Practical Introduction to AI, Deep Learning, and Large Scale Image Analytics

PRACTICAL INTRODUCTION TO

ARTIFICIAL INTELLIGENCEDEEP LEARNINGLARGE-SCALE IMAGE ANALYTICSKEVIN MADER / FLAVIO TROLESE4QUANT | BIG IMAGE ANALYTICS

PANTALKTUESDAY, MARCH 19 2016 / IMPACTHUB GARAGE ZURICH

https://www.linkedin.com/in/kevinmader

https://ch.linkedin.com/in/flavio-trolese-836a2b6

http://4quant.com/

Flavio Trolese

[email protected] können wir hier eine kurze demo zu video to text machen?

4Quant | BIG IMAGE ANALYTICS

Die Länder, die Österreich umgeben.

↓

Was sind Schweiz, Italien, Slowenien, Ungarn, Tschechische Republik, Deutschland, Slovakei?

http://youtube.com/v/WFR3lOm_xhE

http://4quant.com/javascript-breakout/


http://youtube.com/v/Q70ulPJW3Gk

HOW?


STANDARD MACHINE LEARNING


CORE IDEASWhat is an image?

What a human sees What a machine sees


CORE IDEASFeature Generation → Making the computer see more


CORE IDEASTraining / Validation

With all machine learning techniques it is critical to divide data into training and validation sets.

The algorithm can then be tested (validated) on data it has never seen before to ensure it generalizes


OUTPUT / LOSS FUNCTION


The in order for machine learning to work there has to be a single output for the system which quantifies how well it is working

- the number of correctly identified structures (true-positives)

- the number of correct letters in a sentences

- the score of a game

CORE IDEASLearning from tagged data (supervised)


What is this?

PROBLEMSFeatures can be very difficult to ‘engineer’.

What makes a person a person?

More data doesn’t always lead to better results.


http://youtube.com/v/kFt7WYARAVg

DEEP LEARNING


One of these can recognize without any programming by just experiencing and getting feedback.

THE IDEA


https://flic.kr/p/2eryEj

https://flic.kr/p/2eryEj

The human brain is a large, layered, connected network of neurons.

THE IDEA


https://flic.kr/p/5J4uci

https://flic.kr/p/5J4uci

We understand how some of these layers work and can make computationally fast models for simulating their behavior

THE IDEA


DEEP LEARNING

Deep learning is a set of algorithms in machine learning that attempt to learn in multiple levels, corresponding to different levels of abstraction.


THE IDEA


A machine learning system with millions of inputs

And 1 output

THE IDEA


The networks can get very large (hence the deep)

Here is the Inception Network from Google

TYPES OF ARTIFICIAL NEURONS


Fully-connected → everything connected to everything

Convolutional (CNN) → mix things together

Recurring (RNN) → remember parts of sequences

Recurring Networks


http://karpathy.github.io/2015/05/21/rnn-effectiveness/


Recurring Networks



Given the starting letter h

Predict the rest of the letters


SCHWIIZERDÜTSCH


Goes through hundreds of pages of text character by character and trains neurons to predict the correct output

The text shows the algorithm learning to complete the sentence.

The curve shows how confident it is in each guess

SCHWIIZERDÜTSCH


100 gu sisxt n eigeiua a esSWctaicobemhat,E out?s v t t uew

10K Uhe uf Hountigm don d’Bomura fürsyn al jerisim Sbeour Rucch

65K Übschamt wiänä wo und ebs haGscham, üblart uls zä flusch, zänsert. De Unner sindämzalagsel

100K Totatwärt. Dischtä Tittä vo dä ues und erwiä Gsacht agä schtüswongeilä. Beterischtiongehärne vordä em Verbichunt. Diä Mieräng ader h d Zientlichnig vu CHF


SCHWIIZERDÜTSCH

Spell/Grammar Check (for a language with ‘no rules’) Dialect Detector

Autocomplete

APPLICATIONS


Automatic C code

Wikipedia Text

BEYOND SCHWIIZERDÜTSCH


CONVOLUTIONAL NETWORKS


Pixels Edges Object parts Object models

→ → →

Street, Trees, Fence, Bicycle

UNDERSTANDING COMPLEX SCENES


Self-driving cars need to be able to identify walkways automatically

All point geo-referenced

IDENTIFY WALKWAYS


Understanding what is happening inside of these complex networks

DREAMING



DREAMING

http://youtube.com/v/oyxSerkkP4o

Applying parts of trained networks to other types of images.

TRANSFER


A challenging field

- noisy

- highly variable

- many tissues / diseases look the same

MEDICAL IMAGES


Red are lungs

Yellow are bones

Blue are the other organs

MEDICAL IMAGES


FINDING CANCER


MEDICAL IMAGES


Convolutional neurons act on the image and learn to extract the relevant information

MEDICAL IMAGES


These representations can then be used to automatically find organs like the heart and measure blood flow

→ →

Open Challenges


You need a lot of data to identify (1K-100M)

Some networks learn well, others do not

Parameters can make a huge difference

Intermediate layers can be difficult to interpret

RESOURCESGoogle Cloud Vision APIIBM Watson Vision ServiceMicrosoft Project Oxford

TensorFlowCaffeMoodstocks

4Quant

https://cloud.google.com/vision/

https://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/services-catalog.html

https://www.projectoxford.ai/

https://www.tensorflow.org/

http://caffe.berkeleyvision.org/

https://moodstocks.com/

http://4quant.com/

PRACTICAL INTRODUCTION TO

ARTIFICIAL INTELLIGENCEDEEP LEARNINGLARGE-SCALE IMAGE ANALYTICSTHANK YOUKEVIN MADER / FLAVIO TROLESE4Quant Ltd.

PANTALKTHURSDAY MARCH 19 2016 / IMPACTHUB GARAGE ZURICH

4Quant| BIG IMAGE ANALYTICS

Practical Introduction to AI, Deep Learning, and Large Scale Image Analytics

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