The document discusses a workshop on whole brain architecture held at BICA 2015. It provides an agenda for the workshop including introductions and presentations on whole brain architecture and the WBAI initiative. The WBAI aims to create human-level artificial general intelligence by 2030 by learning from the entire brain architecture and taking a collaborative open community approach. A key activity is the BriCA project to develop a brain-inspired cognitive architecture platform.

1. Let’s build a brain together
BICA 2015 participants of the WBAI
Chairperson
Vice-chair
Team Ito
WBAI Workshop@BICA2015, November 7
https://liris.cnrs.fr/bica2015/wiki/doku.php/program

2. Chair: Hiroshi Yamakawa(WBAI chair),
Tarek Besold(Free University of Bozen-Bolzano)
20 minutes: Hiroshi Yamakawa
Introduction to the Whole Brain Architecture
10 minutes: Koichi Takahashi (WBAI vice-chair)
Open development platforms for the Whole Brain Architecture Project
10 minutes: Takeshi Ito
1st WBAI hackthon winners' presentation:
Modeling the development of place cells in hippocampus
20 minutes: Panel Discussions:
”Positioning WBA in BICA”
Moderator： Tarek Besold
Panelist: Koichi Tkahashi, Takashi Omori(WBAI),
Satoshi Kurihara(WBAI),
Antonio Chella(Università degli Studi di Palermo)
WBAI workshop@BICA2015
Agenda

3. Non-profit organization:
Whole Brain Architecture Initiative
Chairperson:
Hiroshi Yamakawa
Introduction to
Whole Brain Architecture
Let’s build a brain together

4. Artificial General Intelligence (AGI)
WBAI workshop@BICA2015
Narrow AIs are mature
n Operates intelligently within a
particular domain
n Many systems with
capabilities exceeding those
of people have already been
implemented, for example:
n computer shogi/chess
n Google Self-Driving Car
n medical diagnosis
AGI is our technological goal
n Learning problem-solving from
various perspectives in multiple
domains
n Can solve new problems that
exceed the assumptions made
during its design
n Self-awareness / autonomous
self-control
n Original goal of AI research,
but it was difficult.
Learning expertise
Designing expertise

5. Abilities of AGI
WBAI workshop@BICA2015
Robustness: Can handle
exceptional situations.
Creativity: Creates hypotheses
and understands the universe.
Development costs are lower than narrow AIs
Disruptive innovation
Generalist AI: (1) Make decision by integrating
diverse specialist. (2) Communicating with each
specific user with wide range of of topics
Autonomy
Exploring the
world, without
others' controls.
Versatile
Learning various
problem-solving
capabilities
Will be
beneficial
for humanity

6. Artificial General Intelligence
Domain Knowledge Learning (DKL)
Prior general knowledge
DKL bridging the gap between narrow AI and AGI
WBAI workshop@BICA2015
Narrow AI (trained)
Machine Learning (mainstream up to now)
Domain KnowledgeDomain Knowledge
Narrow AI (untrained)
Rule
Rule
Rule
Rule
Execution
Data
Extent of Domains
Designedknowledgetendstogeneral

7. Each expertise are learned in the neocortex
WBAI workshop@BICA2015
1. A Neocortex learn variety of expertise via similar neural mechanisms
2. Deep neural network open the door to understand this mechanism
3. Build AGI is now feasible
Image source: http://bio1152.nicerweb.com/Locked/media/
ch48/48_27HumanCerebralCortex.jpg
l bodily-kinesthetic
l linguistics
l logical-
mathematical
l musical
l interpersonal
l visual
l spatial

8. Whole brain architecture (WBA)
Our mission is
‘to create a human-like AGI
by learning from the architecture
of the entire brain.’
WBAI workshop@BICA2015

9. Whole brain architecture (WBA)
Our mission is ‘to create a human-like AGI by
learning from the architecture of the entire brain.’
AIBrain
The whole brain architecture (WBA) approach
http://www.sig-agi.org/wba/
WBAI workshop@BICA2015
Basal
Ganglia
Neocortex
Amygdala
Hippocampus
(1) Develop machine
learning modules
for parts of the
brain
(2) Integrate those
modules to create a
cognitive
architecture

10. WWhhoollee BBrraaiinn AArrcchhiitteeccttuurree
＝ MMLL ＋ ccooggnniittiivvee aarrcchhiitteeccttuurree
This approach is becoming feasible.
WBA approach becomes feasible
WBAI workshop@BICA2015
To construct an AGI, mimicking a brain is obviously reasonable,
because there are no AGI systems other than human ones.
One can consider deep
learning as a model of
some early regions of
neocortex.
Connectomics can help
formation of learning
machines in a brain-like
way.

11. Neuroinformatics for a cognitive architecture
n Current situation: Macroscopic neuroscientific knowledge of the brain
(connectome) is ever increasing.
n Challenge of neuroinformatics:
n Neuroscientific knowledge should be transformed into cognitive
architectures.
Connectome
（neuroscientific knowledge）
Network of learning machine
→ going to whole brain scale
Cogni&ve
architecture
described
by
architecture
descrip&on
language
WBAI workshop@BICA2015

12. WBAI workshop@BICA2015

13. Brain-inspired is useful for building AGI
RReeaacchhiinngg AAGGII
iiss gguuaarraanntteeeedd
can
be
a
acceptable
framework
to
integrate
many
essence
of
preceding
architectures.
SSccaaffffoolldd ttoo
ggaatthheerr wwiissddoomm
gathering
knowledge
from
various
field
such
as
cogni&ve
science,
neuroscience,
AI.
HHiinnttss ffoorr uunnaacchhiieevveedd
ffuunnccttiioonnss
combina&on
of
ac&ve
modules
&
sets
of
parameters,
curriculum
of
training,
etc.
CCoollllaabboorraattiivvee
ddeevveellooppmmeenntt
divide
development
depending
on
brain
modules
and
areas.
The brain is a guide:
“Biological plausibility” is not the goal of WBA
WBAI workshop@BICA2015

14. World AGI developers’ map
WBAI workshop@BICA2015
Biologically
plausible
Engineering
Neocortex
centered:
Nengo
(2015〜)
(2015〜)
OPEN
OPEN
Entire
brain
CLOSED
OPEN
OPEN
(2015〜)OPEN
Collabora&on
of
AGI
development
is
discussed
with
some
open
oriented
partners

15. Position of the WBA in AGI
Project
Name
Biological plausibility
Inside of modules
Remarks
WBA
Strong about
architecture
(connectome, etc.)
Machine learning
（mainly ANN）
2013〜
GoodAI
Little strong
Artificial neural network
2013〜
CogPrime
Weak
Mainly machine learning
2006〜
ACT-R
Yes (identify the
module position in
fMRI)
Production system
1973〜
Symbolic AI
Nengo
Very strong
Spiking neuron model
2003〜
Science Journal
WBAI workshop@BICA2015

16. n Whole brain building by collaboration
n Standardization for collaboration
n Neuroinformatics for cognitive architecture
n Target is AGI
n Distributed representation
n Functional modeling (Won't seek detail eagerly)
WBAI workshop@BICA2015
Positioning WBA in BICA

17. WBA movement began in 2013 in Tokyo
n Objective：
n Researchers in AI, neuroscience, and cognitive science meet
and develop new talent in these multiple fields
n Founding members:
n Hiroshi Yamakawa (Dwango AI lab)
n Yutaka Matsuo (Tokyo University)
n Yuji Ichisugi (Advanced Industrial Science and Technology)
n Seminar:
n As of Aug 2015, 11 seminars have been organized.
(average about 200 people, max. 420 participants)
n Related Facebook Group: 2,436 members
n Youth Assembly ‘WBA Future Leaders’ was organized
in the summer of 2014
n Almost every month held a study on subject
such as machine learning
WBAI workshop@BICA2015

18. 1st WBAI Hackathon (Sept. 19-23, 2015）
若手中心に5日間で複
合機械学習器を作成
審査基準
1. 実現機能と解決タスクの重要性
2. 実現可能性 （完成度）
3. 独創性、発展性
4. 神経科学的な現実性
WBAI workshop@BICA2015

19. 1st WBAI Hackathon (Sept. 19-23, 2015）
Theme:
Programing machine learning complex
Teams
Nakamura team:Rebuilding deep learning machine
Tsuzuki team: Synthesis and visualization of concept
according by Word2dream - Toward the
creative machine
Nishida team: Comparison of imitation learning using video
games
Ito team： Modeling the development of place cells in
hippocampus
Doi team： Japanese sign language recognition system using
CNN-LSTM
Hiroshiba team： Acquisition of a mirror self-recognition mechanism
Parmas team： Using neural networks to find an efficient state
space for model-based reinforcement learning
using Gaussian processes
Criteria
1. Impact
2. Feasibility
3. Originality,
potential
4. Biological
plausibility
https://youtu.be/0QS5Z3WrHSA
WBAI workshop@BICA2015
Winner

20. WBAI mission
WBAI aims to build a human-like AGI until
2030, by learning from the entire architecture of
the brain. We will build a collaboration platform
(BriCA), and promote a development community.
As an NPO, we contribute to the co-
evolutionary future of AI and humanity, through
the open community-based development of AGI.
(Founded Aug. 21, 2015)
WBAI workshop@BICA2015

21. l Long-lasting:
We aim to build AGI with the WBA approach by 2030.
l Open community development of AGI
l Promoting cooperation with related disciplines:
neuroscience, AI, cognitive science, machine learning, etc.
l Developing multidiscipline human resources
l R&D for WBA developmental environment:
l evaluation method of AGI
l simulator / data for AI learning
l software platform to integrate machine learning
WBAI workshop@BICA2015
Charter

22. (1) Whole Brain Architecture Seminars (since 2013)
• 11 sessions to date, with max. 500 participants
• Facebook group with over 2,400 members
(2) BriCA project (see right)
(3) WBAI Hackathon:
The first camp held in September 2015
(4) Fostering resources for supporting future AI
development
• design curricula for developing multidiscipline talent
• supporting the WBA Future Leaders Association
(since summer 2014) http://wbawakate.jp/
WBAI workshop@BICA2015
Key activity

23. BriCA(Brain inspired Computing Architecture)
SSoopphhiissttiiccaattee
nneeooccoorrtteexx mmooddeell
WBA roadmap： Merging two streams
Emo&on,
Cogni&on,
Memory,
sensory-­‐
motor
associa&on
２０１５
２０２５
２０３０
AGI
Smart
paFern
processing,
Planning,
social
skill,
etc.
２０２０
Human architectures
Apes architecture
(1)
Developing
machine
learning
modules
Visual/
Auditory
cortex
Deep
learning,
Bayesian
net
Basal
ganglia＋thalamus
Reinforcement
learning
Hippocampal
forma&on
SLAM、Invariance
search
Language
area
??
Prefrontal
cortex
Social/
Logical
func&on
Amygdala
Value
system
Motor
cortex
+
Cerebellum
Control
system
Language,
crea&vity,
logical
thinking,
etc.
Increase
cogni&ve
func&on
Rodents architectures
Connectome etc
Neuroscient
ific
knowledge
(2)
Cogni&ve
architecture
(BriCA
language)
Ontology,
NLP, etc.
WBAI workshop@BICA2015

24. BriCA platform is the scaffold for gathering wisdom
WBAI workshop@BICA2015
Standardized architecture description language is the key to the
sharing, distribution, recombination, re-use, and replacement of
the ML modules that constitute WBA.
Hardware
layer
Execution
layer
Languag
e/Module
layer
User
interface
layer
BriCA core： Execution mechanism for
multi-module cognitive architecture,
handling and scheduling various
machine learning modules.
Cognitive architecture： Description of
module connectivity information based
on neuroscientific data (connectome )
BriCA language: Architecture
description language for combining
machine learning modules
• inter-module interface description
• hierarchical organization of modules
• independent of execution layer
sensor
s
Cognitive architecture
of a whole brain
Host
computer
BriCA Core
（Virtual/Real time scheduler）
Control &
monitoring
GPGPU・
FPGA・MIC・
Neuromorphic
Environment
(data
generation)
We start from
virtual mouse
experiments.
actuators
Application
layer
Machine
learning
modules
include
utilization of
various
existing tools
Architecture
description
by
BriCA
language
Interpreter

25. Let’s build a brain together
AGI will be beneficial
WBA is now feasible path to AGI
and is one of BICA approach
Thanks for your attention
WBAI workshop@BICA2015