[[/public:T-719-NXAI:nxai-25:main|T-719-NXAI-2025 Main]] \\ \\ ***//Apr. 13, 2025: This page is work in progress -- will be ready one week before course starts.//*** \\ \\ ===== T-719-NXAI-2025 READINGS ===== Make sure to read the required number of papers (first number inside the brackets - the second one is the "minimum recommended") and make sure to not fall behind on readings (I assign you only a few papers per day for a good reason - so you can get through them in time for the discussion session on that day). Note: We will interweave content from prior sessions in the following ones, so if you fall behind two or more days in a row, you will be significantly challenged to keep up (there are subtleties in the content that is really key to understanding the content and passing the course - you may feel like you're following along the discussion, but there will likely be important things you're missing). [[/public:T-719-NXAI:nxai-25:readings?#readings_readme|Guidelines for how to read in this course (seriously! - do not skip).]] \\ \\ \\ ===== INTELLIGENCE: THE PHENOMENON [3,5] ===== ====Key Questions==== //What is intelligence? \\ How do experts talk about it? \\ What has been uncovered? \\ What uniquely separates the phenomenon of intelligence from other similar phenomena in the world?// * [[https://proceedings.mlr.press/v192/thorisson22b/thorisson22b.pdf | The Future of AI Research: Ten Defeasible 'Axioms of Intelligence']] by K.R.Thórisson and H. Minsky * [[https://alumni.media.mit.edu/~kris/ftp/DCAULKR-JAGI-2020.pdf | Discretionarily Constrained Adaptation Under Insufficient Knowledge & Resources]] by K.R.Thórisson * [[https://pdfs.semanticscholar.org/4688/e564f16662838938a2d729685baab068751b.pdf?_ga=2.196493103.2038245345.1566469635-1488191987.1566469635|Cognitive Architecture Requirements for Achieving AGI]] by J.E. Laird et al. * [[https://sciendo.com/article/10.2478/jagi-2019-0002|On Defining Artificial Intelligence]] by P. Wang * [[http://cadia.ru.is/wiki/public:t720-atai-2012:what_is_agi|What is GMI?]] by K. R. Thórisson. * [[https://pdfs.semanticscholar.org/4688/e564f16662838938a2d729685baab068751b.pdf?_ga=2.196493103.2038245345.1566469635-1488191987.1566469635|Cognitive Architecture Requirements for Achieving AGI]] by J.E. Laird et al. * [[http://cis-linux1.temple.edu/~pwang/Publication/AGI_Aspects.pdf|Introduction: Aspects of Artificial General Intelligence]] by P. Wang & B. Goerzel (first 3 sections) * [[https://ojs.aaai.org//index.php/aimagazine/article/view/2322|Mapping the Landscape of Human-Level Artificial General Intelligence]] by Adams et al. * [[http://www.iiim.is/2010/05/questions-about-artificial-intelligence/| Four Basic Questions about Artificial Intelligence]] by P. Wang. * [[http://cis-linux1.temple.edu/~pwang/Publication/AI_Misconceptions.pdf|Three Fundamental Misconceptions of Artificial Intelligence]] by P. Wang. * [[https://en.wikipedia.org/wiki/Artificial_general_intelligence| Artificial General Intelligence]] on Wikipedia. * [[https://aeon.co/essays/how-close-are-we-to-creating-artificial-intelligence| The very laws of physics imply that artificial intelligence must be possible. What's holding us up?]] by D. Deutch. * [[https://www.youtube.com/watch?v=lcUk1cYWY9I | Can artificial intelligence become sentient, or smarter than we are - and then what?]] Techtopia @ Deutsche Welle \\ \\ ===== EMPIRICAL REASONING [3,5] ===== ====Key Questions==== //What is reasoning for? \\ What kinds of processes does reasoning consist of? \\ How can the sub-processes of reasoning be coordinated at runtime? // * [[https://cis-linux1.temple.edu/~pwang/Publication/learning.pdf|The Logic of Learning]] by P. Wang * [[https://www.iiim.is/wp/wp-content/uploads/2011/05/wang-agisp-2011.pdf|Behavioral Self-Programming by Reasoning]] by P. Wang * [[https://philosophynow.org/issues/106/Critical_Reasoning|Critical Reasoning]] by M. Talbot * [[http://ftp.cs.ucla.edu/pub/stat_ser/r284-reprint.pdf|Bayesianism and Causality, or, Why I Am Only A Half-Bayesian]] by J. Pearl * [[http://alumni.media.mit.edu/~kris/ftp/Helgason%20et%20al-AGI2013.pdf| Predictive Heuristics for Decision-Making in Real-World Environments]] by H.P.Helgason et al. \\ \\ ===== CUMULATIVE LEARNING [4,6] ===== ====Key Questions==== //What is learning? \\ Do different //kinds// of learning exist? \\ What are the component processes of learning? \\ How can these processes be unified in a single coherent system? \\ Is "machine learning" comparable to human (kinds of) learning?// * [[http://cadia.ru.is/wiki/public:t720-atai-2012:what_is_agi|What is GMI?]] by K. R. Thórisson. * [[https://www.academia.edu/40043621/Cumulative_Learning|Cumulative Learning]] by K.R. Thórisson et al. * [[http://alumni.media.mit.edu/~kris/ftp/AGI18__Cumulative_Learning_With_Causal_Relational_Models.pdf|Cumulative Learning With Causal Relational Models]] by K.R.Thórisson & A.Talbot * [[http://alumni.media.mit.edu/~kris/ftp/Seed-Programmed-General-Learning-Thorisson-PMLR-2020.pdf|Seed-Programmed Autonomous General Learning]] (sections 1 and 2) by K.R. Thórisson * [[https://www.aaai.org/ojs/index.php/aimagazine/article/view/498/434|The Emergence of Artificial Intelligence: Learning to Learn]] by P. Bock * [[https://proceedings.mlr.press/v159/eberding22a/eberding22a.pdf|Comparison of Machine Learners on an ABA Experiment Format of the Cart-Pole Task]] by Eberding et al. * [[https://www.cs.ubc.ca/~murphyk/Bayes/pomdp.html|A Brief Intro to Reinforcement Learning]] by Kevin Murphy * //Alternative to Murphy:// [[http://www.gatsby.ucl.ac.uk/~dayan/papers/dw01.pdf|Reinforcement Learning in the Encyclopedia of Cognitive Science]] by P. Dayan and C. Watkins * [[https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf|Reinforcement Learning: An Introduction]] by Rich Sutton and Andrew Barto (1998), //First chapter//. This is **"the"** introductory text on RL. \\ \\ ===== AUTONOMY & CONTROL [4,6] ===== ====Key Questions==== //How is autonomy defined? \\ What are the levels of autonomy? \\ What are the minimum requirements for different autonomy levels? \\ How can autonomy be achieved in an artificial system? \\ Is learning necessary for autonomy? // * [[/public:T-719-NXAI:nxai-25:creating_a_child_machine_-_raj_reddy.pdf| Creating a Child Machine: Reflections on Turing’s Proposal]] by R. Reddy * [[http://alumni.media.mit.edu/~kris/ftp/AutonomyCogArchReview-ThorissonHelgason-JAGI-2012.pdf| Cognitive Architectures & Autonomy: A Comparative Review]] by K.R. Thórisson & H.P. Helgason. * [[http://www.aslab.org/~sanz/old/docs/ISIC-2000.pdf|Fridges, Elephants, and the Meaning of Autonomy and Intelligence]] by R. Sanz et al. * [[http://cogprints.org/5941/1/ASLAB-A-2007-011.pdf| Principles of Integrated Cognitive Control]] by R. Sanz et al. * [[http://alumni.media.mit.edu/~kris/ftp/nivel_thorisson_replicode_AGI13.pdf| Towards a Programming Paradigm for Control Systems With High Levels of Existential Autonomy]] by E. Nivel & K.R.Thórisson. * {{http://cadia.ru.is/wiki/_media/wiki:a_primer_for_conant_and_ashby_s_good-regulator_theorem.pdf| A Primer For Conant & Ashby's “Good-Regulator Theorem”}} by Daniel L. Scholten. * [[http://cleamc11.vub.ac.be/books/Conant_Ashby.pdf| Every Good Regulator of a System Must be a Model of that System]] by Conant & Ashby * [[https://en.wikipedia.org/wiki/Hybrid_system| Hybrid Systems]] on Wikipedia. * [[https://www.researchgate.net/profile/Elena_Messina/publication/228630874_A_framework_for_autonomy_levels_for_unmanned_systems_(ALFUS)/links/0c960517a990b246b1000000.pdf|A Framework for Autonomy Levels for Unmanned Systems (ALFUS)]] by Huang et al. * [[http://www.aaai.org/Papers/Symposia/Fall/2007/FS-07-01/FS07-01-025.pdf | Self-awareness in Real-Time Cognitive Control Architectures]] by R. Sanz et al. * [[http://pespmc1.vub.ac.be/Papers/Cybernetics-EPST.pdf|Cybernetics and Second-Order Cybernetics]] by Heylighen & Joslyn. * [[http://journals.isss.org/index.php/proceedings53rd/article/viewFile/1249/410| Robert Rosen’s Anticipatory Systems Theory: The Art and Science of Thinking Ahead]] by J. Rosen * [[http://research.microsoft.com/en-us/um/people/lamport/pubs/time-clocks.pdf| Time, Clocks, and the Ordering of Events in a Distributed System]] by L. Lamport * [[http://tierra.aslab.upm.es/~sanz/old/docs/ISIC-2000.pdf| Fridges, Elephants, and the Meaning of Autonomy and Intelligence]] by R. Sanz et al. * [[https://www.aaai.org/ojs/index.php/aimagazine/article/view/498/434|The Emergence of Artificial Intelligence: Learning to Learn]] by P. Bock \\ ===== SYMBOLS, MODELS, CAUSALITY [4,6] ===== ====Key Questions==== // Are symbols and words the same thing? \\ Is the relation between words and symbols bijective? \\ Can anything be a model of anything? \\ How are symbols related to models? \\ How are models of causal relations made? // * [[http://cogprints.org/7150/1/10.1.1.83.5248.pdf|Minds, Brains & Programs]] by John Searle * [[http://consc.net/papers/subsymbolic.pdf|Subsymbolic Computation and the Chinese Room]] by D. Chalmers * [[https://www.academia.edu/11263800/The_Cybernetic_Cut_Progressing_from_Description_to_Prescription_in_Systems_Theory|The 'Cybernetic Cut': Progressing from Description to Prescription in Systems Theory]] by D. L. Abel * * [[http://arxiv.org/pdf/cs/0002009.pdf| Syntactic Autonomy - Or Why There is no Autonomy Without Symbols and how Self-Organizing Systems Might Evolve Them]] by L. M. Rocha \\ \\ ===== MEANING & UNDERSTANDING [3,5] ===== ====Key Questions==== //Where does meaning come from? \\ Who and what is meaning for? \\ Are there different kinds of meaning? \\ How does reasoning fit into the concept of meaning? \\ Is meaning necessary for understanding? // * [[http://alumni.media.mit.edu/~kris/ftp/AGI16_understanding.pdf|About Understanding]] by K. R. Thorisson et al. * [[https://proceedings.mlr.press/v159/thorisson22b/thorisson22b.pdf| The ‘Explanation Hypothesis’ in General Self-Supervised Learning]] by K.R. Thórisson * [[http://ai.stanford.edu/~nilsson/OnlinePubs-Nils/PublishedPapers/pssh.pdf| The Physical Symbol System Hypothesis: Status & Prospects]] by N. J. Nilsson. * [[https://www.researchgate.net/publication/359625587_Understanding_is_a_process| Understanding is a Process]] by Blaha et al. * [[http://alumni.media.mit.edu/~kris/ftp/AGI17_Understanding&CommonSense.pdf| Understanding & Common Sense: Two Sides of the Same Coin?]] by K. R. Thórisson & D. Kremelberg * [[https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwj1gdit6qDsAhXJ5-AKHZU3B8YQFjAAegQIARAC&url=http%3A%2F%2Fwww.academia.edu%2F863857%2FCell_psychology_an_evolutionary_approach_to_the_symbol-matter_problem%3Fauto%3Ddownload&usg=AOvVaw0RFBI0LxmOtGrKvE8u6Adz| Cell psychology: An evolutionary approach to the symbol-matter problem]] by H. Pattee * [[http://alumni.media.mit.edu/~kris/ftp/IJCAI17-EGPAI-EvaluatingUnderstanding.pdf| Evaluating Understanding]] by J.Bieger & K.R.Thórisson * [[http://alumni.media.mit.edu/~kris/ftp/AGI17-UUW-DoMachinesUnderstand.pdf| Do Machines Understand? A Short Review of Understanding & Common Sense in Artificial Intelligence]] by K.R. Thórisson & D. Kremelberg \\ \\ ===== COGNITIVE ARCHITECTURE [5,7] ===== ====Key Questions==== // What role does a cognitive architecture play in intelligence? \\ How is cognitive architecture different from software architecture? \\ How does reasoning, goals, understanding and meaning come into a cognitive architecture? \\ How does reasoning fit into the concept of meaning? \\ Is meaning necessary for understanding? // * [[http://www.cs.bham.ac.uk/research/projects/cogaff/Sloman.kd.pdf | Architectural Requirements for Human-like Agents Both Natural and Artificial]] by A. Sloman * [[http://alumni.media.mit.edu/~kris/ftp/Thorisson_chapt9_TFofAGI_Wang_Goertzel_2012.pdf| A New Constructivist AI: From Manual Construction to Self-Constructive Systems]] by K.R. Thórisson * {{:public:archmismatch-icse17.pdf| Architectural Mismatch or Why it’s hard to build systems out of existing parts}} by Garlan, D., R. Allen and J. Ockerbloom. * Also available [[http://www.cs.cmu.edu/afs/cs/project/able/ftp/archmismatch-icse17/archmismatch-icse17.pdf| here]] * {{:public:intro_to_software_arch.pdf| Introduction to Software Architecture}} by Garlan & Shaw. * [[http://alumni.media.mit.edu/~kris/ftp/AGI16_growing_recursive_self-improvers.pdf| Growing Recursive Self-Improvers]] by B. Steunebrink et al. * [[http://alumni.media.mit.edu/~kris/ftp/BoundedSeedAGI_agi14.pdf| Bounded Seed-AGI]] by E. Nivel et al. * [[http://alumni.media.mit.edu/~kris/ftp/agi-09-self-programming-Nivel-Thorisson.pdf| Self-Programming: Operationalizing Autonomy]] by E. Nivel & K.R. Thórisson. * [[http://alumni.media.mit.edu/~kris/ftp/JAGI-Special-Self-Progr-Editorial-ThorissonEtAl-09.pdf| Approaches & Assumptions of Self-Programming in Achieving Artificial General Intelligence]] by K.R. Thórisson et al. \\ \\ ===== A(G)I Theories & Methodologies ===== * {{/public:t-720-atai:peiwang_2019_ondefiningai_jagi.pdf|On Defining Artificial Intelligence, Journal of Artificial General Intelligence 10(2):1-37}} by P. Wang. * [[http://act-r.psy.cmu.edu/wordpress/wp-content/uploads/2012/12/526FSQUERY.pdf| An integrated theory of mind]] by Anderson et al. * [[http://act-r.psy.cmu.edu/wordpress/wp-content/uploads/2012/12/526FSQUERY.pdf|(ACT-R) An Integrated Theory of the Mind]] by Anderson, J. R.; Bothell, D.; Byrne, M.D.; Douglass, S.; Lebiere, C. & Qin, Y. * [[http://www.iiim.is/2010/05/agi-cognitive-synergy-goertzel/| Does the Future of AGI Lie in Cognitive Synergy?]] by B. Goertzel * [[http://arxiv.org/abs/0712.3329|Universal Intelligence: A Definition of Machine Intelligence]] by S. Legg and M. Hutter. * [[https://www.kurzweilai.net/essentials-of-general-intelligence-the-direct-path-to-agi|Essentials of General Intelligence: The Path Towards AGI]] by P. Voss. * [[/public:t_720_atai:atai-20:ConstructivistAI|Constructivist AI Methodology]] by K.R. Thórisson * {{public:i-700-abms-07-1:cantherebeascienceofcomplexity-simon-iccsproc.pdf| Can there be a science of complex systems?}} by H. A. Simon * [[http://pespmc1.vub.ac.be/Papers/Cybernetics-EPST.pdf|Cybernetics and Second-Order Cybernetics]] by Heylighen & Joslyn \\ \\ \\ \\ \\ =====IMPLEMENTED COGNITIVE ARCHITECTURES===== \\ ==== SUBSUMPTION ARCHTIECTURE ==== //The Subsumption Architecture is definitely GOFAI-style architecture: With baked-in hand-coded goals and control structures, these systems are notoriously difficult to build for autonomous adaptation of any kind. But they are fun to build, robust and easy to debug. // * [[https://en.wikipedia.org/wiki/Subsumption_architecture| Subsumption Architecture]] on Wikipedia. * //(supplementary, optional)// [[http://people.csail.mit.edu/brooks/papers/how-to-build.pdf| How to Build Complete Creatures Rather than Isolated Cognitive Simulators]] by Rodney Brooks. * //(supplementary, optional)// [[ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-864.pdf| A Robust Layered Control System for a Mobile Robot]] by R. Brooks. * //(supplementary, optional)// [[http://www.artificialhumancompanions.com/robot-mind-robot-body-whatever-happened-subsumption-architecture/|Whatever happened to the subsumption architecture?]] by Simon Birrell. ==== NARS ==== // This is the definitive reasoning architecture, under development since the 1990s.// * [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.134.4298&rep=rep1&type=pdf|From NARS to a thinking machine]] by P. Wang * [[public:t720-atai-2012:nars|What is NARS]] by K. R. Thórisson * [[https://cis.temple.edu/~pwang/NARS-Intro.html| Introduction to NARS]] by P. Wang * [[http://www.cis.temple.edu/~wangp/Publication/unifiedAI.pdf| NARS intro paper]] by P. Wang * [[http://www.cis.temple.edu/~wangp/demos.html| NARS demos]] by P. Wang ==== AERA ==== // The Reykjavik University architecture that has been shown to learn very complex tasks by observation.// * [[http://alumni.media.mit.edu/~kris/ftp/PeeweeGranularity-Thorisson-Nivel-09.pdf|Achieving Artificial General Intelligence Through Peewee Granularity]] by Thórisson, K. R. & Nivel, E. * [[http://alumni.media.mit.edu/~kris/ftp/agi-09-self-programming-Nivel-Thorisson.pdf|Self-Programming: Operationalizing Autonomy]] by Nivel, E. & K. R. Thórisson * [[http://alumni.media.mit.edu/~kris/ftp/BoundedSeedAGI_agi14.pdf|Bounded Seed-AGI]] by E. Nivel et al. * [[http://alumni.media.mit.edu/%7Ekris/ftp/AAoNL-wHeadr.pdf|Autonomous Acquisition of Natural Communication]] by K. R. Thórisson et al. * [[http://alumni.media.mit.edu/~kris/ftp/AnytimeBoundedRationalityagi15_nivelEtAl.pdf|Anytime Bounded Rationality]] by E. Nivel et al. * [[http://arxiv.org/pdf/1312.6764v1.pdf|Bounded Recursive Self-Improvement]] by E. Nivel et al. ==== Drescher's Constructivist Schema System ==== // One of the earliest examples of implemented self-guided learning systems. // * [[http://cadia.ru.is/wiki/_media/public:t-720-atai:dvhw-made-up-minds-drescher-1991.pdf|Made-Up Minds: A Constructivist Approach to Artificial Intelligence]] by G. Drescher * Read chapters 1 & 2 for understanding Drescher's (and Piaget's) psychological motivation; Ch. 3 & 4 for an overview of the computational framework. ==== Sigma [ 0,1 ] ==== // Sigma doesn't learn, but it's a great tool for learning about generality and autonomy. // * [[http://cs.usc.edu/~rosenblo/Pubs/Sigma%20AISBQ%20D.pdf|The Sigma Cognitive Architecture and System]] by P. S. Rosenbloom ==== OpenCOG ==== // Originally based on NARS (see above), the latest incarnation of the OpenCOG is called . // * [[http://goertzel.org/dynapsyc/2009/OpenCogPrime.pdf|OPENCOG PRIME: A COGNITIVE SYNERGY BASED ARCHITECTURE FOR ARTIFICIAL GENERAL INTELLIGENCE]] by Ben Goertzel * [[http://wiki.opencog.org/w/Getting_Started|Getting Started with Open Cog]] by Ben Goertzel ==== Other Such Systems ==== * Franklin, S. (2007). **(LIDA) A Foundational Architecture for Artificial General Intelligence**. Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms. IOS Press, Amsterdam, The Netherlands, The Netherlands, pp. 36-54. [[http://goertzel.org/agiri06/%5B4%5D%20StanFranklin.pdf | PDF]] * Anderson, J.R. & Schunn, C.D. (2000). **Implications of the ACT-R learning theory: No magic bullets**. Advances in instructional psychology. 5:1-34. Lawrence Erlbaum [[http://www.lrdc.pitt.edu/schunn/research/papers/nomagicbullets.pdf || PDF]] * Laird, J.E.; Newell, A. & Rosenbloom, P.S. (1987). **SOAR: An architecture for general intelligence**. Artificial Intelligence, Volume 33, Issue 1, Pages 1-64 [[http://shelf2.library.cmu.edu/Tech/16128161.pdf | PDF]] * Snaider, J; McCall, R. & Franklin, S. (2011). **The LIDA framework as a general tool for AGI**. Artificial General Intelligence, Lecture Notes in Computer Science. 2011. Volume 6830/2011. pp. 133-142 [[http://ccrg.cs.memphis.edu/assets/papers/2011/The%20LIDA%20Framework%20as%20a%20General%20Tool%20for%20AGI%20final.pdf | PDF]] * J. Schmidhuber (2016). [[https://mainatnips.github.io/mainatnips.github.io/slides/schmidhuber-rsi2016white.pdf|Learning how to Learn Learning Algorithms: Recursive Self-Improvement]]. - slides \\ \\ \\ \\ \\ --------------------------------------------------------- ==== Resource Management: Attention, Self-Control, Integrated Cognitive Control ==== * [[http://alumni.media.mit.edu/~kris/ftp/Helgason-AGI2012-AwardEdition-Final.pdf| On Attention Mechanisms for AGI Architectures: A Design Proposal]] by Helgason et al.; accompanying video can be found [[https://www.youtube.com/watch?v=-EKO5u86DYM|here]]. * [[http://web.mit.edu/torralba/www/josa.pdf| A model of attention that takes global scene factors into account]] by Torralba. * [[http://alumni.media.mit.edu/~kris/ftp/HelgasonEtAl-2014-Attention-IJCSAI10339-20140314-163624-3675-40677.pdf|Towards a General Attention Mechanism for Embedded Intelligent Systems]] by H. P. Helgason et al. * [[http://alumni.media.mit.edu/~kris/ftp/Helgason-Thorisson-AttentionForAI.pdf| Attention Capabilities for AI Systems]] by H. P. Helgason & K. R. Thórisson. \\ \\ \\ \\ \\ \\ =====FOUNDATIONAL TOPICS===== \\ === Self-Organization & Emergence === * [[http://web.stanford.edu/dept/HPS/WritingScience/RochaSelfOrganisation.pdf|Selected Self-Organization and the Semiotics of Evolutionary Systems]] by L. M. Rocha * [[http://people.reed.edu/~mab/publications/papers/weak-emergence.pdf|Weak Emergence]] by M. A. Bedau * [[http://consc.net/papers/emergence.pdf|Strong and Weak Emergence]] by D. Chalmers * [[http://cadia.ru.is/wiki/_media/emergence-causation:eolss-scienceof-self-organiz.pdf|THE SCIENCE OF SELF-ORGANIZATION AND ADAPTIVITY]] by Heylighen === (Phenomenal) Consciousness === * [[http://organizations.utep.edu/portals/1475/nagel_bat.pdf|What's it Like to be a Bat?]] by T. Nagel * [[http://consc.net/papers/nature.pdf|Consciousness and its Place in Nature]] by D. J. Chalmers * [[https://www.youtube.com/watch?v=1d5RetvkkuQ|The Future of Consciousness]] - TEDx lecture by R. Hameroff * [[https://www.youtube.com/watch?v=GzCvlFRISIM|Consciousness is a Mathematical Pattern]] - TEDx lecture by M. Tegmark * [[https://www.youtube.com/watch?v=j_OPQgPIdKg|Consciousness and the Brain]] - TEDx lecture by J. Searle \\ \\ \\ \\ =====Societal Impact & Ethics ===== \\ * [[http://mobile.abc.net.au/news/2018-09-18/china-social-credit-a-model-citizen-in-a-digital-dictatorship/10200278?pfmredir=sm|Leave no dark corner]] by Matthew Carney. * [[https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf|THE FUTURE OF EMPLOYMENT: HOW SUSCEPTIBLE ARE JOBS TO COMPUTERISATION?]] by Frey and Osborne \\ \\ ===== Additional Readings & Study Material ====== === Reinforcement Learning === * [[https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html|Reinforcement Learning: An Introduction]] by Rich Sutton and Andrew Barto (1998) is //the// introductory text on RL. * [[http://www.vetta.org/documents/Machine_Super_Intelligence.pdf|Machine Super Intelligence]] is Shane Legg's 2008 PhD thesis. While it is not on reinforcement learning, it does connect the concepts of RL to AI, what he calls "universal AI". * [[https://www.udacity.com/course/reinforcement-learning--ud600|Reinforcement Learning]] is a full Udacity course on RL from Georgia Tech. * [[https://www.youtube.com/playlist?list=PLf6J8du4ey8CL0pUxbLQpdbbtDcc2M1bI|A Short Course on Reinforcement Learning]] by S. Singh at MLSS'11 introduces RL and discusses some important shortcomings and proposed first steps to solving them. * [[http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html|Advanced Topics: RL]] by D. Silver is a more in-depth, modern RL course from one of the people who worked on Google DeepMind's Atari playing system that received a lot of (media) attention. * [[http://videolectures.net/nips09_littman_mbrl/|Model-Based Reinforcement Learning]] is a tutorial given by Michael Littman at NeurIPS'09 about model-based RL, which is a lot less common than model-free RL, but not less interesting. * [[http://link.springer.com/chapter/10.1007/978-3-642-39521-5_13#page-1|Resource-Bounded Machines are Motivated to be Effective, Efficient & Curious]] by B. Steunebrink === Deep Learning === * [[https://www.codementor.io/james_aka_yale/a-gentle-introduction-to-neural-networks-for-machine-learning-hkijvz7lp|A gentle introduction to neural networks]] by J. Le - gives a good overview of the different approaches * [[https://garymarcus.substack.com/p/form-function-and-the-giant-gulf|Form, function, and the giant gulf between drawing a picture and understanding the world]] by G. Marcus === Other === * [[https://www.aaai.org/ocs/index.php/FSS/FSS15/paper/view/11702/11476| Kognit: Intelligent Cognitive Enhancement Technology by Cognitive Models and Mixed Reality for Dementia Patients]] by D. Sonntag and [[https://vimeo.com/132704158|accompanying video]] * [[http://bicasociety.org/cogarch/architectures.htm| Comparison table of cognitive architectures]], courtesy of BICA Society / A. Samsonovich * [[http://qz.com/627989/why-are-so-many-smart-people-such-idiots-about-philosophy/|Why are so many smart people such idiots about philosophy?]] by O. Goldhill * [[http://alumni.media.mit.edu/~kris/ftp/CognitiveMapIEEE-09.pdf|Cognitive Map Architecture: Facilitation of Human-Robot Interaction in Humanoid Robots]] by V. Ng-Thow-Hing et al. (2009). * [[http://www.aaai.org/Papers/Symposia/Fall/2004/FS-04-01/FS04-01-014.pdf | Toward a unified artificial intelligence]] by Wang, P. * [[https://www.youtube.com/watch?v=8nHVUFqI0zk|Judea Pearl lecture on Causation]] on YouTube * [[https://www.willamette.edu/~gorr/classes/ids101/links/nutshell.pdf|Creativity in a Nutshell]] by M. Boden. * [[http://www.ppi-int.com/files/role-of-cse-in-se.pdf| The Role of Cognitive Systems Engineering in the Systems Engineering Design Process]] by Militello, L.G., Dominguez, C.O., Lintern, G. & Klein, G. (2010). In Systems Engineering, Vol 13(3), pp. 261-273. * [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.158.4126&rep=rep1&type=pdf| A survey on transfer learning]] by Pan, S. J. & Yang, Q. (2011). IEEE Transactions on Knowledge and Data Engineering, 22(10), pp. 1345–1359. * [[http://tierra.aslab.upm.es/documents/controlled/ASLAB-A-2009-016.pdf | Systems, models and self-awareness: Towards architectural models of consciousness]] by Sanz, R., Hernandez, C., Gomez, J., Bermejo-Alonso, J., Rodriguez, M., Hernando, A. & Sanchez, G. (2009). International Journal Of Machine Consciousness, 1(2), pp.255–279. * [[http://wiki.humanobs.org/_media/public:events:agi-summerschool-2012:silverpaper_reqforml3.pdf| Requirements for Machine Lifelong Learning]] by Silver, D.L. & Poirier, R. (2007). IWINAC, LNCS (4527), pp.313-319. * [[http://www.lehigh.edu/~mhb0/physicalemergence.pdf| Physicalism, Emergence and Downward Causation]] by Campbell and Bickhard * [[http://agi-school.org/2009/dr-joscha-bach-man-as-machine|Man as Machine]] - J. Bach's AGI intro lecture at AGI Summer School 2009 * {{:public:t-720-atai:atai-16:silver2016.pdf|Mastering the game of Go with deep neural networks and tree search}} by Silver, Huang et al. (DeepMind) * [[http://www.scottaaronson.com/papers/philos.pdf|Why Philosophers Should Care About Computational Complexity]] by S. Aaronsson * [[http://www-verimag.imag.fr/~sifakis/RecentPublications/2011/AVisionforCS.pdf|A Vision for Computer Science - the System Perspective]] by J. Sifakis \\ ===== Other Sources ====== * [[http://www.degruyter.com/view/j/jagi| Journal of Artificial General Intelligence]] * [[http://aima.eecs.berkeley.edu/~russell/intro.html|Introduction to Artificial Intelligence - A Modern Approach]] by S. Russell & P. Norvig * [[http://philosophy.wisc.edu/forster/520/Chapter%201.pdf|Chapter 1: An Introduction to Philosophy of Science]] by Malcolm Forster ====Reinforcement Learning==== * [[http://www.ualberta.ca/~szepesva/RLBook.html|Algorithms for Reinforcement Learning]] by Csaba Szepesvári (2010) is a much more recent, shorter book that discusses the strengths and weaknesses of various RL algorithms. See also: [[http://incompleteideas.net/sutton/RL-FAQ.html|Rich Sutton's FAQ]]. * [[https://en.wikipedia.org/wiki/Deep_learning|Deep learning on Wikipedia]] (Sections: Intro, Overview, and Neural Networks). \\ \\ \\ \\ =====Readings README===== //Note: DO NOT SKIP READING THE BELOW TEXT// **As you read papers from each of the following categories I want you ask yourself a few questions:** * For each paper in each category X, ask yourself: * What is X? * How does the human mind do X? * Do current computers do X? * ...and ... * Do we need (to replicate or capture) what the human mind does to achieve X to create a machine that rivals the ability of humans to do X? If you can answer them satisfactorily when you're done reading you're good! Even if you can't you'll be fine if you: Write down the discrepancies and //bring them to class in the form of questions//. There is no such thing as a 'stupid question' when you're learning something new! \\ \\ \\ \\ //2025(c)K.R.Thórisson//