soinn : an artificial brainwatanabe-...3 1. understand a voice command 2. learn autonomously from...

SOINN : An Artificial Brain

Osamu Hasegawa

2

Motivation

hard-cording

operator

Confinedenvironment

3

1. Understand a voice command

2. Learn autonomously from the Internet, if needed

3. Generate the procedure and accomplish the task.

We need to develop a robot which …

Autonomous Mental DevelopmentWeng, J., et. al. (2001). Autonomous mental development by robots and animals., Science, 291, 599-600.

Big Data• “Infoplosion” is underway globally on daily basis. Data

accumulation volume is enormous.

• Computers and robots in addition to human beings should use these data for learning, associating, reasoning, knowledge-transferring and forecasting.

• We want to eliminate irrelevant noise automatically.

• SOINN does it!

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Major Advantages of SOINN :

1. SOINN user does not need to define a mathematical model for learning.

2. SOINN can automatically eliminate noise.

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Self-organizing Incremental Neural Network (SOINN)

Noise is eliminated. Pattern is recognized.

SOINN applet demo

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SOINN Algorithm

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SOINN has vast possibilities

• SOINN is algorithm: it works with any language: C, C++, C#, JAVA, GP-GPU, Matlab

• SOINN works with any hardware:– Google Glass, Cellular phone, smart phone, personal

computer, embedded system, etc.

• SOINN works best with internet：– Google Drive, finance, distribution,

climate/environment, education, medicine, etc.

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SOIAM• SOIAM: SOINN for Associative Memory• Learn a pair of vectors (a key and an

associative vector)• Realize the association between the

above pair• One-to-many and many-to-many

association

11Example of one-to-many association

SOIAM (conceptual diagram)

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associative vector

key vector

Node of SOINN

SOINN-PBR

• SOINN-PBR: SOINN for Pattern-Based Reasoning

• Realize reasoning with the pattern-based if-then rules of propositional logic– Furthermore, realize deductive inference by

using the known pattern-based if-then rules• Extend SOIAM to learn the pattern-based

if-then rules

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A∧B→CA→B∨C

SOINN-PBR(conceptual diagram)

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conditional rule

sequential rule

Edge for ‘AND’

SOINN for Real-world Robots

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UNGUIDED ROBOT NAVIGATION USING CONTINUOUS ACTION SPACESirinart Tangruamsub, Manabu Tsuboyama, Aram Kawewong and Osamu Hasegawa

If-then Rules• Use for searching path fragments (from start to goal).

• where• q = ‘from-position’ or ‘current position’• a = ‘robot action’• f = ‘to-position’

q ^ a f

Do an action aq

fqa

SOINN Architecture

SOINN Architecture• Input Layer• Role:

• Receive data from robot• We separated this layer

into 2 parts• Image data (q,f) • Robot action data

• Memory Layer• Role:

• Memorize the data pattern from the input layer

SOINN Architecture• Associative Layer• Role:

• Shows the relation ship between image data and action data (if-then rules).

^q ^ a f

Experiment 1• Robots automatically learns the associations between images including actions

Experiment 1• Autonomous path planning

想起推論１推論２

推論３

推論４推論５推論６

入力データ内部データ

#6253内部データ

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左：1.0右：-1.0(右旋回)

左：1.0右：-1.0(右旋回)

左：1.0右：0.6(斜め右)

左：1.0右：1.0（前進）

左：1.0右：0.6(斜め右)

左：1.0右：-1.0(右旋回)

Start

Goal

Experiment 2

Experiment 2• Training

＋“さん”

＋“よん”

＋“いち”

＋“に”

Experiment 2

Autonomous path planning from 「いち」 to 「に」

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Experiment in crowded environment

Autonomous Robot Navigation

Morioka 2010

Proposedw/o Prop.Method

Final results

Mate Tea Cup …??

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SOINN + internet• With internet SOINN learns instantly

and becomes wiser.– High-speed learning and online learning

• In the ever-changing real world environment, it is extremely difficult to preliminary assume effective attributes.

• SOINN extracts and decides effective attributes by interacting with human beings and the environment.

– Internet information is extremely noisy.• SOINN is highly noise-resistant.

Transfer Learning • SOINN learns basic concepts (“attributes”),

not objectives• SOINN recognizes non-pre-learned

objectives by combining attributes.

boxes

Box-style thing with dial and window＝ microwave

balls

window

microwaves

Defined by human beings

Images for learningdials

transfer

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Alphabet and Dictionary.

Tomato Broccoli

Banana Cinnamon

Image examples for experiments

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“Guess unknown objects”

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Results

CVPR09[1]

IJCNN11[2]

CVPR09[1]

IJCNN11[2]

70 days

6 hours

7 mins

Test time（Approx. 6000imgs） 2 days

4 hours

90 secs

Training time（Approx. 25000imgs）

Without time to extract features

Proposed

Proposed

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We have many modalities.

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Modalities of SOINN Robot

1. Image2. Sound3. 3D-Info4. Pressure5. Weight

Pressure

Sound

Image

Depth

Weight

Mate tea cup

SOINN robot also learn Weight of attributes.

Wooden-attr

ImageDepthSoundPressureWeight

e.g.,

Attribute of “Wooden” is mostly understood

by Sound

Knowledge transfer between robots

Robots teaching robots

Hi, I will tell you how to

make British tea.

Transfer

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In the future, people will grow up with her/his own Artificial Brain to supplement brain functions. 43

MY Artificial Brain,Always with Me !

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Appendix

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SOINNによる超高速・汎用確率密度推定

𝒩𝒩 ：ノードの全体集合

𝒘𝒘𝑛𝑛：ノード 𝑛𝑛の位置ベクトル

𝑡𝑡𝑛𝑛 ：ノード 𝑛𝑛が競合学習で勝者になった回数

𝑇𝑇𝒩𝒩 = ∑𝑛𝑛∈𝒩𝒩 𝑡𝑡𝑛𝑛

𝐾𝐾𝑯𝑯 𝒙𝒙 = 12𝜋𝜋 𝑑𝑑 𝑯𝑯

𝑒𝑒𝑒𝑒𝑒𝑒 − 12𝒙𝒙𝑇𝑇𝑯𝑯−1𝒙𝒙 ： Gauss Kernel

�̂�𝑒 𝒙𝒙 = �𝑛𝑛∈𝒩𝒩

𝑡𝑡𝑛𝑛𝑇𝑇𝒩𝒩

𝐾𝐾𝑪𝑪𝑛𝑛 𝒙𝒙 − 𝒘𝒘𝑛𝑛

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SOINN vs Infinite Gaussian mixture model (IGMM)

実験 : 計算時間

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S-KDE : 𝐻𝐻 = 𝜎𝜎2𝐼𝐼 (行列計算なし)U-KDE : 𝐻𝐻 (行列計算あり)

0

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10000 20000 30000 40000 50000 60000 70000 80000 90000 100000

計算

時間

(s)

サンプル数

KDE-SOINN

S-KDE

U-KDE

KDE-SOINN : Local Kernel

0

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10000 20000 30000 40000 50000 60000 70000 80000 90000 100000

計算

時間

(ms)

サンプル数

KDE-SOINN

Proposed

実験 : 計算時間(前頁の拡大図)

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サンプル数が10倍に増えても計算時間は2倍程度の増加に収まっている！

Local Kernel

soinn : an artificial brainwatanabe-...3 1. understand a voice command 2. learn autonomously from...

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