the evolution of personal assistants: from science fiction to reality to the transparent brain

8/12/2019 The Evolution of Personal Assistants: From Science Fiction to Reality to the Transparent Brain

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The Evolution ofPersonal Assistants

from Science Fictionto Realityto the Transparent rain

Mobile Voice ConferenceSan Francisco, March 3-5, 2014

Marsal [email protected]


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Summary

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ThesisHuman behavior is getting easier to observe and predict.

First Corollary

Personal assistants will become uncannily good.

Second Corollary

Deviating from the recommended course of action

will need to be enshrined as an unalienable right.


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The evolution of taxi receipts

3


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Measuring productivity

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Tracking shoppers

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Source: The New York Times

Sourc

e:

Berry

&Ree

d


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Magnifying motion

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Source:Wu, Hao-Yu, et al. Eulerianvideo magnification for revealing subtle

changes in the world. ACM Transactions

on Graphics (TOG) 31.4 (2012): 65.\


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Predicting user location from historical cell tower data: 93% accuracy

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Source:Science 327, 1018 (2010)


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Amazons Anticipatory shipping

Sour

ce:Amazon

Source:US PTO


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Mechanical chess-playing Turk (Wolfgang von Kempelen, 1770)

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Source:Wikipedia


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Singing automaton Olimpia

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E.T.A. Hoffmanns The Sandman(1816)

Jacques Offenbachs Tales of Hoffmann(1881)

Source:You

Tu

be


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ApplesKnowledge Navigator(1987)

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Messages and calendar

Document search

Related articles

Video conferencing

Article sharing

Data mashup

Source:App

le


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Spike JonzesHer(2013)

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Source: herthemovie.com


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Artificial Intelligence slowly but inexorably improving

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Source: NIST

Progress in

Automatic Speech Recognition

Dynamic speaker adaptation Deep/recurrent neural networks Ultra large language modelsNatural Language Understanding

Conversation and topic modeling Knowledge Graph

570 million entities 18 billion facts & relationships

Machine Learning

Latent factor models for recommendersystemsleads to improved understanding of natural,

human-to-human conversations.

Source: Google


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Deep learning applied to Automatic Speech Recognition

Microsoft

Context-dependent deep neural network, hidden

Markov model:-33% WER

Deep tensor neural networks:-8% WER

Source: Large vocabulary speech recognition usingdeep tensor neural networks, Yu et al. (2012)

IBM

Deep neural network with linear feature-space

maximum mutual information discriminative

objective function:-5% WER

Source: Discriminative feature-space transforms using deep neural networks, Saon et al. (2012)

GoogleContext-dependent ANN/HMM hybrid system

pretrained with deep belief networks:-5% WER

Source: Application of pretrained deep neural networks to large vocabulary

speech recognition, Navdeep Jaitly et al. (2012)


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Deep learning applied to Natural Language Understanding

Source: Recursive deep models for semantic compositionality over a sentiment treebank, Socher et al. (2013)

Stanford Sentiment TreebankFrom semantic vector spaces to

recursive neural tensor networks:-25% sentiment accuracy error


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Knowledge Graphs

From disembodied strings to grounded entities

Yahoo! 10 M entities, 30 M properties, 10 M connections Microsoft 300 M entities, 800 M connections Google 570 M entities, 18 B properties and connections Wikipedia 4 M entities Freebase 40 M topics, 2 B facts Factual 66 M local businesses and POIs in 50 countries LinkedIn 225 M people Facebook 1.15 B peopleCf.

Cyc239 K concepts,2 M facts

OpenCyc6 K concepts,60 K facts

Source:Yahoo!


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Recommender systems

Amazon Netflix Pandora

Spotify FourSquare LinkedIn

Facebook

Source: Tempo AI


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Mobile devices capture data via many sensors

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Cameras,

microphones,

Wi-Fi, LTE, GPS,

and

Source:Samsung

S

ource:

fun

fopensens

ing

frame

work


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Context awareness helps modulate response

Youre on speakerphone and my wife is in the car

Source:

Ento

urage

by

HBO


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Even more sensors are coming

Coupling an electronic skin tattoo to

a mobile communication device

(U.S. Pat. Application 20130297301)

Speech recognition from facial

muscle activity captured by

electrodes (Schultz & Wand, 2010)

Emotiv EPOC

EEG headset

Google Glass

3 axis gyroscope

3 axis accelerometer

3 axis magnetometer ambient light and proximity sensors

bone conduction transducer

camera, Wi-Fi, Bluetooth


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Quantified Self

Source:Giga OM

Source:Rachelle DiGregorio

Source:Giga OM

Source:Facebook


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ALL THAT HAPPENS MUST BE KNOWN

SHARING IS CARING

SECRETS ARE LIES

PRIVACY IS THEFT

WAR IS PEACE

FREEDOM IS SLAVERY

IGNORANCE IS STRENGTH

1949 2013


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The possibility of

total digital surveillance

touches the essence of our life.

It is thus an ethical task

that goes far beyond the

politics of security [ to the]freedom and dignity

of the individual.

Angela MerkelOn Freedom and Security

January 29, 2014Source:The New York Review of Books(March 20, 2014)


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Literally Transparent Brain

26Source: National Geographic Magazine

Source:National Geographic Magazine


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Towards mind reading

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Source:Science 320.5880 (2008)

Source:Carneg

ieMe

llon

Un

ivers

ity


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When one has weighed the sun

in the balance, and measured

the steps of the moon, and

mapped out the seven heavens

star by star, there still remainsoneself. Who can calculate the

orbit of his own soul?Oscar Wilde

De Profundis (1897)


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urce:

Risda

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the evolution of personal assistants: from science fiction to reality to the transparent brain

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