scheduling%20include)). For instance, a mobile robot uses computer vision to perceive obstacles, planning algorithms to decide a path, and control theory to execute movements. Robotics integrates many aspects of intelligence (vision, language, motor control, decision-making) into a situated system. *(Embodied AI emphasizes that intelligence can involve having a body and sensorimotor experience, linking cognitive processes to the physical world similarly to human cognition.)* - **Expert Systems** – AI programs designed to emulate the decision-making ability of a human expert in a specific domain. An expert system consists of a **knowledge base** (containing domain-specific facts and heuristics, often represented as if-then rules) and an **inference engine** that applies these rules to given situations to derive conclusions or recommendations. The system “simulates the judgment and behavior of a human with expert knowledge” in that field ([Expert System In AI](https://medium.com/@wisemonkeysoffpage/expert-system-in-ai-f23ea0d81d90#:~:text=Expert%20System%20In%20AI%20An,behavior%20of%20a%20human)). For example, MYCIN was an early expert system for medical diagnosis of infections. While powerful in narrow domains, expert systems rely on manually encoded knowledge and struggle with tasks requiring learning or common-sense reasoning outside their rules. - **Multi-Agent Systems** – Systems composed of multiple interacting intelligent agents, which may cooperate or compete with each other ([Multi-agent cooperative swarm learning for dynamic layout ...](https://link.springer.com/article/10.1007/s10845-023-02229-7#:~:text=Multi,that%20are%20beyond%20the)). Each agent in a multi-agent system has its own goals or behaviors, and the system as a whole can exhibit complex, emergent behavior from their interactions. Multi-agent systems are used to model and solve problems that are decentralized or distributed in nature, such as teams of robots working together, distributed sensor networks, or economic markets with autonomous trading agents. They require mechanisms for communication, coordination, negotiation, and sometimes competition among agents. *(Understanding multi-agent interactions also relates to social aspects of intelligence; in humans, theory of mind and social intelligence play a role in how we navigate multi-agent (multi-human) environments.)* - **Swarm Intelligence** – An approach to AI and optimization inspired by the collective behavior of social insects and animals (like ant colonies, bee swarms, or flocks of birds). Swarm intelligence systems involve many simple agents (or particles) following simple rules locally, without centralized control, leading to **emergent** global behavior that is intelligent or efficient ([The Collective Power Of Swarm Intelligence In AI And Robotics](https://www.forbes.com/councils/forbestechcouncil/2021/05/13/the-collective-power-of-swarm-intelligence-in-ai-and-robotics/#:~:text=Robotics%20www,quickly%20in%20a%20coordinated)). Examples include ant colony optimization (where simulated ants collectively find shortest paths, inspired by real ants’ pheromone trails) and particle swarm optimization. Swarm intelligence demonstrates how decentralization and self-organization can solve complex problems; it’s applied in robotics (e.g., coordinating fleets of drones) and in solving computational optimization problems by collective agent behavior ([The Collective Power Of Swarm Intelligence In AI And Robotics](https://www.forbes.com/councils/forbestechcouncil/2021/05/13/the-collective-power-of-swarm-intelligence-in-ai-and-robotics/#:~:text=Robotics%20www,quickly%20in%20a%20coordinated)). - **Recommender Systems** – (An application of AI/ML, often considered under machine learning) Recommender systems are algorithms that provide personalized suggestions to users, typically in domains like e-commerce, streaming media, or social networks. By analyzing patterns in user data (e.g. past purchases, movie ratings, or click behavior), these systems infer user preferences and predict which other items the user may like. Techniques include collaborative filtering (recommending items liked by similar users), content-based filtering (recommending items similar to those the user liked before), or hybrid methods. While not a “core” cognitive process, recommender systems illustrate machine intelligence in digesting large-scale human preference data to augment decision-making. ### Advanced and Interdisciplinary Topics - **Artificial General Intelligence (AGI)** – A hypothetical form of AI that possesses general-purpose intelligence at a human level or beyond. Unlike narrow AI systems that excel at specific tasks, AGI would be able to understand, learn, and apply knowledge across a wide range of tasks and domains, demonstrating flexibility and depth of understanding comparable to human cognitive abilities ([The Latest Developments in AI: AGI, Chatbots, Legal Battles, and ...](https://www.toolify.ai/ai-news/the-latest-developments-in-ai-agi-chatbots-legal-battles-and-enhancements-993455#:~:text=,systems%20to%20handle%20unfamiliar)). Achieving AGI remains an open research problem; it would require integrating learning, reasoning, perception, language, and common sense in one architecture. AGI also raises significant ethical and safety considerations, as such an AI would be extremely powerful. - **Cognitive Architectures** – Frameworks for building comprehensive models of human-like cognition in computational terms. A cognitive architecture specifies the fundamental structures (memory systems, knowledge representations) and processes (mechanisms of learning, reasoning, perceiving) of the mind, with the aim of modeling and understanding human cognitive abilities. Examples include **ACT-R** and **Soar**, which provide a set of cognitive modules and rules that can simulate human performance on various tasks ([ACT‐R: A cognitive architecture for modeling cognition - Ritter](https://wires.onlinelibrary.wiley.com/doi/10.1002/wcs.1488#:~:text=ACT,to%20predict%20and%20explain)). Cognitive architectures serve as blueprints for creating AI systems that think in ways analogous to humans, and they are a key intersection of AI and cognitive psychology (used both to test theories of human cognition and to design human-like AI). ([ACT‐R: A cognitive architecture for modeling cognition - Ritter](https://wires.onlinelibrary.wiley.com/doi/10.1002/wcs.1488#:~:text=ACT,to%20predict%20and%20explain)) - **AI Alignment and Ethics** – The study of how to design AI systems that behave in accordance with human values, ethical principles, and intended goals. **AI alignment** refers to ensuring that an AI’s objectives and actions reliably align with the objectives its creators or users intend ([AI alignment - Wikipedia](https://en.wikipedia.org/wiki/AI_alignment#:~:text=AI%20alignment%20,goals%2C%20preferences%2C%20or%20ethical%20principles)). This is especially crucial when imagining very advanced AI or AGI – the so-called *alignment problem* is to prevent an AI from pursuing harmful goals or misinterpreting its instructions in ways that could lead to unintended negative outcomes ([The AI Alignment Problem - LinkedIn](https://www.linkedin.com/pulse/ai-alignment-problem-neven-dujmovic-tbcsf#:~:text=The%20AI%20Alignment%20Problem%20,with%20human%20values%20and)) ([AI alignment - Wikipedia](https://en.wikipedia.org/wiki/AI_alignment#:~:text=AI%20alignment%20,goals%2C%20preferences%2C%20or%20ethical%20principles)). Research in this area spans technical approaches (like reward modeling, constraint specification, or cooperative AI) and broader ethics, such as fairness, transparency, and accountability of AI decisions. Ensuring AI is beneficial and does not cause harm is now recognized as a core aspect of developing and deploying intelligent systems. - **Neuromorphic Computing** – *(Emerging intersection of AI, neuroscience, and hardware engineering)* Neuromorphic computing involves designing computer hardware and architectures inspired by the brain’s neural structure. The idea is to mimic the brain’s event-driven, parallel processing through specialized circuits (e.g., spiking neural networks implemented on chips) to achieve efficient, brain-like information processing. While not yet mainstream, neuromorphic chips aim to run neural network computations with far lower energy consumption, potentially enabling AI systems to operate more like biological brains. This field exemplifies the feedback loop between understanding natural intelligence (neuroscience) and creating artificial intelligence. *(The above list covers a broad spectrum of **intelligence topics** from human cognitive science to artificial intelligence. Many areas overlap: for instance, insights from human cognition (like neural networks inspired by the brain, or cognitive architectures) inform AI, and conversely AI models (like deep networks) offer hypotheses for neuroscience. Understanding these topics provides a foundation for academic exploration into how intelligence works and how it can be emulated in machines.)* More: [[AI-written intelligence]]