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

1. 2015-07 02015-07
ARAKAWA, Naoya, Ph.D
Human-Level AI &
Phenomenology
2015-07-11

2. 2015-07 1
Today’s Topic
● Creating Human-Like AI
○ Background, Issues & Approaches
○ Its relation to Embodiment &
Phenomenology
○ My recent activities

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 2015-07 16
Robotics for Social Intelligence
● Communicatin study with robots
● Communication requiring the body
● Mimetics
● Joint attention
● Empathy

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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