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INTRODUCTION. AI - Artificial Intelligence What is AI? Problems with definition of AI Main difficulty: What is Intelligence ?. AI - Some Definitions. AI is the study of ideas which enable computers to do the things that make people seem intelligent (Winston’s book AI, 1st edition, 1979)

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introduction

INTRODUCTION

AI - Artificial Intelligence

What is AI?

Problems with definition of AI

Main difficulty: What is Intelligence?

ai some definitions
AI - Some Definitions
  • AI is the study of ideas which enable computers to do the things that make people seem intelligent (Winston’s book AI, 1st edition, 1979)
  • But, what is human intelligence?

Surely:

(1) ability to reason

(2) ability to learn (acquire and apply new

knowledge)

(3) ability to communicate ideas

(...) creativity, emotions, consciousness, ... ?

ai problems with definition
AI, problems with definition
  • Definition of consciousness = ?
  • Searl: Chinese room argument
  • One’s ability of competent conversation in Chinese enough to say that he really knows Chinese? Understands, feels Chinese?
  • Chinese room argument too strong? It practically makes AI impossible
  • One view: “Who cares?” (John Sowa)
winston s updated definition of ai
Winston’s updated definition of AI
  • AI is the study of the computations that make it possible to perceive, reason and act (Winston’s book on AI, 3rd edition, 1992)
goals of ai winston 1992
GOALS OF AI (Winston 1992)
  • Engineering goal:

Solve real-world problems using AI as an armamentarium of ideas about representing knowledge, using knowledge, and assembling systems

  • Scientific goal:

Determine which ideas about representing knowledge, using knowledge and building systems explain various sorts of intelligence

  • AI helps us to become more intelligent.
turing test
TURING TEST
  • When can we say that a computer is truly intelligent?
  • Alan Turing defined a test to decide whether a computer has achieved intelligence comparable to human:

An observer, after 30 min of conversation,

cannot distinguish intelligent computer from a

human

a definition of ai with reference to turing test
A definition of AI with reference to Turing test
  • AI is the enterprise of constructing

a physical symbol system that can reliably pass the Turing test (M. Ginsberg, Essential of Artificial Intelligence, Morgan Kaufmann 1993)

Reference to logic

strong vs weak ai
STRONG vs. WEAK AI
  • Mainly topic of philosophical discussion (Searl, Penrose, ...),

not of so much interest to AI practitioners

  • What is strong AI?
  • Ginsberg’s definition of AI expresses the spirit of strong AI by referencing logic
strong vs weak ai comments by donald michie
Strong vs. Weak AI, comments by Donald Michie
  • Spirit of strong AI: By sufficiency of “logic crunching” we can program computers to out-think humans.
  • Spirit of weak AI: Humans don’t think logically anyway; so why not try neural nets, ultra parallelism, or accept that mechanising intelligence is impossible.
strong vs weak ai comments by donald michie ctd
Strong vs. Weak AI, comments by Donald Michie, ctd.
  • Topics missed by “strong AI”:

“visual thinking”,

sub-cognitive mental skills,

explanation as confabulation

  • “... both sides of this debate may find that their artillery is being wasted on positions that are not so much untenable as abandoned”.
areas of ai
AREAS OF AI
  • Problem solving and search
  • Means-ends planning
  • Knowledge representation
  • Reasoning, inference
  • Knowledge engineering
  • Common sense reasoning
  • Qualitative reasoning, naive physics
  • Machine learning
  • Data mining, knowledge discovery in data bases
  • Neural networks
areas of ai cont
AREAS OF AI, cont.
  • Natural language understanding
  • Computer vision
  • Robotics
  • Evolutionary programming:

genetic algorithms

genetic programming

artificial life

  • Simulated annealing
example applications of ai
EXAMPLE APPLICATIONS OF AI
  • Planning and search: production planning, scheduling, resource allocation, logistics
  • Machine learning: medical diagnosis in various medical domains. Diagnostic accuracy better than physicians’.
  • Synthesis of new scientific theories from measured data: automated construction of genetic network theories from genetic experimental data
breast cancer recurrence
Breast Cancer Recurrence

Degree of Malig

< 3

>= 3

Tumor Size

Involved Nodes

< 15

>= 15

< 3

>= 3

Age

no rec 125

recurr 39

no rec 30

recurr 18

recurr 27

no_rec 10

>45

no rec 4

recurr 1

no rec 32

recurr 0

Tree induced by Assistant Professional

Interesting: Predictive accuracy of this tree better than medical specialists

predictive accuracy
PREDICTIVE ACCURACY
  • Accuracy : probability of correct classification of a randomly chosen new object
application of ai in genetics
APPLICATION OF AI IN GENETICS
  • GenePath, a system that helps biologists in functional genomics research
  • Collaboration between:

Ljubljana University, Faculty of Computer

and Info. Sc. (Zupan, Demšar, Juvan, Curk, Bratko)

Baylor College of Medicine, Houston, Texas

(Kuspa, Shaulsky, Halter)

functional genomics
FUNCTIONAL GENOMICS
  • Determining gene function through genetic experiments:

- What is the role of each gene in a genome?

- How do the genes interact?

- How do they influence the phenotype?

  • One way of modelling these relations: genetic networks
dictyostelium
DICTYOSTELIUM
  • A simple, but very interesting organism
  • A social amoeba: Can exist as single cell or multi cell organism
  • Has been attracting biologists for long
  • A topic of current research in functional genomics
  • Also used in this study
dictyostelium time to move
Dictyostelium: Time to Move

When food is cleaned, Dictyostelium get together and converge in mound.

Development: the mounds stretch into slugs, which topple over and crawl away.

dictyostelium aggregation
Dictyostelium: Aggregation

Experimental Data (7 genes)

Prior Knowledge

pkaC

pkaR

pkaR

acaA

resulting models for dictyostelium
Resulting Models for Dictyostelium

yakA

pufA

pkaC

aggregation

pkaR

regA

yakA

acaA

regA

pufA

pkaC

aggregation

acaA

pkaR

yakA

pufA

pkaC

aggregation

regA

acaA

pkaR

yakA

pufA

acaA

pkaR

pkaC

aggregation

regA

example applications of ai ctd
EXAMPLE APPLICATIONS OF AI, ctd.
  • Machine learning: synthesis of new knowledge from measured data - ecological modelling (Lagoon of Venice, Lake Glumsoe, Lake Bled)
  • Learning to predict river water quality from organisms living in river
  • Learning to predict deer population in a forest
  • Predicting biodegradability of chemicals
example applications learning to predict weather
EXAMPLE APPLICATIONS:Learning to predict weather
  • E.g. learn to predict temperature at noon next day
  • Students project 2001/2 (Žabkar, Vrabec, Indihar), data from Environment Agency
  • Take measured weather data and Aladin’s predictions, improve on Aladin’s prediction
prediction of ozone concentration
PREDICTION OF OZONE CONCENTRATION
  • Learn with ML to predict ozone concentration on the basis of measured air and weather parameters (Ljubljana,Nova Gorica; Zabkar et al. 2004)
  • Meteorological Agency required to issue these forecasts by European regulations
savinja napovedovanje poplav
SAVINJA NAPOVEDOVANJE POPLAV
  • Hudournik – težko napovedovati pretok, še posebno ekstremne vrednosti, ki pomenijo poplave => cilj: izboljšati napovedni model
  • Trenutno je v uporabi numerični model HBV, ki ne daje dobrih rezultatov (hidrologi: pomemben vhod so napovedi padavin, ki pa so slabe!)
  • HBV: aplikacija splošnega modela na konkretno domeno
  • Naš pristop: Uporaba podatkov določene domene za induciranje specifičnega modela
example applications of ai ctd27
EXAMPLE APPLICATIONS OF AI, ctd.
  • Machine learning in mechanical engineering: prediction of surface roughness from acoustic data in machining
  • Machine learning in textile industry: prediction of mechanical properties of thread from material mixture used in weaving
  • Learning to predict aesthetic appearance of clothes
  • Behavioural cloning: Reconstructing sub-cognitive skills from behaviour data
  • Data mining in marketing: determining target population for advertisement
v izualizacija podatkov v sistemu za strojno u enje orange
Vizualizacija podatkov v sistemu za strojno učenje ORANGE
  • Nova vizualizacijska metodo, imenovana VizRank, iz podatkov avtomatsko poišče zanimive točkovne geometrijske vizualizacije.
  • Vizrank za ocenjevanje vizualizacij in hevristično preiskovanje prostora možnih vizualizacij uporablja metode strojnega učenja.
  • Aplikacije metode VizRank na področju bioinformatike (članek v reviji Bioinformatics, IF=6.7, januar 2005) ter analize genskih izrazov rakastih tkiv (v 2005 dva prispevka s tega področja na odličnih konferencah AIME in KDD, članek za revijo je v pripravi).
genetski algoritem za optimizacijo procesnih parametrov
Genetski algoritem za optimizacijo procesnih parametrov
  • Optimiranje parametrov v ulivanju jekla. Za več primerov jekel v železarnah Acroni in Ruukki Steel (Finska) smo izboljšali nastavitve procesnih parametrov, predvsem pretokov hladil.
ozon napovedovanje koncentracije
OZON NAPOVEDOVANJE KONCENTRACIJE
  • Evropski predpisi: obvezno napovedovanje koncentracije ozona
  • Napovedovanje koncentracije ozona v LJ in NG (Q2 učenje) in model za razlago procesov nastajanja O3.
  • Izhodišče:
    • zapleteni meteorološki in kemijski procesi pri nastajanju ozona
    • ni napovednega modela
    • zelo pomembni lokalni dejavniki
napovedni model
Napovedni model
  • Atributi (napovedi modela ALADIN in meritve ekoloških parametrov):

MAXNO (max. konc. dušikovega oksida v zadnjem dnevu), Ssum015LJ (vsota napovedi sončnega sevanja do 15h v LJ), Tavg915LJ (povprečje napovedi temperature med 9h in 15h, v LJ)

nekateri e vropski projekti v laboratoriu za umetno inteligenco fri
Nekateri evropski projekti v Laboratoriu za umetno inteligenco FRI
  • ASPIC, Argumentation Services Platform with Integrated Components
  • XMEDIA, Knowledge Sharing and Reuse across Media
  • XPERO, Learning by Experimentation
xmedia consortium
XMEDIA Consortium
  • University of Sheffield, Shef, Prof. Fabio Ciravegna, Dr. Mark Stevenson, Dr. Daniela Petrelli
  • 2Centre for Research and Technology, Hellas, CERTH Dr. Yannis Avrithis
  • CognIT a.s CognIT Dr. Robert Engels
  • Instituto Trentino Di Cultura ITC-Irst Dott. Alberto Lavelli
  • Universitaet Koblenz-Landau KOB Prof. Steffen Staab
  • Laboratoire Bordelais Recherche en Informatique, Labri, Prof. Jenny Benois-Pineau
  • Ontoprise GmbH Intelligente, Losungen fur das Wissensmanagement, Ontoprise Prof. Juergen Angele
  • Open University, OU, Prof. Enrico Motta
  • Quinary Spa, Quinary, Dott. Luca Gilardoni
  • Rolls Royce plc, RR, Dr. Ian Jennions
  • University of Freiburg, UFrei, Prof. Lars Schmidt-Thieme
  • Universitat Karlsruhe, UKarl, Prof. Rudi Studer, Mr. Philip Cimiano
  • Faculty of Computer and Information Science, University of Ljubljana, UL Prof. Ivan Bratko
  • Centro Ricerche Fiat, Societa Consortile per Azior, C.R.F., Fiat, Ing. Marialuisa Sanseverino
  • Solcara Limited Solcara, Mr. Ray Jackson
xpero robot gaining insights
XPERO: Robot gaining “insights”
  • A definition of insight in the spirit of XPERO:

an insight is a new piece of knowledge that makes it possible to simplify the current agent’s theory about its environment

  • Examples of insights are discoveries of notions like:
    • absolute coordinate system,
    • arithmetic operations,
    • notion of gravity,
    • notion of support between objects
    • ...