from data to numbers to knowledge: semantic embeddings by alvaro barbero

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7 de diciembre de 2016

Data Numbers KnowledgeÁlvaro Barbero – Chief Data Scientist

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How do we see the world?

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Understanding correlations

The more, the better

Linear correlations

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Understanding correlations

“Moral virtue is a mean”Aristotle

Convex correlations

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Understanding correlations

Few is bad, but slightly more is very good, unless you skipthe sweet spot and then you are doing terribly, but if you

keep going you get better until you do worse again and afterthat nothing changes and you do so-so

Nonlinear correlations

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THE REAL WORLD IS NON-LINEARShocking revelationsBig data scientists rampage the cities after the newly discovered uselessness of their linear models.

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BIG DATA TIMES

VERY VERY BIG DATA SCIENCE - Since 1802

www.iic.uam.es 8The modelling dilemma

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The modelling dilemma

Easy to understandand explain

Linear models

May not faithfullyrepresent reality

Non-linear models

Accuraterepresentations

Very difficultinterpretation

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The brain trick

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Brain power

Low levelobservations

Multilayered non-linear processing

Abstract yet linear concepts

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Abstraction examples

<<

Art quality

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Embeddings: abstract non-linearity away

Low level observations

High levelembedding

Artificial multilayerednon-linear processing

Deep Network

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word2vec

cat chills on a mat

cat chills mushroom a mat

Socher et al (2013)

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Exploiting the linearity: semantic algebra

King man woman queen

Obama USA Russia Putin

human animal ethics

paella Spain Italy risotto

Cristiano Madrid Barcelona Messi

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Bilingual word2vec

WordSpace

WzhWen

EnglishWords

MandarinWords

Socher et al (2013)

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Text embedding models through Recurrent Neural Networks

High dimensional representation of a sequence

0.1

0.5

1.0

0.0

2.4

The lazy brown foxSutskever et al - Sequence to Sequence Learning with neural networks

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Example: books embeddings

Cilibrasi, Vitányi - Normalized Web Distance and Word Similarity

Jonathan Swift

Oscar Wilde

William Shakespeare

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Embedding artistic styles

Gatys et al – A Neural Algorithm of Artistic StyleBottou et al - Optimization Methods for Large-Scale Machine Learning

Low levelobservations

Styleembedding

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Artistic styles as linear relations

Google Research - https://research.googleblog.com/2016/10/supercharging-style-transfer.html

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An application: furniture embedding

Bell and Bala - Learning visual similarity for product design with convolutional neural networks

www.iic.uam.esTake home message

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Take home message

Reality is highly non-linear

Deep Learning can abstract complexity

away

Complex relations become easy comparisons!

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Álvaro Barbero JiménezChief Data Scientist at Instituto de Ingeniería del Conocimiento (IIC)

Elementos gráficos de apoyo obtenidos en:

@albarjip

Alvaro Barbero