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Human-Level AI & Phenomenology

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The English translation of the content presented at the joint meeting of
Research Meeting for Embodied Approach
http://www.geocities.jp/body_of_knowledge/
and
Meta-theoretical Studies of Mind Science
http://www.isc.meiji.ac.jp/~ishikawa/kokoro.html
on July 11th, 2015.
Ref. Phenomenology of Artefacts
http://rondelionai.blogspot.jp/2014/02/phenomenology-of-artefacts.html
The Japanese (original) version: https://www.slideshare.net/naoyaarakawa39/201507-50448060

Published in: Science
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Human-Level AI & Phenomenology

  1. 1. 2015-07 02015-07 ARAKAWA, Naoya, Ph.D Human-Level AI & Phenomenology 2015-07-11
  2. 2. 2015-07 1 Today’s Topic ● Creating Human-Like AI ○ Background, Issues & Approaches ○ Its relation to Embodiment & Phenomenology ○ My recent activities
  3. 3. 2015-07 2 Abridged CV • Education – Undergraduate:Brain & Neural Nets – Graduate (M.E.):Systems Science – Ph.D:Philosophy of Language “The Naturalization of Reference” • Work: Natural Language Processing – Machine Translation – Dialog systems – Semantic analysis, Ontology compiling • Recent activities: Artificial General Intelligence
  4. 4. 2015-07 3 Table of Contents 1.Background for Human-Level AI ● AI with Human cognitive functions ● Recent ‘AI boom’ ● Two contrapositions 2. Issues to be solved 3. How to create Human-Level AI 4. My recent activities
  5. 5. 2015-07 4 Human-Like AI ● An aim/ambition of the AI discipline ○ 「Agalmatophilia」? ○ AI as「Cognitive Science」 ● Constructive (Make & Test) Understanding of Human-beings ○ Build to understand ○ Difficulty in fully analytic understanding
  6. 6. 2015-07 5 Recent “AI Boom” ● Media Coverage ○ AI books for general public ○ TV programs on AI ○ New research centers ● Technological Background ○ Computing Power ○ Availability of “Big Data” ○ Some notable results: Chess, Jeopardy!, Self-driving cars, ... ○ Advances in Machine Learning Deep Learning! ⇒
  7. 7. 2015-07 6 Advances in Machine Learning ● The Neural Net Strikes Back! ● Deep Learning ○ Multi-Layered Neural Networks ○ Notable results in pattern recognition ○ Automatic concept formation Google Brain (Cat), Google Dreams (Inceptionism) ● Recurrent Neural Network (RNN) ○ Learning time-series ○ Captioning images with deep learning (Stanford U.) ● Reinforcement Learning ○ Learning action sequences based on rewards ○ Deep Q Network: playing Atari games
  8. 8. 2015-07 7 AGI vs. Narrow AI ● Artificial General Intelligence vs. Narrow AI ○ Artificial General Intelligence ■ ‘General’ in the sense that it can learn various skills ■ Human-Like AI ⊂ AGI ■ Long hoped... but difficult to realize⇒ ○ Narrow AI: to solve specific issues 〜the current main stream ● GOFAI vs. Emergentist AI ○ Good Old-Fashined (Symbolic) AI ■ Criticized by thinkers such as Dreyfus & Lakoff ■ Knowledge acquisition bottleneck ○ Emergentist AI ■ Knowledge is not to be given but to learn ■ Analog (statistic) ※Advances in machine learning⇒AGI sees the light here!?
  9. 9. 2015-07 8 Table of Contents 1.Background for Human-Level AI 2. Issues to be solved ● Knowledge Acquisition=Learning=Epistemology ● Cognitive Functions 2. How to create Human-Level AI 3. My recent activities
  10. 10. 2015-07 9 Issues to be solved Knowledge Acquisition=Learning =Epistemology ● How do we get knowledge? ● How do machines get knowledge? ● More concretely: ○ Acquistion of concepts(←perception & motion) ○ Knowledge acquisition on action (praxis/pragmatics←motion & perception) ○ Language Acquistion ■ Acquistion of Vocabulary (the Symbol Grounding Problem) ■ Acquistion of Grammar
  11. 11. 2015-07 10 Cognitive Functions to be realized ○ Human-Level AI⇔Inventory of Human Cognitive Functions ○ Learning〜Knowledge Acquisition ■ Pattern Recognition (mostly supervised) ■ Conceptual Learning (mostly unsupervised) ● ‘Clustering’ ● ‘Representation Learning’ in Deep Learning ■ Reinforcement Learning:learning action sequences based on rewards ■ Episodic Memory:One-shot Learning ○ Planning & Execution ■ Emergentist AI: trying to get inspiration from the prefrontal cortex? ○ Linguistic Functions ■ Generativity(Syntax) ■ Social aspects(Pragmatics) ■ Grounding(Semantics)
  12. 12. 2015-07 11 Table of Contents 1.Background for Human-LevelAI 2. Issues to be solved 3. How to create Human-Level AI ● Three Pillars ● Make & Test (Constructive) Approach 2. My recent activities
  13. 13. 2015-07 12 How to Create Human-Level AI 1.Three Pillars(IMHO) •Cognitive Architecture: Overall Structural Models Intelligence has ‘structure’ Traditional ones: symbolic You can learn from the brain too. •Machine Learning Mathematical models for learning •Cognitive Robotics (embodiment) Learning developmentally in the environment 2.The Constructive (Make & Test) Approach • Hypotheses⇒robots/simulation to corroborate • Cognitive Robotics • Artificial Brains
  14. 14. 2015-07 13 Cognitive Robotics • Robotics as Cognitive Science • Stance: cognition requires the body. • ‘Constructive’ understanding of cognition Construct to understand! • Genres – Cognitive Developmental Robotics • Developing cognitive abilities like human children – Robotics for Symbol Emergence • Learning language via interaction with the environment – Robotics for Social Intelligence • Communicating robots
  15. 15. 2015-07 14 Cognitive Developmental Robotics • Developing cognitive abilities like human children • Robots learns from interaction with the environment • To complement experiments with human infants (which are difficult for ethical reasons) • Researches in Japan, e.g.: –Asada Lab. @ Osaka U. –Kuniyoshi Lab. @ Tokyo U. –The Constructive Developmental Science @ MEXT • Ref. – Cangelosi, A. et al.: Developmental Robotics -- From Babies to Robots, MIT Press (2015). – Asada M. et al.: "Cognitive developmental robotics: a survey," in IEEE Transactions on Autonomous Mental Development, Vol.1, No.1, pp.12--34 (2009)
  16. 16. 2015-07 15 Robotics for Symbol Emergence • Learning language via interaction with the environment • Human-beings:no grammar, no vocabulary given • ref. Developmental Linguistics – Tomasello, Meltzoff, Spelke, … – Chomskians(the merge theory) – cf. Evolutional Linguistics (animal cognitive functions) • The Symbol Grounding Problem: mapping symbols to things in the world • Machine learning methods – Non-parametiric bayes, Recursive Neural Net… • Getting insights from developmental linguistics • Yet to succeed in language acquistion
  17. 17. 2015-07 16 Robotics for Social Intelligence ● Communicatin study with robots ● Communication requiring the body ● Mimetics ● Joint attention ● Empathy
  18. 18. 2015-07 17 Cognitive Robotics & Embodiment • The interests of cognitive robotics researchers 〜the interests of embodiment researchers • Common terms – Body Image & Body Scheme, etc.
  19. 19. 2015-07 18 Artificial Brains ● Reproducing human cognitive functions by creating something similar to the brain ● Brain Simulation ○ Physiological models ○ Blue Brain Project, Neurogrid Project, etc. ● Brain-Inspired Cognitive Architectures ○ Examples ■ Nengo/SPAUN (C. Eliasmith et al.) ■ Leabra (O’Reilly et al.) ■ The Whole Brain Architecture (to be mentioned later)
  20. 20. 2015-07 19 脳研究の現状 ● Advance in functional brain imaging (e.g., fMRI) ● Cognitive Neuro-Scientists ○ A. Damasio:Somatic Marker Hypothesis(role of emotion) ○ V.S. Ramachandran:presenting cognitive disorders ○ E. Kandel:memory research ○ E. Goldberg:cerebral hemispheres & prefrontal cortex ● Modeling cerebral organs ○ Cerebral cortex & areas(perception, motion, planning, …) the uniform structure of cortex [Mountcastle] ○ Basal ganglia (striatum, etc.: reinforcement learning, WM…) ○ Limbic System (amygdala, etc.: emotion, reward,...) ○ Hypocampus (memory, space representation) ○ Cerebellum (motion control, higher-order cognitive functions) ⇒ To draw an integrated picture soon?
  21. 21. 2015-07 20 The Brain and Cognitive Functions(Figure) Prefrontal Cortex: Planning Motor Area:Motion Sequences Basal Ganglia: Reinforcement Learning Cerebellum:Feed-forward prediction? Hypocampus:Episodic Memory (Place Memory in Rodents) Where Path What Path Amigdalae, etc.: Emotion Language Areas To think of an ‘architecture’ constituting of such functional modules to realize human-level intelligence
  22. 22. 2015-07 21 Table of Contents 1.Background for Human-LevelAI 2. Issues to be solved 3. How to create Human-Level AI 4. My recent activities ● Issue of Semantics ● Overall Objectives ● Phenomenology of Artefacts(Manifesto) ● Phenomenology of Time ● Language Acquistion by Artifacts ● AGI related activities
  23. 23. 2015-07 22 Semantic Issue:doubts from my pre-history • Creating an ontology for natural language • The problem of polysemy (ambiguity) – How many senses? E.g., prepositions – Border-line uses... • How do humans acquire word senses? • Keys in human developmental process • Counsel by Lakoff, the Cognitive Linguists Women, Fire, and Dangerous Things It is impossible to deal with meaning with symbolic logic! ⇒ Radical readdressing is required!
  24. 24. 2015-07 23 Overall Goal:Explaining Cognition ● More precisely:Grounding Semantics ● But semantics requires epistemology. ○ No sense made without knowing the world. ● By-product:AGI/Human-Leval AI ○ But the by-product is the mean in the constructive method. ⇒ Methodological Loop
  25. 25. 2015-07 24 Approach ● Learning from animals ○ Modeling brains, comparative psychology, etc. ● Phenomenological & Developmental ○ Knowledge acquisition from information given to individuals ● Constructive (make & test) ○ Machine Learning ○ Robotics(simulation) ● Language Acquistion ○ Language :an essential component of cognition ○ Explanation with 1〜3 above
  26. 26. 2015-07 25 Phenomenology of Artefact (2014-02) • Husserlean phenomenology〜Grounding Epistemology • Epistemology from the first person view • Robots has the first person view Video:MIT Atlas robot - first person view sensor visualization ⇔ • Robots with kinesthetics • Developmental knowledge acquistion • Information processing with robots – inspectable – methematically verifiable • Time consciousness with machine learning? ⇒ Reconstructing phenomenology with artifacts (robots)?
  27. 27. 2015-07 26 Phenomenology of Time ● Time Consciousness by Husserl: Urimpression, Protention, Retention ● Time-series Learning〜Time-series Prediction ○ RNN (recurrent neural network) ○ Temporal Cerebral Models:HTM, DeSTIN, etc.(cf. akinestopsia @V5) ○ PSI model by Dörner (cognitive psychologist) Bach J.: Principles of Synthetic Intelligence -- PSI: An Architecture of Motivated Cognition, Oxford U. ○ LLoyd, M.: “Time after Time -- Temporality in the dynamic brain,” Being Time: Dynamical Models of Phonomenal Experience, John Benjamins Pub. Co. (2012) ● Time-series Learning & Phenomenology of Time ○ Protention:memory of the future (prediction) ○ Retention:memory of the context (the internal state from the past input) ○ Urimpression⇔ contextualized (differential) present ● cf. Jun Tani, the roboticist ○ RNN ○ Ref. to Husserlean phenomenology of time: longitudinal/transverse intentionality
  28. 28. 2015-07 27 Towards Language Acquistion by Artifacts • Developmental Robotics in the virtual world • Learning from Infants’ language acquistion •Spelke •Concepts of things: certain constraints –cf. Quine: “Gavagai” –Seeing thing as a whole cf. Husserl: looking around objects⇒3D object concept •Tomasello • Understanding reference by others requires understanding intention. •Usage-based grammar learning (anti-generative grammar) •Meltzoff •Infants’ understanding of the intention of others •Modeling own intentional motions first?
  29. 29. 2015-07 28 Towards Language Acquistion by Artifacts (cont.) • Acquistion of Verbs •Verbs are the crux of sentence structure •Acquired after object/nominal concepts •Modeling own intentional motions first (←Meltzoff)? cf. sense of agency Own intention is ‘given’ •Mapping to verbs • ‘Parental’ verb uses •Pragmatic success/failure of own utterances • Acquistion of syntax • Concatenating subsequent structures⇒Merge? • Language acquistion with machine learning
  30. 30. 2015-07 29 AGI-related Activities(ads :-) ❖ Dwango AI Lab. ● Brain/Cognitive Modeling, Language Acquistion, etc. ❖ The Whole Brain Architecture Initiative (NPO) ● Brain-inspired cognitive architecture ● Education, promotion ❖ SIG AGI(@ Japanese AI Society) ● a reading group ● planning to publish a textbook (in Japanese)… ❖ Web site in Japanese ● www.sig-agi.org ● Facebook Group For more information, contact naoya.arakawa@nifty.com

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