welcome to whole brain architecutre

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

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

Non-profit organization: Whole Brain Architecture Initiative

Chairperson:

Hiroshi Yamakawa

Introduction to Whole Brain Architecture


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

Abilities of AGI


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

　　Artificial General Intelligence

Domain Knowledge Learning (DKL)

Prior general knowledge

DKL bridging the gap between narrow AI and AGI


Narrow AI (trained)

Machine Learning (mainstream up to now)

Domain Knowledge Domain Knowledge

Narrow AI (untrained)

Rule Rule Rule Rule

Execution

Data

Extent of Domains

Designed know

ledge tends to general

Each expertise are learned in the neocortex


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

Whole brain architecture (WBA)

Our mission is ‘to create a human-like AGI

by learning from the architecture of the entire brain.’


Whole brain architecture (WBA)

Our mission is ‘to create a human-like AGI by learning from the architecture of the entire brain.’

AI Brain

The whole brain architecture (WBA) approach

http://www.sig-agi.org/wba/


Basal Ganglia

Neocortex

Amygdala

Hippocampus

(1) Develop machine learning modules for parts of the brain

(2) Integrate those modules to create a cognitive architecture

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＝ MMLL ＋ ccooggnniittiivvee aarrcchhiitteeccttuurree

This approach is becoming feasible.

WBA approach becomes feasible


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.

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


Brain-inspired is useful for building AGI

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can be a acceptable framework to integrate many essence of preceding architectures.

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gathering knowledge from various field such as cogni&ve science, neuroscience, AI.

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combina&on of ac&ve modules & sets of parameters, curriculum of training, etc.

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divide development depending on brain modules and areas.

The brain is a guide: “Biological plausibility” is not the goal of WBA


World AGI developers’ map


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

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


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)


Positioning WBA in BICA

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


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


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


Winner

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)


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


Charter

(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/


Key activity

BriCA(Brain inspired Computing Architecture)

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WBA roadmap： Merging two streams

Emo&on, Cogni&on, Memory, sensory-‐motor associa&on

２０１５２０２５２０３０

AGI

Smart paFern processing, Planning, social skill, etc.

２０２０

Human architectures

Apes architecture

(1) D

evelop

ing machine

learning m

odules

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

Neuroscientific

knowledge

(2) C

ogni&ve archite

cture

(BriCA language)

Ontology, NLP, etc.


BriCA platform is the scaffold for gathering wisdom


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

Language/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

sensors

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 　


AGI will be beneficial

WBA is now feasible path to AGI and is one of BICA approach

Thanks for your attention WBAI workshop@BICA2015