machine intelligence: promises and challenges

MUSES_SECRET: ORF-RE Project - © PAMI Research Group – University of Waterloo 1/22 1 Techne Summit 2015 © Dr. Alaa Khamis

Machine Intelligence: Promises and Challenges

Alaa Khamis, PhD, SMIEEE

Principal Consultant at MIO, Waterloo, Canada

Associate Professor at Suez University, Egypt

http://www.alaakhamis.org/

Bibliotheca Alexandrina, Alexandria, October 24-25, 2015 Machine Intelligence and Optimization

http://www.isteshaar.com/

http://www.isteshaar.com/




Talk Description


Outline


Human versus Machine Intelligence Credit: Techne Summit 2015

Introduction to Machine Intelligence


• Machine vs. Human

• distinguishing faces

• identifying objects and

• recognizing language sounds Brain

Hamburger


Better in


• Machine vs. Human

• dealing with more complex patterns

such as that exist in financial,

scientific, or product data.

• operations that require fast, precise,

highly repeatable actions

• Working in harsh environments

Machine


Better in


• Brian Functions



Machine Intelligence = Non-biological Intelligence

Inspired from Natural Sciences

Non-biological Intelligence

Neural Networks

Computational Intelligence (CI)Artificial Intelligence (AI)

Metaheuristics

Trajectory-based Population-based

Evolutionary Computing Swarm Intelligence

Mathematics, probability and Statistics

Fuzzy Logic

Inspired from Social Sciences

Classical AI Distributed AI (DAI)

Psychology,

linguistics, logic Neuro-physiology

Bayesian Techniques

Reinforcement Learning

Behaviorist

psychologyParallel

Problem

Solving

Distributed

Problem

Solving

Multiagent-based

Simulation (MABS)



• Levels of Intelligence:

Smart Phones

Phones

Cognitive Phones



Outline


Application Domains and Trends

• Paradigm Shift



• Paradigm Shift

Largest direct marketing platform

World’s largest bookseller

Fastest growing entertainment companies

Fastest growing telecom company

Fastest growing recruiting company

[1]



• Paradigm Shift

18.49

16.77

2.83 2.49

2 1.86 1.78 1.4

0.27

0

2

4

6

8

10

12

14

16

18

20

European

Union

USA Arab World Africa 8 Top Tech

Companies

Russia Canada Australia Egypt

GD

P (

tril

lion U

S$)



302% increase in funding received in 2014 by machine

intelligence start-ups in areas such as natural language processing,

predictive analytics and deep learning

Total venture capital money for pure AI start-ups, by year


• Smart Cities


Smart

City

Market

Source: Frost and Sullivan

Smart cities to crate

huge business

opportunities with a

market value of 1.5

Trillion $ in 2020.


• Consumer Electronics

Smart

Bluetooth®

Speaker

BSP60



[5]

• Robotics



Robots now are with us, within us and among us

• Robotics


[5]


Google commits $1.36

billion for NASA

facility, to house their

robotics, space and

flight technologies [More info:

http://robohub.org/google-commits-

1-36-billion-for-nasa-facility-to-house-

their-robotics-space-and-flight-

technologies/

• Robotics


http://robohub.org/google-commits-1-36-billion-for-nasa-facility-to-house-their-robotics-space-and-flight-technologies/
































◊ Congress has mandated that by

2015, 1/3rd of all US military

missions should be unmanned.

◊ There are 17,300 drones in the

US army inventory.

◊ These drones can carry up to

3000 pounds of weapons.

◊ Fabricated by Boeing

A forward looking infrared

(FLIR) camera onUAV

UAV carrying

Viper Strike

Weapon

System

Source: http://www.marketresearchmedia.com/?p=509

• Robotics


http://www.marketresearchmedia.com/?p=509


The Transparent Car

• Machine Vision


According to a new report

from , the market for

computer vision

technologies will grow from

$5.7 billion in 2014 to

$33.3 billion by 2019 ,

representing a compound

annual growth rate (CAGR)

of 42%.

5.7

33.3

0

5

10

15

20

25

30

35

Year-2014 Year-2019

Mar

ket

siz

e (B

illi

on U

SD

)


Automatic recognition of fabric defects

(visual inspection)

Lane detection Face Detection

• Machine Vision



Alfred Russel Wallace

Charles Darwin

Charles

Darwin

Alan Turing with

Darwin’s beard

Face Recognition

[6]

• Machine Vision



Behind the Mic: The Science of Talking with Computers: https://www.youtube.com/watch?v=yxxRAHVtafI

• Speech Recognition

SPEECH RECOGNIZER

Speaker

Recognized text

Acoustic

Model

Dictionary &

Grammar

Speech signal


https://www.youtube.com/watch?v=yxxRAHVtafI

https://www.youtube.com/watch?v=yxxRAHVtafI


Global voice recognition market to reach $113 Billion in 2017 [More info: http://www.bccresearch.com/pressroom/ift/global-voice-recognition-market-reach-$113-billion-2017 ]

• Speech Recognition


http://www.bccresearch.com/pressroom/ift/global-voice-recognition-market-reach-$113-billion-2017
















• Visual Microphone



• Natural Language Processing (NLP)

◊ Machine translation

◊ Optical character recognition (OCR)

◊ Natural language understanding

◊ Topic segmentation and recognition

◊ Language Modeling in Speech recognition

◊ Information retrieval (IR)and extraction (IE)

◊ …



• Natural Language Processing (NLP)

Source: Natural Language Processing Market, by marketsandmarkets.com, June 2015, Report Code: TC 3492



• Predictive Maintenance



• Predictive Analytics and Big Data

Discover, optimize, and deploy predictive models by analysing

data sources to improve business outcomes.



IoT brings physical and digital worlds together. It replaces

ownership by remote access and sharing.

[2]

• Internet of Things (IoT)



[3]

• Internet of Things (IoT)



Biology: tumor detection,

drug discovery

Energy: Load, price

forecasting, trading

Financial Services: identify prospective customers, dissatisfied

customers, good customers and bad payers.

Security: Face recognition,

Signature/fingerprint/iris

verification, DNA fingerprinting

Internet: Hit ranking,

Spam filtering, Text

categorization

• Others



• Machine Intelligence Landscape


[4]


Technological singularity

hypothesis is that accelerating

progress in technologies will cause a

runaway effect wherein artificial

intelligence will exceed human

intellectual capacity and control, thus

radically changing or even ending

civilization in an event called the

singularity [10].

Application Domains and Trends • Technological Singularity


[5]



Genetics

Emerging Technologies

Nano-technology Robotics

Humanity’s artificial-intelligence capabilities begin to

upstage our human intelligence at the end of the 2030s

[6].

Accelerating progress

in biotechnology will

enable us to

reprogram our genes

and metabolic

processes.

Nanotechnology promises

the tools to rebuild the

physical world, our bodies,

and our brains, molecular

fragment by molecular

fragment and potentially

atom by atom.

Creation of machine

thinking ability that

exceeds the thinking ability

of humans.

Immortality by 2045!



Outline


Challenges


1. Technological Challenges

Challenges


• Big Data

Challenges

90% of the world’s

stock of data was

generated in the

past two years.

99% of that is now

digitized, and over

half IP-enabled.

Multimodal

structured and

unstructured

data (Human-

space, sensor-

space and

Internet-space)

Data is dynamically changing

Relevant data (used to be only 10% of all the data)


• Lack of domain tools

Challenges


• Time Consuming

Challenges

Integrated Machine-learning and Knowledge Acquisition Approach [7]


• Model performance Evaluation and Iterative Process

Challenges E

rro

r

Training Cycles

Training error

Too simple model Too complex model

Underfitting

High bias

Low variance

Overfitting

Low bias

High variance

Best balance

smallest testing error and

acceptable training error

Testing

error


Individual Behaviour

i-Level

Algorithm–based Behaviour

Group behaviour

g-Level

• g-Level Algorithms

Challenges


• g-Level Algorithms

Challenges

The problem of designing both the physical morphology and

behaviours of the individual agents such that when those agents

interact with each other and their environment, the desired

overall collective behaviours will emerge.

At present there are no principled approaches to the design of

low-level behaviours for a given desired collective

behaviour [8].

“collective behavior is NOT simply the sum of each

participant’s behavior, as others emerge at the society level” [9].


Challenges

• Industry-Academia Collaboration


2. Business Challenges

Challenges


• The Innovator’s Dilemma

Challenges


• The Innovator’s Dilemma

Challenges

expensive

navigation systems


3. Social Impact

Challenges


• Social Impact

Challenges


• Social Impact: Privacy

Challenges

Collecting data happens invisibly and passively, as a by product

of another service.


• Social Impact: Impact on human skills and intelligence

Challenges

Research is needed to answer the following open questions:

How does machine intelligence affect human cognitive

processes and reduce our overall intelligence?

Does machine intelligence hurt the development of our

hippocampus, impact our critical thinking skills, hinder

knowledge acquisition, and harm our ability to

concentrate?

Cleveland Amory (1917-1998) American Author

“In my day the schools taught two things,

love of country and penmanship — now

they don’t teach either.”


• Social Impact: Changing Social Norms

Challenges

Physical activities

Face-to-face interaction


• Social Impact: Employment

Challenges

A study out of Oxford

University in 2014 found that

in the near future artificially

intelligent technology could

take over nearly half of all

U.S. jobs.

We can expect a wave of structural unemployment to spring

from the technology in the medium term.


• Social Impact

Challenges Top 10 countries by robot density

(industrial robots per 10,000 manufacturing workers)

[15]


• Social Impact

Challenges

1

10

100

1000

Japan Singapor South

Korea

Germany Sweden Italy Finland Belgium US Spain

Robot denisty

in 2008

Total

unemploymen

t rate in 2008

There is a negative correlation between robot density and unemployment rate (-0.44)



Thank you for your attention

Questions?

View publication statsView publication stats

https://www.researchgate.net/publication/285769829

machine intelligence: promises and challenges

Presentations & Public Speaking