Artificial intelligence

Introduction to AI

1. 1Compiled By -BiniamBehailu

2. CHAPTER –3
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
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3. 3
Explain what artificial intelligence (AI) is.
Describe the eras of AI.
Explain the types and approaches of AI.
Describe the applications of AI in health, agriculture,
business and education
List the factors that influenced the advancement of AI in
recent years.
Understand the relationship between the human’s way of
thinking and AI systems
Identify AI research focus areas.
Identify real-world AI applications, some platforms, and
tools.

4. 4
“Artificial intelligence is now in our living rooms, cars,
and often our pockets. “

5. 5Introduction to Emerging Technologies------------Compiled by BiniamBehailu

6. 6
ArtificialIntelligenceiscomposedoftwowordsArtificialand
Intelligence.
Artificialdefines"man-made,"andintelligencedefines"thinking
power",or“theabilitytolearnandsolveproblems”henceArtificial
Intelligencemeans"aman-madethinkingpower.“
ThetermAIwasintroducedbyProf.JohnMcCarthyataconferenceat
DartmouthCollegein1956.
McCarthydefinesAIasthe“scienceandengineeringofmaking
intelligentmachines,especiallyintelligentcomputerprograms”.

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ArtificialIntelligence(AI)isthebranchofcomputersciencebywhich
wecancreateintelligentmachineswhichcanbehavelikeahuman,
thinklikehumans,andabletomakedecisions.
AIsystemsexhibitcharacteristicsofhumanintelligence.
Artificialintelligence(AI)isthesimulationofhumanintelligence
processesbymachines,especiallycomputersystems.

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AI-basedmachinesareintendedtoperceivetheirenvironmentand
takeactionsthatoptimizetheirlevelofsuccess.
The Intelligence is composed of:
Learning
Reasoning
Problem Solving
Perception
Linguistic Intelligence

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AIresearchusestechniquesfrommanyfields,suchaslinguistics,
economics,andpsychology.
•Controlsystems
•Naturallanguageprocessing
•Facialrecognition
•Speechrecognition
•Businessanalytics
•Patternmatching
•Datamining
•Psychologyetc…

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MachineLearningisanadvancedformof
AIwherethemachinecanlearnasitgoes
ratherthanhavingeveryaction
programmedbyhumans.
Neuralnetworksarebiologicallyinspired
networksthatextractfeaturesfromthe
datainahierarchicalfashion.
Thefieldofneuralnetworkswithseveral
hiddenlayersiscalleddeeplearning.

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Tocreateexpertsystemsthatexhibitintelligentbehaviorwiththe
capabilitytolearn,demonstrate,explainandadviceitsusers.
Helpingmachinesfindsolutionstocomplexproblemslikehumansdo
andapplyingthemasalgorithmsinacomputer-friendlymanner.

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Replicate human intelligence
Solve Knowledge-intensive tasks
An intelligent connection of perception and action
Building a machine which can perform tasks that requires human
intelligence such as:
✓Proving a theorem
✓Playing chess
✓Plan some surgical operation
✓Driving a car in traffic
Creating some system which can exhibit intelligent behavior, learn new
things by itself, demonstrate, explain, and can advise to its user.

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To achieve the intelligence factors for a machine or software AI
requires the following disciplines

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High Accuracy with fewer errors
High speed
High reliability
Useful for risky areas
Digital Assistant
Useful as a public utility

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What could be the disadvantage of AI?
Computational Costs
Unemployment
Can’t think outside of the box
Increase dependence on machines
Potential for miss use
Artificial Super Intelligence

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During the Second World War, noted British computer scientist Alan
Turing worked to crack the ‘Enigma’ code which was used by German
forces to send messages securely.
Alan Turing and his team created the Bombe machine that was used
to decipher Enigma’s messages.
The Enigma and Bombe Machines laid the foundations for Machine
Learning.
In 1956, American computer scientist John McCarthy organized the
Dartmouth Conference, at which the term ‘Artificial Intelligence’ was
first adopted.

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In the late 1990s, American corporations once again became interested
in AI.
The Japanese government unveiled plans to develop a fifth generation
computer to advance of machine learning.
AI enthusiasts believed that soon computers would be able to carry on
conversations, translate languages, interpret pictures, and reason like
people.

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In 1997, IBM’s Deep Blue defeated and became the first computer to
beat a reigning world chess champion, Garry Kasparov.

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Stage1-RulebasedSystems
SimplestformofAI
Businesssoftware(RoboticProcessAutomation)anddomestic
appliancestoaircraftautopilots.
Stage2-ContextAwarenessandRetention
Algorithmsthatdevelopinformationaboutthespecificdomainthey
arebeingappliedin.
Well, known applications of this level are chatbotsand “roboadvisors”.

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Stage3-DomainspecificExpertise
Goingbeyondthecapabilityofhumans,thesesystemsbuildup
expertiseinaspecificcontexttakinginmassivevolumesof
informationwhichtheycanusefordecisionmaking.
Successful use cases have been seen in cancer diagnosis and the well
knownGoogle Deepmind’sAlphaGo.
Stage4-ReasoningMachines
Thesealgorithmshavesomeabilitytoattributementalstatesto
themselvesandotherstheyhaveasenseofbeliefs,intentions,
knowledge,andhowtheirownlogicworks.
Thismeanstheycouldreasonornegotiatewithhumansandother
machines.

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Stage5-Selfawaresystems/ArtificialGeneralIntelligence(AGI)
Thesesystemshavehuman-likeintelligence–themostcommonly
portrayedAIinmedia–however,nosuchuseisinevidencetoday.
It is the goal of many working in AI and some believe it could be
realized already from 2024.
Stage 6-Artificial Super Intelligence (ASI)
AIalgorithmscanoutsmarteventhemostintelligenthumansinevery
domain.

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Stage7-SingularityandTranscendence
Humanaugmentationcouldconnectourbrainstoeachotherandtoa
futuresuccessorofthecurrentinternet,creatinga“hivemind”that
sharesideas,solvesproblemscollectively,andevengivesothers
accesstoourdreamsasobserversorparticipants.

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1. Weak AI/ Artificial Narrow Intelligence (ANI)
Weak AI, also known as narrow AI.
ThisapproachisnotconcernedaboutwhethertheAIsystemsdisplay
human-likecognitivefunctions;thefocusisonAIsystemsthat
performspecifictasksaccuratelyandcorrectly.
It focuses on a specific task.
The strength of ANI is that it focuses on doing something extremely
well, sometimes exceeding a human’s capabilities.
ANI is a good fit for automating simple and repetitive tasks.
Based on their Capabilities

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Examples of ANI are botsand virtual assistants, such as Siri, Microsoft
Cortana, AmazonAlexa, restaurant recommendations, weather
updates, WatsonDeepQA, and customer services chatbotsfor
answering simple and repetitive customer inquiries.
Based on their Capabilities

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2. Strong AI/ Artificial General Intelligence (AGI)
AGI belongs to the strong AI.
It refers to computer systems that exhibit capabilities of the human
brain.
Artificial General Intelligence is the ability of an AI agent to learn,
perceive, understand, and function completely like a human being.
AGI refers to systems or machines that can generally perform any
intellectual task that a human can do.
Based on their Capabilities

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ThethreelargestprojectsworkingonAGIareDeepMind,theHuman
BrainProject(anacademicprojectthatisbasedinLausanne,
Switzerland),andOpenAI.
Based on their Capabilities

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3. Super/ Artificial Super Intelligence (ASI)
ASIreferstomachinesthatsurpasshumansingeneralintelligence.
NickBostrom,definesASIas“anintellectthatismuchsmarterthan
thebesthumanbrainsinpracticallyeveryfield,includingscientific
creativity,generalwisdomandsocialskills.”
Theuniquecapabilitiesofthehumanbrainarethereasonwhy
humanshaveadominantpositionoverotherspecies.
Superintelligentmachinesmightsurpassthehumanbrainingeneral
intelligence.
RegardingASI,manyprominentscientistsandtechnologistshave
ethicalconcernsaboutthefutureofhumanityandintelligentlife.
Based on their Capabilities

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CanwecreateSuperIntelligence?

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Based on their Capabilities

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1.ReactiveMachines
PurelyreactivemachinesarethemostbasictypesofArtificial
Intelligence.
AIsystemsdonotstorememoriesorpastexperiencesforfuture
actions.
Machinesonlyfocusoncurrentscenariosandreactonitasper
possiblebestaction.
Example : IBM’s Deep Blue
Based on their Functionality

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2.LimitedMemory
Limitedmemorymachinescanstorepastexperiencesorsomedata
forashortperiodoftime.
Thesemachinescanusestoreddataforalimitedtimeperiodonly.
Self-drivingcarsareoneofthebestexamplesofLimitedMemory
systems.Thesecarscanstoretherecentspeedofnearbycars,the
distanceofothercars,speedlimits,andotherinformationtonavigate
theroad.
Based on their Functionality

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3.TheoryofMind
TheoryofMindAIshouldunderstandhumanemotions,people,
beliefs,andbeabletointeractsociallylikehumans.
ThistypeofAImachineisstillnotdeveloped,butresearchersare
makinglotsofeffortsandimprovementsfordevelopingsuchAI
machines.
4.SelfAwareness
Self-awarenessAIisthefutureofArtificialIntelligence.These
machineswillbesuperintelligentandwillhavetheirown
consciousness,sentiments,andselfawareness.
Based on their Functionality

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Intelligenceor the cognitive process is composed of three main
stages:
•Observeand inputthe information or data in the brain.
•Interpretand evaluatethe input that is received from the
surrounding environment.
•Make decisions as a reaction towards what you received as an input
and interpreted and evaluated.
Simulating the same stages in building AI systems or models
represents the main threelayersor componentsof AI systems.

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BecauseAIisthescienceofsimulatinghumanthinking,itispossibleto
mapthehumanthinkingstagestothelayersorcomponentsofAI
systems.

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Acquire information from
their surrounding
environments through
human senses.
•Hearing and
•Sight senses
1
st
stage
Humans AI
Represented by the sensing
layer, which perceives
information from the
surrounding environment.
•voice recognition for sensing
voice
•visual imaging recognition for
sensing images

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Interpreting and evaluating
the input data
Human brain
2
nd
stage
Humans AI
Represented by the
interpretation layer
Reasoning and thinking about
the gathered input that is
acquired by the sensing layer.

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Taking action or making
decisions
3
rd
stage
Humans AI
After evaluating the input data,
the interacting layer performs
the necessary tasks.
Robotic movement control and
speech generation are
examples of functions that are
implemented in the interacting
layer.

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Big data
Cloud computing and APIs
Emergence of data science
Advancements in computer processing speed and new chip
architectures

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Cloudcomputingisageneraltermthatdescribesdeliveryofon-
demandservices,usuallythroughtheinternet,onapay-per-use
basis.
Companiesworldwideoffertheirservicestocustomersovercloud
platforms.
Theseservicesmightbedataanalysis,socialmedia,videostorage,e-
commerce,andAIcapabilitiesthatareavailablethroughtheinternet
andsupportedbycloudcomputing.
Cloudplatformcapabilities,suchasavailability,scalability,
accessibility,rapiddeployment,flexiblebillingoptions,simpler
operations,andmanagement.

43. 43Compiled By -BiniamBehailu

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All the significant companies in the AI services market deliver their
services and tools on the internet through APIs over cloud platforms.
API is a software intermediary that allows two applications to talk to
each other.
•IBM delivers Watson AI services over IBM Cloud.
•Amazon AI services are delivered over Amazon Web Services
(AWS).
•Microsoft AI tools are available over the MS Azure cloud.
•Google AI services are available in the Google Cloud Platform.

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Transportation
Home services and robots
Healthcare
Education
Public Safety and Security
Employment and workplace
Entertainment
Agriculture
Banking, Financial Services and Insurance (BFSI)
Manufacturing
Oil and Gas

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Transportation
Self-driven vehicles, such as
driverless cars and unmanned
ground vehicles (UGVs).
Vehicles that can sense their
environment and navigate
without human input

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Home services and robots
Home services and robots have already
entered people’s homes in the form of
vacuum cleaners and personal assistants.
Drones are already delivering packages.

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Healthcare
In healthcare, there has been a huge forward leap in collecting useful
data from personal monitoring devices and mobile apps, electronic
health records (EHRs)in clinical settings, surgical robots that assist
with medical procedures, and service robots that support hospital
operations.

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Public Safety and Security
Improved cameras and drones for surveillance, algorithms to detect
financial fraud, and predictive policing.

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Microsoft AZURE Machine Learning
Google Cloud Prediction API,
TensorFlow,
Infosys Nia,
Wipro HOLMES,
API.AI,
Premonition…

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Machine learning
Natural language processing (NLP)
NLP is the subfield of AI that applies computational techniques to
analyze and synthesize human natural language and speech.
Computer vision (CV)
Business analytics

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NLP is found today in the following types of applications
Machine translation
Search engines, such as Google and Baidu
Spell checkers IBM Watson
Natural language assistants, such as Siri
Translation systems, such as Google translate
News digest, such as Yahoo

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Sample AI Applications
Facebook -When you upload photos to Facebook, the service
automatically highlights faces and suggests friends to tag.
Pinterest -Pinterest uses computer vision, an application of AI where
computers are taught to “see,” in order to automatically identify
objects in images (or “pins”) and then recommend visually similar pins
Instagram –Instagram uses machine learning to identify the contextual
meaning of emoji, which have been steadily replacing slang (for
instance, a laughing emoji could replace “lol”)
Snapchat -Snapchat introduced facial filters, called Lenses, in 2015.
These filters track facial movements, allowing users to add animated
effects or digital masks that adjust when their faces moved.

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“If you’re not concerned about AI safety, you should be. Vastly
more risk(y) than North Korea.”
Elon Musk

57. THANK YOU
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