IA:#Hard#vs#So7#Compu<ng# Topics#and#Techniques Topics#and#Techniques Logic#and#AI#

(1)

1 BBS#Tel#Aviv#2016

IA:#Hard#vs#So7#Compu<ng#

Soft Computing

Subconscious Sub-simbolic

Connectionism (Hebb 40’s)

Computa<on# (and# mental# processes)# as#

the# emergent# process# of# interconnected#

networks#of#simple#units.

#

Neural#networks#(deep#learning) Swarm#intelligence

Hard Computing

Conscious Symbolic High level

Computa<on# as# manipula<on# of# symbols#

via#structured#rules#

RuleLBased#Systems#(Op<miz.#and#search) Logic#and#Constraint#Programming Ontologies

Probabilis<c#computa<on

…

2 BBS#Tel#Aviv#2016

Topics#and#Techniques

Knowledge#representa<on#

Logic Ontologies Sub0symbolic

Problem#solving

Search6(in6large6spaces) Op:miza:on

Adversarial6search6(games) Constraint6Sa:sfac:on6Problems

#Reasoning

Inference Planning PROLOG6and6CLP

Uncertain6reasoning

Probabilis:c6reasoning Markov6decision6processes Bayesan6networks

Neural6networks

Feed6forward6network Recurrent6network Deep6learning

Learning6and6data6analy:cs

Supervised Unsupervised Renforcement Induc:ve

3 BBS#Tel#Aviv#2016

Topics#and#Techniques

Communica<on

Natural6Language6Processing Human0machine6communica:on Automa:c6transla:on

Percep<on

Acous:c6percep:on Visual6percep:on

#Robo<cs

Sensors6and6aOuators Autonomous6decision6making

Expert6systems

Medicine Finance

Natural6programming

Gene:c6algorithms Ant6compu:ng

Cogni:ve6modeling

Cogni:ve6states Emo:ons

4 BBS#Tel#Aviv#2016

Logic#and#AI#

Many#diﬀerent#logics

Proposi<onal,#ﬁrstLorder,#modal#(epistemic,#temporal,#deon<c)#…

Many#techniques#for#represen<ng#knowledge#and#reasoning Deduc<on,#Induc<on,#Abduc<on#…

Many#languages#and#tools

PROLOG,#Constraint#programming

As#the#base#of#many#Expert#Systems

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5 BBS#Tel#Aviv#2016

PROLOG#(Kowalski#1973):#logic#programming

Example:#sum#of#two#integer#numbers

1. sum(0,X,X).

2. sum(X+1,Y,Z+1)

!

sum(X,Y,Z).

Meaning:#

1.#the#sum#of#0#and#X#is#X

2.#if#the#sum#of#X#and#Y#is#Z#then#the#sum#of#X+1#and#Y#is#Z+1

Many#possible#queries

Fact Rule

sum(2,4,Z).############Answer#Z#=#6 sum(2,X,5).# #########Answer#X#=#3#

6 BBS#Tel#Aviv#2016

Deduc<on#

E#(deduce)

#

parent(X,Y)#:L#mother(X,Y).

parent(X,Y)#:L#father(X,Y).

##

mother(mary,vinni).

mother(mary,andre).

father(carrey,vinni).

father(carry,andre).

parent(mary,vinni).

parent(mary,andre).

parent(carrey,vinni).

parent(carrey,andre).

U

→

B T

L

In#logic#programming

Deduc<on#allows#to#derive#consequences#of#the#assumed:

B#can#be#derived#from#A#if#B#is#a#logical#consequence#of#A##(A#|=#B) Given# the# truth# of# the# assump<on# A# follows# the# truth# of# the#

conclusion#B###

#

7 BBS#Tel#Aviv#2016

Induc<on#

parent(mary,vinni).

parent(mary,andre).

mother(mary,vinni).

mother(mary,andre).

father(carry,andre).

parent(X,Y)#:L#father(X,Y).##

U

E+

EL parent(mary,carrey).

→

B T#(induce)

#

∀e⁺#∈#E⁺:###B∪T#|=##e⁺####(T#è#completo)

∀e^L#∈#E^L:####B∪T#|=##e^L####(T#è#consistente)

Induc<ve# reasoning# allows# inferring# B# from# A# where# B# # does# not# follow#

necessarily#from#A

The# premises# are# viewed# as# supplying# strong# evidence# for# the# truth# of# the#

conclusion

In# some# case# deriva<on# of# general# principles# from# observa<ons:# # we# have#

seen#white#swans#we#derive#the#rule#“all#swans#are#white”

In#LP

8 BBS#Tel#Aviv#2016

Abduc<on

Abduc<on#allows#to#infer##A#an#explana<on#of#B

In#other#words,#the#precondi<on#A#is#inferred#(abduced)#from#

the#consequence#B.

Given#a#theory#T#and#an#observa<on#O#we#infer#(abduce)#and#

explan<on#E#Such#that

T#U#E#|#=#O

T#U#E#is#consistent#

(3)

9 BBS#Tel#Aviv#2016

Abduc<on#in#LP

E

parent(X,Y)#:L#father(X,Y).

##

mother(mary,vinni)#∨##father(mary,#vinni).

mother(mary,andre)#∨#father(mary,andre).

mother(carrey,vinni)#∨#father(carrey,vinni).

mother(carey,andre)#∨#father(carry,andre).

parent(mary,vinni).

parent(mary,andre).

U →

B#(abduce)

T

T∪B#|=##E T∪B#|=##IC

Spesso#si#usano#anche#“vincoli#di#integrità”#per#controllare#la#

generazione#di#ipotesi.#

E

parent(X,Y)#:L#father(X,Y). mother(mary,vinni).

mother(mary,andre).

father(carey,andre).

parent(mary,vinni).

parent(mary,andre).

U →

B#(abduce)

T

mother(X,Y)#→#female(X).

father(X,Y)###→#male(X).

female(mary).

male(carrey).

IC

T∪B#|=##E

10 BBS#Tel#Aviv#2016

Expert#systems#and#decision#support#systems#(1980#L)

Knowledge#based#systems#(expert#systems)

solve#problems#in#a#limited#domain#

have#performance#similar#to#an#expert#of#the#domain use#knowledge#representa<on#and#search#in#large#spaces build#dynamically#the#solu<on

search#and#solu<on#genera<on#guided#by#rules

.

Example#of#rule#based#system:#simple#diagnos<c#problem

OBIETTIVO: prescrivere una medicina adeguata per un determinato paziente in base ai

risultati di alcuni esami di laboratorio: prescribe(Drug).

BASE di CONOSCENZA Fatti:

gram(neg).

not(allergic(antb)).

Regole:

R1: gram (neg) → id (ecoli).

Se il risultato dell'esame è gram-negativo allora l'identità è enterium-coli R2: gram (pos) → id (strep).

Se il risultato dell'esame è gram-positivo allora l'identità è streptococco R3: id(strep) OR id(bact) → ind(pen).

Se l'identità è streptococco o bactero allora è bene indicare penicillina R4: ind(X) AND not (allergic(X)) → prescribe(X)

Se è bene indicare una certa medicina e il paziente non è allergico a tale medicina, allora si può prescrivere tale medicina al paziente

R5: id(ecoli) → ind(antb).

Se l'identità è enterium-coli allora è bene indicare antibiotici

Motore#di#Inferenza#o#Controllo#(backward)

Match R5 (X/antib)

Match R1

Act R1 Goal: prescribe(Drug)

prescribe(X) Match R4 (Drug/X)

ind(X) not allergic(X) Act R4 AND

Match R3 (X/pen) ind(pen) ind(antib) YES

Act R3 Act R3

id(strep)

id(bact) id(strep)

Match R2

gram(pos) Act R2

NO

Act R5 id(ecoli)

id(ecoli)

gram(neg) YES

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Example#of#working#session

Sys: È la coltura Gram positiva ? User: NO.

Sys: È la coltura Gram negativa?

User: SI.

Sys: Il paziente è allergico agli antibiotici ? User: NO.

Sys: Allora prescriverei una cura a base di antibiotici.

Sessione di spiegazione:

Sys: È la coltura Gram positiva ?

User: WHY? < perchè è importante determinare tale informazione?

Sys: Tale informazione è di aiuto per determinare se (R1) l'identità dell'organismo è streptococco.

User: WHY?

Sys: Tale informazione è di aiuto perchè per tale organismo è indicata la penicilina ( R3 ).

Sys: ... Allora prescriverei una cura a base di antibiotici.

User: HOW? <come puoi concludere tale responso?

Sys: Ho utilizzato la regola R4: ho prescritto antibiotici poichè sono indicati per tale infezione (prem1) e il paziente non è allergico (prem2).

User: HOW Premessa 1 ?

<come puoi concludere che sono indicati gli antibiotici?

Sys: Ho utilizzato la regola R5: sono indicati antibiotici poichè l'organismo che ha causato l'infezione è ecoli.

Some#recent#applica<ons###

(from#the#Expert#system#with#applica<ons#journal,#2017)

The# impact# of# microblogging# data# for# stock# market#

predic<on:# Using# Twiper# to# predict# returns,# vola<lity,#

trading#volume#and#survey#sen<ment#indices

Detec<on# of# idea# plagiarism# using# syntax–Seman<c# concept#

extrac<ons#with#gene<c#algorithm

A#compara<ve#study#on#base#classiﬁers#in#ensemble#methods#

for#credit#scoring

WordLsentence# coLranking# for# automa<c# extrac<ve# text#

summariza<on

….

Applica<ons#of#AI:#categories

“Normal”#human##ac<vi<es#(very#diﬃcult#for#machines)

Natural#Language#Processing#(NLP)#

Vision#### #########################################################################beper#subLsymbolic Movement##and#robo<cs

…

Formal#ac<vi<es Games

Mathema<cs#and#Logic

….

Specialized#ac<vi<es

Expert#and#decision#support#systems#

Reccomender#systems Diagnosis

Planning

…

}

Some#examples

Chess

Limited#number#of#states Explicit,#non#ambiguous#rules

In#a#sense#“easy”#(but#huge#search#space)

Natural#language#processing#(Watson) Ambiguous,#context#depended#

Congi<ve#states

Robo<cs#and#autonomous#systems In#a#physical#ambient

Dynamic,#realL<me In#part#non#symbolic

(5)

Natural#language#understanding#and#QA

Watson#(IBM)#won#at#Jeopardy,#2011#!#

Jeopardy:#Given#an#answer#must#ﬁnd#a#ques<on Language#understanding#+#Ques<on#Answering Uses#4#terabytes#of#data#(Encylopedia#etc.) Analyses#200#M#pages#of#content#in#3#second

Prac<cal#applica<ons#of#Watson

Financial# domain:# Bridgewater# Associates# (managing# 160# $# bn)# hired# the# chief#

developer#of#Watson#to#create#a#system#for#managing#daily#opera<ons.#Long#term#

goal#is#to#have#in#5#years#¾#of#the#managing#decision#done#by#so7ware

Health#domain:#Watson#Oncology#is#a#cogni<ve#compu<ng#system#deevloped#at#

Memorial#Sloan#Kepering#Cancer#Center##to#interpret#cancer#pa<ents’#clinical#

informa<on#and#iden<fy#individualized,#evidenceLbased#treatment#op<ons.

Personalized#tutoring##The#Teaching#Assistant#of#the#2016#Ar<ﬁcial#Intelligence#

course##at#Georgia##Tech#was#a#program#(based#on#IBM#Watson).#It#was#answering#

students#ques<ons#onLline#with#a#success#rate#of#97%.#

Weather#forecast#Watson#used##to#analyze#data#from#over#200000#sta<ons

Element Number

of cores

Time to answer one Jeopardy! question

Single core 1 2 hours

Single IBM Power 750 server 32 <4 min

Single rack (10 servers) 320 <30 seconds IBM Watson (90 servers) 2 880 <3 seconds

Memory:

20 TB 200 million pages

(~1 000 000 books)

~1 000 000 million lines of code 5 years development

(20 men)

John# Searle:# “Watson# Doesn't# Know# It# Won# on# 'Jeopardy!’# IBM# invented# an#

ingenious#program—not#a#computer#that#can#think.”

Noam#Chomsky:#“Watson#understands#nothing.#It’s#a#bigger#steamroller.##Actually,#

I#work#in#AI,#and#a#lot#of#what#is#done#impresses#me,#but#not#these#devices#to#sell#

computers.”

Not#crea<on#of#a#new#algorithm#but#ability#to#quickly#execute#hundreds#of#proven#

language#analysis#algorithms#simultaneously#to#ﬁnd#the#correct#answer.

^[12][

Both#hardware#and#so7ware#are#needed#for#eﬃciency#and#results:

10/01/17 14:16

Page 1 of 1 file:///Users/Mau1Nuovo/Downloads/DeepQA-1.svg

Question analysis

Query decomposition

Hypothesis generation

Soft filtering

Hypothesis and

evidence scoring Synthesis Final merging

and ranking

Hypothesis generation

Soft filtering

Hypothesis and evidence scoring Evidence

sources

Primary search Question

Answer and confidence Candidate

answer generation

Supporting evidence retrieval

Deep evidence

scoring Answer

sources

Trained models

“The#system#we#have#built#and#are#con<nuing#to#develop,#called#DeepQA,#is#a#

massively#parallel#probabilis<c#evidenceLbased#architecture.#For#the#Jeopardy#

Challenge,# we# use# more# than# 100# diﬀerent# techniques# for# analyzing# natural#

language,#iden<fying#sources,#ﬁnding#and#genera<ng#hypotheses,#ﬁnding#and#

scoring#evidence,#and#merging#and#ranking#hypotheses”.#

[1]#

[1]#Building6 Watson:6 An6 Overview6 of6 the6 DeepQA6 Project.# AI# Magazine# Fall,# 2010.# David#

Ferrucci,# Eric# Brown,# Jennifer# ChuLCarroll,# James# Fan,# David# Gondek,# Aditya# A.# Kalyanpur,#

Adam#Lally,#J.#William#Murdock,#Eric#Nyberg,#John#Prager,#Nico#Schlaefer,#and#Chris#Welty