soinn : an artificial brainwatanabe-...3 1. understand a voice command 2. learn autonomously from...
TRANSCRIPT
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SOINN : An Artificial Brain
Osamu Hasegawa
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Motivation
hard-cording
operator
Confinedenvironment
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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 …
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Autonomous Mental DevelopmentWeng, J., et. al. (2001). Autonomous mental development by robots and animals., Science, 291, 599-600.
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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.
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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
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SOIAM (conceptual diagram)
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associative vector
key vector
Node of SOINN
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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’
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SOINN for Real-world Robots
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UNGUIDED ROBOT NAVIGATION USING CONTINUOUS ACTION SPACESirinart Tangruamsub, Manabu Tsuboyama, Aram Kawewong and Osamu Hasegawa
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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
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SOINN Architecture
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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
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SOINN Architecture• Associative Layer• Role:
• Shows the relation ship between image data and action data (if-then rules).
^q ^ a f
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Experiment 1• Robots automatically learns the associations between images including actions
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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
#6303内部データ#11719
内部データ#16564
内部データ#8193
内部データ#58194
内部データ#58278
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
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Experiment 2
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Experiment 2• Training
+“さん”
+“よん”
+“いち”
+“に”
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Experiment 2
Autonomous path planning from 「いち」 to 「に」
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Experiment in crowded environment
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Autonomous Robot Navigation
Morioka 2010
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Proposedw/o Prop.Method
Final results
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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.
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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.
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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
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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
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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
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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)
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SOINN vs Infinite Gaussian mixture model (IGMM)
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SOINN vs Infinite Gaussian mixture model (IGMM)
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SOINN vs Infinite Gaussian mixture model (IGMM)
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SOINN vs Infinite Gaussian mixture model (IGMM)
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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)
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KDE-SOINN
S-KDE
U-KDE
KDE-SOINN : Local Kernel
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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