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
The Foundations of Artificial Intelligence, The History of
Artificial Intelligence, and the State of the Art. Intelligent Agents: Introduction, How Agents
should Act, Structure of Intelligent Agents, Environments. Solving Problems by Searching:
problem-solving Agents, Formulating problems, Example problems, and searching for Solutions,
Search Strategies, Avoiding Repeated States, and Constraint Satisfaction Search. Informed
Search Methods: Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative
Improvement Algorithms.
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence
Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
Architecture as language. Who creates architectures? Not the Heroes, but you and I. Its a collective enterprise. Architects selecting and combining each others works.
The Foundations of Artificial Intelligence, The History of
Artificial Intelligence, and the State of the Art. Intelligent Agents: Introduction, How Agents
should Act, Structure of Intelligent Agents, Environments. Solving Problems by Searching:
problem-solving Agents, Formulating problems, Example problems, and searching for Solutions,
Search Strategies, Avoiding Repeated States, and Constraint Satisfaction Search. Informed
Search Methods: Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative
Improvement Algorithms.
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence
Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
Architecture as language. Who creates architectures? Not the Heroes, but you and I. Its a collective enterprise. Architects selecting and combining each others works.
barch_1st sem_anna univ. affl._msajaa_INTRODUCTION TO ARCHITECTURE_ELEMENTS OF ARCHITECTURE_ELEMENTS OF ARCHITECTURE – FORM_ELEMENTS OF ARCHITECTURE – SPACE_PRINCIPLES OF ARCHITECTURE
Some of the theories are now certainly outdated and have little interest to a modern builder, but some contain still valid information about important goals of building, notably on the questions of functionality, construction, economy and ecology. While theory of design is intended to help design, it does not necessarily precede design. On the contrary, the first building where a new architectural style is exposed, is usually created intuitively, without the help of any theory, just by the skill of a brilliant architect. The design theory comes a little later, and even less brilliant architects can then base their work on it.
Theories can be seen as building-specific branches of the general goal-specific theories which pertain to all types of products and are listed in Paradigms Of Design Theory. Thematic or "analytic" theories are treatises which aim at the fulfilment of one principal goal of architecture. Theories of architectural synthesis are examples of theories which aim at fulfilling simultaneously several goals, usually all the goals that are known.
In present day, the design theory of architecture includes all that is presented in the handbooks of architects: legislation, norms and standards of building. All of them are intended to aid the work of the architect and improve its product -- the quality of buildings technology and production in general: proven theory helps designers to do their work better and more effectively. It occasionally even helps to do things that were believed to be impossible earlier on. As an old saying goes, there is nothing more practical than a good theory. The aesthetization of utilitarian ideas is the primacy of architecture as a vessel of life, accommodating the needs of human beings .
Presentation shared by author at the 2019 EDEN Annual Conference "Connecting through Educational Technology" held on 16-19 June, 2019 in Bruges, Belgium.
Find out more on #eden19 here: http://www.eden-online.org/2019_bruges/
Introduction of Artificial Intelligence related to BIT course.pdfHome
On this presentation slide we discussed about Artificial intelligence. Here is the content available In artificial intelligence.
What is Intelligence/Artificial Intelligence (AI)
• History of AI
• AI Perspectives (Defining AI)
• Turing Test
• Foundations of AI,
• Scope of Symbolic AI
• Applications of AI
We used to think that everyone could teach. That if someone is a good professional and expert in her field, she will be a good instructor, teacher, or mentor. And that she will easily transfer her knowledge to others. This view is far from reality and harms. Every time there are new (and not that new) teachers and educators who get into this trap. Teaching is a profession that should be mastered like any other one. At this talk, we will try to answer the following questions. What are the main challenges while teaching adults? How to take into account our cognitive abilities and brain’s behavior while designing a learning experience for your students? How and when does it better to use the ed-tech tools to leverage the study process?
This talk was delivered at Engageducate conference from Softserve University
RoboBrain: A software architecture for mapping the human brainIlias Trochidis
RoboBrain proposes a biologically inspired ‘blue-print’ software architecture for humanoid robots that maps the main functions of the human brain. Given the extreme complexity of designing such an architecture, our intention is to provide a basic robots’ IT command and control foundation with minimal required components to address humanoid robots’ functionality needs. Our approach integrates the ‘must have’ components that emulate the human brain’s corresponding functionality as well as addresses the command, control, perception and task execution requirements of a humanoid robot.
Modern machine learning is immensely powerful but also has very significant limitations that don't always get the attention they deserve. In this talk, I tried to contrast machine learning against AI and the original goals of that field, give some context and discuss a potential path forward.
2015年7月11日に
エンボディード・アプローチ研究会
http://www.geocities.jp/body_of_knowledge/
「心の科学の基礎論」研究会
http://www.isc.meiji.ac.jp/~ishikawa/kokoro.html
の共同研究会で発表した内容です。
Ref. Phenomenology of Artefacts
http://rondelionai.blogspot.jp/2014/02/phenomenology-of-artefacts.html
The English version: http://www.slideshare.net/naoyaarakawa39/humanlevel-ai-phenomenology
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
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