The Birth of Doraemon
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Transcript of The Birth of Doraemon
TAIPEI | SEP. 21-22, 2016
Hao Wu, Cheng Hsin Lee 2016/09/21
THE BIRTH OF DORAEMON
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AGENDA
What’s a Robot
The birth of DoraemonFunctions and Application scenarios
Difficulties and ChallengesAI2 for Service Robot
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ABOUT US
潮汐科技(北京)有限公司
Tide Technology (Beijing) Ltd.
2014.4 2015.7 2016.9
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WHAT’S A ROBOT
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WHAT’S A ROBOT
Perception Thinking Decision-making Implementation
Robot is made of a complex system
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WHY DO DORAEMON
All of us have a dream in childhood,to have one Doraemon.
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THE BIRTH OF DORAEMON
R&D for over 1 year,10+ professional teams close cooperation,
more than 200 engineers effort,6 times optimizing on prototypes!
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THE FUNCTION OF DORAEMON
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APPLICATION PERSPECTIVE
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PRESENTATIONListen to reading in familiar voice
Solution:to acquire user’s Voice Model through deep learning method
Practice:Reading for 2~3 minutes(36 sentences)
Application scenarios:Reading news for elder in Children’s voice
Reading story for child in Mother’s voice
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PRESENTATIONEmotion Recognition
To analyze emotion via face recognition.
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DIFFICULTIES AND CHALLENGES
Tegra TX1
AI2 FOR SERVICE ROBOTAI2 = AI Application & Integration
Resources1. Algorithm2. Data3. Calculation capability
Machine Learning1.Unsupervised2.Supervised3.Reinforcement learning
AI2 + Robot1.Environment:Perceive2.People:Understand&Communication3.Autonomous Learning
1.Visual Perception & Understanding
(Supervised Deep Learning)
2.Strategic Dialogue System
(Deep Reinforcement Learning)
PROJECTS
1. Training Env.
2. Run time Env.
HARDWARE
OS – Ubuntu 14.04
CUDA – Nvidia
Performance Libarary - Nvidia
for Visual Perception : Framework –Torch (facebook open source), Neural Talk
For Strategic Dialogue System : Framework – ConvNet, SimpleDS
Dataset : Microsoft COCO & ImageNet
SOFTWARE
Global Opened Dataset Global Opened Model : VGGNet
PROJECT : DATASET & MODEL
CNN
object recognition
RNN
language model
PROJECT : ARCHITECTURE
Smart home environment
PROJECT : TRANSFER OF LEARNING
PROJECT : FINDING
PROJECT : APPROACHDeep Reinforcement Learning
PROJECT
Strategic DialogueDeep Reinforcement
LearningStrategic Dialog System
For compliance
REFERENCE
● CVPR 2015 Paper● Deep Visual-Semantic Alignments for Generating Image Descriptions Andrej Karpathy, Li Fei-Fei● http://www.cs.toronto.edu/~frossard/post/vgg16/● H. Cuayáhuitl. SimpleDS: A Simple Deep Reinforcement Learning Dialogue System. International
Workshop on Spoken Dialogue Systems (IWSDS), 2016● https://www.cs.utexas.edu/~eladlieb/RLRG.html
TAIPEI | SEP. 21-22, 2016
Thank you