GOOGLE BRAIN

Rawan Al-Omari and Zeina Al-Helwani

Damas ITE, AI Dept. -2012

ANN Presentation – In Supervision of Dr. Maisa Abo AlKassem

Infant’s Vision

Infant’s Vision

Infant’s Vision

Infant’s Vision

Infant’s Vision

Infant’s Vision

Infant’s Vision

Infant’s Vision

Google X Lab

Google Research Team

Stanford

Andrew Y. Ng and Jeff Dean

Machine Learning - Andrew Y. Ng

*Machine Learning; a branch of artificial intelligence research concerned with developing learning algorithms.

Open Questions!

Open Questions!

Can We simulate these neurons?

Open Questions!

Can We simulate these neurons?

If we think of our neural network as

simulating a very small-scale

“newborn brain”

Can We simulate these neurons?

Open Questions!

If we think of our neural network as

simulating a very small-scale

“newborn brain”

Show it YouTube video for

a week, what will it learn?

Can We simulate these neurons?

Open Questions!

If we think of our neural network as

simulating a very small-scale

“newborn brain”

Show it YouTube video for

a week, what will it learn?

Google Brain

LIKE Human Brain!

Previous Work

Supervised Learning

• It uses Labeled Data!

Labeled Data

Learning Process

Labeled VS Unlabeled

Labeled VS Unlabeled

Cat

Labeled VS Unlabeled

Cat

Labeled VS Unlabeled

Cat Cat

Labeled VS Unlabeled

Cat Cat

The Research

Why Unlabeled Data?!

• Cost

Why Unlabeled Data?!

• Cost

• Available Data

Why Unlabeled Data?!

• Cost

• Available Data

• Malicious Data

Why Unlabeled Data? - Malicious Data

Why Unlabeled Data? - Malicious Data

Guerrilla

Why Unlabeled Data? - Malicious Data

Guerrilla

Why Unlabeled Data? - Malicious Data

Guerrilla Kitkat

Unsupervised Features Learning

Self Taught Learning

Data Set & Test Set

YOUTUBE 10,000,000 images

16,000 CPU Cores

1 Billion Connection

ImageNet 22,000 Categories

16,000,000 images

Training Duration

Training Duration

OVER THREE DAYS !!

Image Features

Features

Pixels Edges

Face Parts

(Combination

of edges)

Face Detectors

High-level Features

High-level Features

High-level Features

Model • Autoenocoders

• Pooling

• Local Contrast

Local Contrast

Local Contrast

BEFOR

Local Contrast

BEFOR

Local Contrast

BEFOR AFTER

Architecture

Architecture

Architecture

Architecture

Architecture L

ayer

1

Image size 200

Architecture L

ayer

1

Image size 200

first sub layer

Architecture L

ayer

1

Image size 200

second sub layer

Architecture L

ayer

1

Image size 200

third sub layer

Architecture L

ayer

1

Image size 200

fourth sub layer

Layer

1

Layer

9

…..

Cats and Faces Detector

Model Parallelism

Model Parallelism

Asynchronous Parallel Model

Larg

e Sc

ale

Largest network to date

Large Scale Human Visual Cortex 106

Experiments

Experiments

Experiments

Google Brain

• 74.8% cat

• 76.7% human body

Experiments

Google Brain

• 74.8% cat

• 76.7% human body

Best linear filters

• 67.2% cat

• 68.1% human body

Experiments

Google Brain

• 74.8% cat

• 76.7% human body

Best linear filters

• 67.2% cat

• 68.1% human body

OpenCV

• 3% of 100,000 samples

9.3% State-of-the-art

9.3% State-of-the-art

15.8% Our method

Dataset version 2009 (∼9M images, ∼10K

categories)

2011 (∼14M images, ∼22K

categories)

State-of-the-art 16.7% 9.3%

Our method 19.2% 15.8%

9.3% State-of-the-art

15.8% Our method

Experiments - Stats

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

Random guess

Best linear filter

Google Brain

Faces

Human bodies

Cats

Conc l u s i o n

Conclusion!

• Largest network to date !

• Leading to significant advances in area as :

– Machine Vision

– Speech Recognition

– Language Translation

• Google Brain LIKE Human Brain.. it may just be a

matter of Time!

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Thank you!