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Hiroshi Yamakawa, Yutaka Matsuo,
Koichi Takahashi and Naoya Arakawa
Strategy to build Beneficial
Artificial General Intelligence
inspired by the Brain
Symposium 2: Whole-Brain Architecture 14:00-15:30, Oct. 26th
The 28th Annual Conference of the Japanese Neural Network Society (JNNS2018)
Abstract
Since 2013, we have been advocating the idea of whole brain architecture.
The Whole Brain Architecture Initiative (a specified NPO) promotes
‘to create (engineer) a human-like artificial general intelligence (AGI) by
learning from the architecture of the entire brain, ’ and explores the way
to develop brain-inspired AGI through activities such as hackathons .
In recent years, AI researches are energized by deep learning
technology and neuroscience researches are heading toward elucidation of
high-level cognitive functions. Despite such boosts, obstacles remains on
the way to brain-inspired AGI. Here we present our strategy to construct
brain-inspired AGI. As AGI's impact on society will be enormous, we
should make AI development beneficial to humanity by learning from the
brain.
Symposium 2: Whole-Brain Architecture@JNNS2018
History of WBA
Symposium 2: Whole-Brain Architecture@JNNS2018
2013 20152014 20172016 2018 20302025
Goal:CompletionofWholeBrainArchitecture
Whole Brain Architecture Seminars
WBA Future Leaders
The Whole Brain Architecture Initiative
2ndWBAsymposium
3rdWBAhackathon
3rdWBAsymposium
4thWBAhackathon
1stWBAsymposium
2ndWBAhackathon
1stWBAhackathon
BasicIdeas(Vision)
WBAcorehypothesis
VisitGatsbywithKenji
ICONIPSession
BiCASession
DiscussionstartedbyYutakaMatsuo,YujiIchisugiandHiroshiYamakawa
KyusyuBranch
Branchof
workingpeople
Kanto/Kansai
branch
JNNSsymposium
Whole Brain Architecture approach
Symposium 2: Whole-Brain Architecture@JNNS2018
‘to create
a human-like AGI
by learning from
the architecture of
the entire brain’
Artificial General Intelligence
Priority is to seek AGI-specific technology X
Symposium 2: Whole-Brain Architecture@JNNS2018
BS-
AI
Knowledge
Knowledge
Knowledge
Knowledge
Task
Task
Task
Task
Big switch statement AI
Switch
Task
Task
Task
Task
AGI
AGI
Know
ledge
Know
ledge
Know
ledge
Know
ledgeKnow
ledge
Ability to generate valid
hypotheses with less data
Technology X: Inference
mechanism by combining
existing knowledge
Technology X= AGI −
𝒊
Specialized AI 𝒊
<Gap
<Gap
<Gap
How to build "Technology X"
Symposium 2: Whole-Brain Architecture@JNNS2018
Essential technology specific to AGI
Technology X: Inference
mechanism by combining
existing knowledge
① Acquisition of highly
reusable knowledge
(machine learning)
• Extension of
mapping scope
• Disentanglement
• Correspondence
between distributed
expressions and
symbols
• Standardization of
representation
② Building Architecture:
Connect reusable ML
modules mimicking the
whole brain.
(All parts are aligned. )
③ Theory for exploring
hypotheses
A theory for efficiently
searching promising
combinations of knowledge as
hypotheses out of a large
number of possibilities.
Find combinations of
knowledge according to the
task
Utilization of knowledge:
Architecture that can
combine knowledge in
various ways
Task
Task
Task
Task
Know
ledge
Know
ledge
Know
ledge
Know
ledgeKnow
ledge
Whole Brain Architecture approach
Symposium 2: Whole-Brain Architecture@JNNS2018
‘to create a human-like AGI by learning from
the architecture of the entire brain’
To reach AGI, we start from mimicking high-level architectures
of the brain, and gradually introduce details as needed
Re-
cognition
Executive
Reward
Utility
Evaluation
State
Transition
Perception Action
Intelligent Agent
Intention
Goal
State
Utility
State
State
Reward
Generation
Environment
Hippo-
campus BG
Neocortex
Amygdala
AI
(1) Develop machine
learning modules for parts
of the brain
(2) Integrate the modules to
create cogni ve
architecture
Basal Ganglia
Neocortex
Amygdala
Hippocampus
Brain
GeneralNarrow
High
Computationalcost
Low
Super AGI
human brain
Task diversity
Mouse brain
Insects brain
WBA
WBA as a pass point for ‘super AGI’
Symposium 2: Whole-Brain Architecture@JNNS2018
Obstacles & prescriptions in WBA development
Symposium 2: Whole-Brain Architecture@JNNS2018
“Road Map Construction” Problem:
How can we define the capability set required for
AGI and plan overall development?
“Trap of Specialization” Problem:
How can we construct an integrated AGI, not a
collection of narrow AIs from different
development projects?
"Engineers are not Neuroscientists":
How can AI/ML engineers without full
understanding of the brain mechanism build
brain-inspired AGI?
Knowledge base:
• Brain organs framework
• Connectome architecture
PrescriptionsObstacles
Stub-driven development:
• Brain-constrained
refactoring
• Merge development
Function map:
• Termination condition
• Iterative development
Knowledge base:
• Brain organs frameworks
• Connectome architecture
Agent
Knowledge base:
Symposium 2: Whole-Brain Architecture@JNNS2018
St.
ML
St.
St.
Environment
: stub
: ML(WBA Development)
: Brain organ's I/F
St.
ML
Tasks
(test)
Brain Organ Frameworks
Brain organs I/F
(Information processing
semantics)
WBCA
(Connectome)
Functions of
brain organs
and circuits
Creating Specifications for engineers to develop
WBA
※ Stub : a piece of code
used to stand in for some
other programming
functionality
Proto-
typing
Complete
WBA
追加開発
プロト開発
プロト開発
マージ開発
プロト開発
Stub-driven development to avoid the Big-switch
Symposium 2: Whole-Brain Architecture@JNNS2018
ML
St.
St.
St.
Environment
St.
ML
St.
St.
Environment
St.
St.
ML
St.
Environment
ML
ML
St.
St.
Environment
St.
St.
ML
ML
Environment
追加開発
改良開発
ML
ML
St.
St.
Environment
St.
ML
ML
ML
Environment
マージ開発
ML
ML
ML
ML
Environment
Replacing with ML : Expanding inductive reasoning
Generality of
Brain-inspired
Architecture
① Brain organ
Frameworks
(I/F, functions)
②Brain-
constrained
refactoring
Expantion
ML
ML
ML
ML
Environment
③Meta-level
mechanism for
exploring hypotheses
:Stub
:ML [WBA Development]
:Brain organs I/F
St.
ML
Entire
Architecture
Add
Prototype
Prototype
Merge
Prototype
ML
St.
St.
St.
Environment
St.
ML
St.
St.
Environment
St.
St.
ML
St.
Environment
ML
ML
St.
St.
Environment
St.
St.
ML
ML
Environment
Add
Improvement
ML
ML
St.
St.
Environment
St.
ML
ML
ML
Environment
Merge
ML
ML
ML
ML
Environment
山川宏
おわりにあたってOct.6th-8th , 2018
History of WBA hackathons
Symposium 2: Whole-Brain Architecture@JNNS2018
Key Concepts Hackathon themes
2015201620172018
The Whole Brain Architecture
Core Hypothesis Combining ML
Cognitive Architecture
Open platform
strategy
Learning from
the Brain
• Tactile
• Hippocampus
GPS criteria Gaze control
The GPS criteria
Symposium 2: Whole-Brain Architecture@JNNS2018
• Functionally General
• Biologically Plausible
• Computationally Simple
Winner
Susumu Ota
built a system that
accomplished
5/6 tasks
Hackathon 2018 Point of arrival
If only partially, a system that
can be called WBA that meets
the GPS criteria was realized for
the first time !
Symposium 2: Whole-Brain Architecture@JNNS2018
Six tasks in the Hackathon
Symposium 2: Whole-Brain Architecture@JNNS2018
Point To
Target
Random Dot Motion
Discrimination
Multiple
Object
Tracking
Visual Search
Change
Detection
Odd One Out
Function allocation in the winner’s model
From https://github.com/wbap/oculomotor/wiki/en:Architecture-Summary
Template
Opt. Flow Accumulator
Phase
Working Memory
Allocentric Image
Subsumption
Architecture
Task
Saliency Map
Actor-CriticReLU
Retina Image
Retina + Angle
Reward
Action
Symposium 2: Whole-Brain Architecture@JNNS2018
Functions requirement and results for each task
Function Search
Saliency
Map
Template
Matching
Working
Memory
Optical
Flow
Results
Tasks
Point To Target ○ ○ ○ ◯
Change
Detection ◎ ◯
Odd One Out ◎ ◎ ○ ◯
Visual Search ○ ○ ○ ◯
Multiple Object
Tracking ○ ○ ×
Random Dot
Motion
Discrimination
◎ ◯
Symposium 2: Whole-Brain Architecture@JNNS2018
Representations used in the system
Symposium 2: Whole-Brain Architecture@JNNS2018
Opt. Flow
4 kinds of accumulators
Phase
Working
Memory
Task
Saliency
Map
Retinal
Image
Template
The basal ganglia select which
accumulator to use
Video
• Point to Target: Input
• Point to Target:
Inspector
• All Task: Input
• All Task: Inspector
Historical impact of AGI
Symposium 2: Whole-Brain Architecture@JNNS2018
Collaborative efforts by the AGI development community and other
key stakeholders are necessary to harmonize the welfare of AGI
with the benefits of all humanity. (IEEE, Ethically Aligned Design – Version II, 2017)
The main factor affecting human welfare is intelligence.
AGI automates science, technology, and economic innovation
In reality, a global AGI development race is in progress.
We want to prevent technological monopoly/oligopoly.
The greatest impact since
the Industrial Revolution
Benefits:
Unprecedented
prosperity
Risks:
• Economic inequality
• Malefic uses (e.g., weapons and crimes)
• Effects on people's occupation, dignity, and values.
• Uncontrollablilty
Nonprofit
organizations
Global AI development trend from the viewpoint of AGI
Enterprises
Brain-
inspired AI
Standard narrow
AI development
AGI development
(& promotion)
AGI
development
promotion
AGI
development
Non Brain-
inspired AI
Symposium 2: Whole-Brain Architecture@JNNS2018
Open Platforms
• Guidelines
• Specifications
• Software platforms
• Knowledge
• Advices
Spreading collaborative AGI development
Lead / promote democratic development of Beneficial
brain-inspired AGI
• Direction
• Technical support
Neuroscientists
interested in functional models
AI/ML Experts
Building Brain-inspired AGI
Future Humanity
In harmony with AI
Committed to promote AGI development for humanity
Non-profit organization
2018〜2019
2020〜2021
2022〜2023
2024〜2029
FeedbackBenefits
Primaryactivities
- Become co-authors of AI /
ML papers
- Refer to models in
experimental data analysis
Symposium 2: Whole-Brain Architecture@JNNS2018
A community to
support developing
brain-inspired AI is
necessary.
Open Platforms
• Guidelines
• Specifications
• Software platforms
• Knowledge
• Advices
Spreading collaborative AGI development
Lead / promote democratic development of Beneficial
brain-inspired AGI
• Direction
• Technical support
Neuroscientists
interested in functional models
AI/ML Experts
Building Brain-inspired AGI
Future Humanity
In harmony with AI
Committed to promote AGI development for humanity
Non-profit organization
2018〜2019
2020〜2021
2022〜2023
2024〜2029
FeedbackBenefits
Primaryactivities
- Become co-authors of AI /
ML papers
- Refer to models in
experimental data analysis
Symposium 2: Whole-Brain Architecture@JNNS2018
Positions of 4 presentations in this symposium
Kosuke Miyoshi:
Do top-down predictions of
time series lead to sparse
disentanglement?
Seiya Sato:
Visualization of morphism
tuples of equivalence
structures
Kotone Itaya:
BriCA Kernel: Cognitive
Computing Platform for Large-
scale Distributed Memory
Environments
Masahiko Osawa:
Development of biologically
inspired artificial general
intelligence navigated by
circuits associated with tasks
Symposium 2: Whole-Brain Architecture@JNNS2018
Today’s POSTER
Today’s POSTER
Basic idea of WBAI
Vision: Create a world in which AI exists in
harmony with humanity.
Values:
• Study: Deepen and spread our expertise.
• Imagine: Broaden our views through public dialogue.
• Build: Create AGI through open collaboration.
http://wba-initiative.org/en/2171/
Mission: Promote the open development of Whole
Brain Architecture
‘to create a human-like AGI by learning from the architecture of the entire brain’
Symposium 2: Whole-Brain Architecture@JNNS2018
Summary
• The combinatory space of machine learning to
realize AGI is huge.
– Using brain as reference architecture prevents divergence
of development and makes AGI reachable.
• Three main obstacles for WBA development : “Road
Map Construction, ” “Trap of Specialization” and
“ Engineers are not Neuroscientists. ”
– WBAI is constructing open platforms to overcome these.
• The winner of the latest hackathon implemented a
model that fairly fulfilled the GPS criteria.
• To prevent monopoly/oligopoly of powerful AGI, the
promotion of democratic development by non-profit
position could be valuable.
Symposium 2: Whole-Brain Architecture@JNNS2018
Symposium 2: Whole-Brain Architecture@JNNS2018
Thank you for your attention!

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Strategy to build Beneficial Artificial General Intelligence inspired by the Brain

  • 1. Kkkj Hiroshi Yamakawa, Yutaka Matsuo, Koichi Takahashi and Naoya Arakawa Strategy to build Beneficial Artificial General Intelligence inspired by the Brain Symposium 2: Whole-Brain Architecture 14:00-15:30, Oct. 26th The 28th Annual Conference of the Japanese Neural Network Society (JNNS2018)
  • 2. Abstract Since 2013, we have been advocating the idea of whole brain architecture. The Whole Brain Architecture Initiative (a specified NPO) promotes ‘to create (engineer) a human-like artificial general intelligence (AGI) by learning from the architecture of the entire brain, ’ and explores the way to develop brain-inspired AGI through activities such as hackathons . In recent years, AI researches are energized by deep learning technology and neuroscience researches are heading toward elucidation of high-level cognitive functions. Despite such boosts, obstacles remains on the way to brain-inspired AGI. Here we present our strategy to construct brain-inspired AGI. As AGI's impact on society will be enormous, we should make AI development beneficial to humanity by learning from the brain. Symposium 2: Whole-Brain Architecture@JNNS2018
  • 3. History of WBA Symposium 2: Whole-Brain Architecture@JNNS2018 2013 20152014 20172016 2018 20302025 Goal:CompletionofWholeBrainArchitecture Whole Brain Architecture Seminars WBA Future Leaders The Whole Brain Architecture Initiative 2ndWBAsymposium 3rdWBAhackathon 3rdWBAsymposium 4thWBAhackathon 1stWBAsymposium 2ndWBAhackathon 1stWBAhackathon BasicIdeas(Vision) WBAcorehypothesis VisitGatsbywithKenji ICONIPSession BiCASession DiscussionstartedbyYutakaMatsuo,YujiIchisugiandHiroshiYamakawa KyusyuBranch Branchof workingpeople Kanto/Kansai branch JNNSsymposium
  • 4. Whole Brain Architecture approach Symposium 2: Whole-Brain Architecture@JNNS2018 ‘to create a human-like AGI by learning from the architecture of the entire brain’ Artificial General Intelligence
  • 5. Priority is to seek AGI-specific technology X Symposium 2: Whole-Brain Architecture@JNNS2018 BS- AI Knowledge Knowledge Knowledge Knowledge Task Task Task Task Big switch statement AI Switch Task Task Task Task AGI AGI Know ledge Know ledge Know ledge Know ledgeKnow ledge Ability to generate valid hypotheses with less data Technology X: Inference mechanism by combining existing knowledge Technology X= AGI − 𝒊 Specialized AI 𝒊 <Gap <Gap <Gap
  • 6. How to build "Technology X" Symposium 2: Whole-Brain Architecture@JNNS2018 Essential technology specific to AGI Technology X: Inference mechanism by combining existing knowledge ① Acquisition of highly reusable knowledge (machine learning) • Extension of mapping scope • Disentanglement • Correspondence between distributed expressions and symbols • Standardization of representation ② Building Architecture: Connect reusable ML modules mimicking the whole brain. (All parts are aligned. ) ③ Theory for exploring hypotheses A theory for efficiently searching promising combinations of knowledge as hypotheses out of a large number of possibilities. Find combinations of knowledge according to the task Utilization of knowledge: Architecture that can combine knowledge in various ways Task Task Task Task Know ledge Know ledge Know ledge Know ledgeKnow ledge
  • 7. Whole Brain Architecture approach Symposium 2: Whole-Brain Architecture@JNNS2018 ‘to create a human-like AGI by learning from the architecture of the entire brain’ To reach AGI, we start from mimicking high-level architectures of the brain, and gradually introduce details as needed Re- cognition Executive Reward Utility Evaluation State Transition Perception Action Intelligent Agent Intention Goal State Utility State State Reward Generation Environment Hippo- campus BG Neocortex Amygdala AI (1) Develop machine learning modules for parts of the brain (2) Integrate the modules to create cogni ve architecture Basal Ganglia Neocortex Amygdala Hippocampus Brain
  • 8. GeneralNarrow High Computationalcost Low Super AGI human brain Task diversity Mouse brain Insects brain WBA WBA as a pass point for ‘super AGI’ Symposium 2: Whole-Brain Architecture@JNNS2018
  • 9. Obstacles & prescriptions in WBA development Symposium 2: Whole-Brain Architecture@JNNS2018 “Road Map Construction” Problem: How can we define the capability set required for AGI and plan overall development? “Trap of Specialization” Problem: How can we construct an integrated AGI, not a collection of narrow AIs from different development projects? "Engineers are not Neuroscientists": How can AI/ML engineers without full understanding of the brain mechanism build brain-inspired AGI? Knowledge base: • Brain organs framework • Connectome architecture PrescriptionsObstacles Stub-driven development: • Brain-constrained refactoring • Merge development Function map: • Termination condition • Iterative development Knowledge base: • Brain organs frameworks • Connectome architecture
  • 10. Agent Knowledge base: Symposium 2: Whole-Brain Architecture@JNNS2018 St. ML St. St. Environment : stub : ML(WBA Development) : Brain organ's I/F St. ML Tasks (test) Brain Organ Frameworks Brain organs I/F (Information processing semantics) WBCA (Connectome) Functions of brain organs and circuits Creating Specifications for engineers to develop WBA ※ Stub : a piece of code used to stand in for some other programming functionality Proto- typing
  • 11. Complete WBA 追加開発 プロト開発 プロト開発 マージ開発 プロト開発 Stub-driven development to avoid the Big-switch Symposium 2: Whole-Brain Architecture@JNNS2018 ML St. St. St. Environment St. ML St. St. Environment St. St. ML St. Environment ML ML St. St. Environment St. St. ML ML Environment 追加開発 改良開発 ML ML St. St. Environment St. ML ML ML Environment マージ開発 ML ML ML ML Environment Replacing with ML : Expanding inductive reasoning Generality of Brain-inspired Architecture ① Brain organ Frameworks (I/F, functions) ②Brain- constrained refactoring Expantion ML ML ML ML Environment ③Meta-level mechanism for exploring hypotheses :Stub :ML [WBA Development] :Brain organs I/F St. ML Entire Architecture Add Prototype Prototype Merge Prototype ML St. St. St. Environment St. ML St. St. Environment St. St. ML St. Environment ML ML St. St. Environment St. St. ML ML Environment Add Improvement ML ML St. St. Environment St. ML ML ML Environment Merge ML ML ML ML Environment
  • 13. History of WBA hackathons Symposium 2: Whole-Brain Architecture@JNNS2018 Key Concepts Hackathon themes 2015201620172018 The Whole Brain Architecture Core Hypothesis Combining ML Cognitive Architecture Open platform strategy Learning from the Brain • Tactile • Hippocampus GPS criteria Gaze control
  • 14. The GPS criteria Symposium 2: Whole-Brain Architecture@JNNS2018 • Functionally General • Biologically Plausible • Computationally Simple
  • 15. Winner Susumu Ota built a system that accomplished 5/6 tasks Hackathon 2018 Point of arrival If only partially, a system that can be called WBA that meets the GPS criteria was realized for the first time ! Symposium 2: Whole-Brain Architecture@JNNS2018
  • 16. Six tasks in the Hackathon Symposium 2: Whole-Brain Architecture@JNNS2018 Point To Target Random Dot Motion Discrimination Multiple Object Tracking Visual Search Change Detection Odd One Out
  • 17. Function allocation in the winner’s model From https://github.com/wbap/oculomotor/wiki/en:Architecture-Summary Template Opt. Flow Accumulator Phase Working Memory Allocentric Image Subsumption Architecture Task Saliency Map Actor-CriticReLU Retina Image Retina + Angle Reward Action Symposium 2: Whole-Brain Architecture@JNNS2018
  • 18. Functions requirement and results for each task Function Search Saliency Map Template Matching Working Memory Optical Flow Results Tasks Point To Target ○ ○ ○ ◯ Change Detection ◎ ◯ Odd One Out ◎ ◎ ○ ◯ Visual Search ○ ○ ○ ◯ Multiple Object Tracking ○ ○ × Random Dot Motion Discrimination ◎ ◯ Symposium 2: Whole-Brain Architecture@JNNS2018
  • 19. Representations used in the system Symposium 2: Whole-Brain Architecture@JNNS2018 Opt. Flow 4 kinds of accumulators Phase Working Memory Task Saliency Map Retinal Image Template The basal ganglia select which accumulator to use Video • Point to Target: Input • Point to Target: Inspector • All Task: Input • All Task: Inspector
  • 20. Historical impact of AGI Symposium 2: Whole-Brain Architecture@JNNS2018 Collaborative efforts by the AGI development community and other key stakeholders are necessary to harmonize the welfare of AGI with the benefits of all humanity. (IEEE, Ethically Aligned Design – Version II, 2017) The main factor affecting human welfare is intelligence. AGI automates science, technology, and economic innovation In reality, a global AGI development race is in progress. We want to prevent technological monopoly/oligopoly. The greatest impact since the Industrial Revolution Benefits: Unprecedented prosperity Risks: • Economic inequality • Malefic uses (e.g., weapons and crimes) • Effects on people's occupation, dignity, and values. • Uncontrollablilty
  • 21. Nonprofit organizations Global AI development trend from the viewpoint of AGI Enterprises Brain- inspired AI Standard narrow AI development AGI development (& promotion) AGI development promotion AGI development Non Brain- inspired AI Symposium 2: Whole-Brain Architecture@JNNS2018
  • 22. Open Platforms • Guidelines • Specifications • Software platforms • Knowledge • Advices Spreading collaborative AGI development Lead / promote democratic development of Beneficial brain-inspired AGI • Direction • Technical support Neuroscientists interested in functional models AI/ML Experts Building Brain-inspired AGI Future Humanity In harmony with AI Committed to promote AGI development for humanity Non-profit organization 2018〜2019 2020〜2021 2022〜2023 2024〜2029 FeedbackBenefits Primaryactivities - Become co-authors of AI / ML papers - Refer to models in experimental data analysis Symposium 2: Whole-Brain Architecture@JNNS2018
  • 23. A community to support developing brain-inspired AI is necessary. Open Platforms • Guidelines • Specifications • Software platforms • Knowledge • Advices Spreading collaborative AGI development Lead / promote democratic development of Beneficial brain-inspired AGI • Direction • Technical support Neuroscientists interested in functional models AI/ML Experts Building Brain-inspired AGI Future Humanity In harmony with AI Committed to promote AGI development for humanity Non-profit organization 2018〜2019 2020〜2021 2022〜2023 2024〜2029 FeedbackBenefits Primaryactivities - Become co-authors of AI / ML papers - Refer to models in experimental data analysis Symposium 2: Whole-Brain Architecture@JNNS2018
  • 24. Positions of 4 presentations in this symposium Kosuke Miyoshi: Do top-down predictions of time series lead to sparse disentanglement? Seiya Sato: Visualization of morphism tuples of equivalence structures Kotone Itaya: BriCA Kernel: Cognitive Computing Platform for Large- scale Distributed Memory Environments Masahiko Osawa: Development of biologically inspired artificial general intelligence navigated by circuits associated with tasks Symposium 2: Whole-Brain Architecture@JNNS2018 Today’s POSTER Today’s POSTER
  • 25. Basic idea of WBAI Vision: Create a world in which AI exists in harmony with humanity. Values: • Study: Deepen and spread our expertise. • Imagine: Broaden our views through public dialogue. • Build: Create AGI through open collaboration. http://wba-initiative.org/en/2171/ Mission: Promote the open development of Whole Brain Architecture ‘to create a human-like AGI by learning from the architecture of the entire brain’ Symposium 2: Whole-Brain Architecture@JNNS2018
  • 26. Summary • The combinatory space of machine learning to realize AGI is huge. – Using brain as reference architecture prevents divergence of development and makes AGI reachable. • Three main obstacles for WBA development : “Road Map Construction, ” “Trap of Specialization” and “ Engineers are not Neuroscientists. ” – WBAI is constructing open platforms to overcome these. • The winner of the latest hackathon implemented a model that fairly fulfilled the GPS criteria. • To prevent monopoly/oligopoly of powerful AGI, the promotion of democratic development by non-profit position could be valuable. Symposium 2: Whole-Brain Architecture@JNNS2018
  • 27. Symposium 2: Whole-Brain Architecture@JNNS2018 Thank you for your attention!