WHAT IS ARTIFICIAL INTELLIGENCE

Abdullah Mutawi

Partner, Head of Corporate Commercial

The term ”Artificial Intelligence” wascoined in 1955 by John McCarthy as"the science and engineering ofmaking intelligent machines”

FIRST STUDY OF ARTIFICIAL INTELLIGENCE

“We propose that a 2 month, 10 man study of artificial

intelligence be carried out during the summer of 1956 at

Dartmouth College in Hanover, New Hampshire. The study is to

proceed on the basis of the conjecture that every aspect of

learning or any other feature of intelligence can in principle be

so precisely described that a machine can be made to simulate

it. An attempt will be made to find how to make machines use

language, form abstractions and concepts, solve kinds of

problems now reserved for humans, and improve themselves.

We think that a significant advance can be made in one or

more of these problems if a carefully selected group of

scientists work on it together for a summer.”“A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECTON ARTIFICIAL INTELLIGENCE” - McCarthy et al 1955

OBJECTIVES OF STUDY

1. Simulating higher functions of the human brain

2. Programming a computer to use general language

3. Arranging hypothetical neurons in a manner so that they can form concepts

4. A way to determine and measure problem complexity

5. Self-improvement

6. Abstraction: Defined as the quality of dealing with ideas rather than events

7. Randomness and creativity“A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECTON ARTIFICIAL INTELLIGENCE” - McCarthy et al 1955

• Initially, researchers were seeking to develop a general-problem solver.

• It became clear by the early 70’s was that the complexity of emulating human capabilities such as common sense and intuition proved too difficult to codify given the computing power available at the time.

• Researchers shifted from trying to develop general intelligence to applying principles of machine learning for specific problems

• AI became divided into specialized tasks allowing scientists to turn towards yielding more concrete results for practical applications to be used by industries

EVOLUTION FROM GENERAL TO SPECIALIZED AI

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TIMELINE

1956DartmouthConference

2008Deep Blue computer beat Chess master Kasparov

2010-2011IBM’s Watson(QAM)Beat JeopardyWinners on National TV

TodayGoogle, Amazon, Microsoft Virtual Assistants in Every Home

1966ELIZE, the first chatbot, developed at MIT and is the predecessor of the likes of Siri and Alexa

2015Researchers declare computers are better at recognizing images than humans (ImageNet Challenge)

2018Google spin-off Waymo released (self-driving taxi service)

2016Deep Mind's AlphaGo beat Go world champion over the course of five matches

AI ELEMENTS

• PLANNING• DECISION MAKING• DATA ANALYTICS• LANGUAGE GENERATION• LANGUAGE UNDERSTANDING• REASONING• PREDICTIVE INFERENCE• COGNITIVE COMPUTING

• IMAGE RECOGNITION• IMAGE IDENTIFICATION• FACE RECOGNITION• FACE IDENTIFICATION

• SPEECH RECOGNITION• SPEECH

IDENTIFICATION• AUDIO RECOGNITION• AUDIO

IDENTIFICATION

• NATURAL LANGUAGE COMMUNICATION

• AFFECTIVE COMPUTING• EMOTION RECOGNITION

• CONTROL• MANIPULATION• MOBILITY

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Creating AI to mimic specifichuman action - what you see incomputer applications,automated processes and robots -an emerging reality that couldrevolutionize our lives. From self-driving cars, to digital assistants,predictive and risk models,decision engines andrecommenders, it is fastbecoming a source of competitiveadvantage for businesses

Has to do with machinesbeing able not only to actlike but have awareness ofwhat is being done. Thefamiliar picture of fictionalAI where machines becomeindistinguishable fromhumanity and turn into athreat. We are a fewdecades out of this withsome even questioning itsfeasibility.

General Specialized

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AT THE CORE OF ARTIFICIAL INTELLIGENCE IS THE FIELD OF MACHINE LEARNING

STRONG AI VS WEAK AI

A simulation of humanthought and interaction.Ultimately a set ofprogrammed responsesor supervised interactions

Uses more clustered orassociation data processing.Typically known for beingable to "teach" itself things- for example, games andanticipating moves.

SupervisedLearning

UnsupervisedLearning

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SUPERVISED LEARNING

Machine Learning in Cybersecurity – Booz – Dec. 2018

UNSUPERVISED LEARNING

Machine Learning in Cybersecurity – Booz – Dec. 2018

MAIN DIVISIONS OF AI

MachineLearning

Deep Learning

Neural Networks

Mimics biological neural networks

Uses statistics and data to help improve

machine functions

Computes multi-layer neural networks for more advanced

learning

IT’S ALL ABOUT BIG DATA

The expected volume of dataaccumulated will increase from 4.4 zbto 44 zb (44 trillion GB)

+

advanced algorithms

high-end computing power and storage

Estimated that 70%of enterprises willimplement AI by2020

+

USES OF AI IN DAILY LIFE

BOUNDLESS POTENTIAL

• Customer Service: 45% of customers prefer chatbot interactions. Globalchatbot market could potentially comprise 25% of customer serviceinteractions by 2020

• Journalism: Major media institutions already deploying machine learningtech to generate content.

• Financial Services: fraud detection, credit scoring, check image captureand loan underwriting

• Healthcare: IBM Watson provides diagnosis suggestions to doctors basedon information given by patients.

• Education: EdTech start-up ALEKS breaks down knowledge domain into anetwork of concepts and evaluates how much someone has learned,allowing students to learn in different speeds.

• Defence: Several countries either developing or already deploying AItechnology in the battlefield

CONTROVERSIES

• Tesla CEO recently claimed that super computers could become "an immortal dictator from which we would never escape.”

• Reports claim that South Korean university Korea Advanced Institute of Science and Technology (KAIST) has partnered with a defense company to create "killer robots." Some 50 academics reportedly signed a call of boycott of KAIST and Hanwha Systems amid concerns of an arms race.

• Regulation of AI artificial intelligence use and expert as related to national security.

Still, it appears that, with every innovation in AI, new concerns over ethics, economics and safety seem to progress with them.

Q&A