Artificial Intelligenceradar.zhaw.ch/~rege/tecd_fs20/V13_AI.pdf · Strong AI is artificial intelligence that matches or exceeds human intelligence — the intelligence of a machine

Artificial Intelligence

■ Perception

■ What is easy and what is hard

■ History

■ Definition

■ Approach and Fields of AI

■ Limits

Dr. C. Lee Giles

Special thanks Prof Lee Giles (PSU) and Michael Scherger (Kent) for Selection of Slides

2 von 51School of Engineering © K. Rege, ZHAW

Perception of AI


Artificial Intelligence in the Movies

■ Mostly Distopic


Face detection Formal verification

Mars Robot Games - Chess Industry - Robots

Artificial Intelligence in Real Life

■ A young science (50 years old - young!)■ Exciting and dynamic field, lots of uncharted territory left■ Impressive success stories■ “Intelligent” in specialized domains■ Many application areas


Search Engines

ScienceSpectrum -> Molecule

Medicine/DiagnosisImage Analysis

Appliances

TranslationsUnderstanding

Surveillance"Social Scoring"

Why the Interest in AI?


Easy and Hard


What’s easy and what’s hard?

■ It’s been easier to mechanize many of the high level cognitive tasks we usually

associate with “intelligence” in people■ e. g., symbolic integration, proving theorems, playing chess, some aspect of medical diagnosis, etc.

■ It’s been very hard to mechanize tasks that animals can do easily■ walking around without running into things■ catching prey and avoiding predators■ interpreting complex sensory information (visual, aural, …)■ modeling the internal states of other animals from their behavior■ working as a team (ants, bees)

■ Is there a fundamental difference between the two categories?

■ Why are some complex problems not subjects of AI?■ e.g., solving differential equations, database operations


… What’s easy and what’s hard?

■ Maple:1980 (online: Wolfram Alpha)

Symbolic Calculation System: Solves

complex math problems ->

Symbolic Calculus

■ Google 2018: “We use machine learning tosave you time by selecting the best photos

of your four-legged friend and laying them

out in a photo book,”LP21 (neu):

Haustierunterscheidungskompetenz


Higher Cognitive Tasks Symbolic

Mathematics Physics

Geometry Chemistry


Self Driving Car


History of AI


History of AI

■ AI has roots in a number of scientific disciplines

■ computer science and engineering (hardware and software)

■ philosophy (rules of reasoning)

■ mathematics (logic, algorithms, optimization)

■ cognitive science and psychology (modeling high level human/animal thinking)

■ neural science (model low level human/animal brain activity)

■ linguistics

■ The birth of AI (1943 – 1956)

■ McCulloch and Pitts (1943): simplified mathematical model of neurons (resting/firing states) can realize all

propositional logic primitives (can compute all Turing computable functions)

■ Alan Turing: Turing machine and Turing test (1950)

■ Claude Shannon: information theory; possibility of chess playing computers

■ Boole, Aristotle, Euclid (logic, syllogisms)


History of AI

■ Early enthusiasm (1952 – 1969)■ 1956 Dartmouth conference: the Term AI is created

■ John McCarthy (Lisp);■ Marvin Minsky (first neural network machine);

■ Emphasis on intelligent general problem solving■ Lisp (AI programming language);

■ Resolution by John Robinson (basis for automatic theorem proving);■ heuristic search (A*, AO*, game tree search)

■ Emphasis on knowledge (1966 – 1974)■ domain specific knowledge is the key to overcome existing difficulties■ knowledge representation (KR) paradigms■ declarative vs. procedural representation■ Expert Systems


History of AI

■ Knowledge-based systems (1969 – 1999) - Expert Systems■ DENDRAL: the first knowledge intensive system (determining 3D structures of complex chemical

compounds)■ MYCIN: first rule-based expert system (containing 450 rules for diagnosing blood infectious diseases)

■ EMYCIN: an ES shell■ PROSPECTOR: first knowledge-based system that made significant profit (geological ES for mineral

deposits)

■ AI became an Industry (1980 – 1989)■ Wide applications in various domains■ commercially available tools■ 5th Generation Project in Japan - FAILED■ AI winter

■ Current trends (2000 – present)■ more realistic goals■ more practical (application oriented)■ distributed AI and intelligent software agents■ resurgence of natural computation - neural networks and emergence of genetic algorithms – many

applications■ dominance of machine learning and neural networls

Rege was thereRege was there


Categorization of AI


What is Artificial Intelligence? (again)

■ Systems that think like humans

■ Cognitive Modeling Approach

■ “The automation of activities that we associate with

human thinking...”

■ Systems that act like humans

■ Turing Test Approach

■ “The art of creating machines that perform functions

that require intelligence when performed by people”

■ Systems that think rationally

■ “Laws of Thought” approach

■ “The study of mental faculties

through the use of computational

models”

■ Systems that act rationally

■ Rational Agent Approach

■ “The branch of CS that is

concerned with the automation

of intelligent behavior”

inside viewinside view

outside view, isperceived as

outside view, isperceived as


Acting Humanly

■ Formulated by Alain Turing (1950)

■ Turing Test■ Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes

■ Anticipated all major arguments against AI in following 50 years

■ Suggested major components of AI: knowledge, reasoning, language understanding, learning

■ Problem!■ The turning test is not reproducible, constructive, or amenable to mathematical analysis


… Acting Humanly The Turing Test

■ A machine can be described as a thinking

machine if it passes the

■ Turing Test (1950). i.e. If a human agent is

engaged in two isolated dialogues(connected by teletype say); one with a

computer, and the other with another

human and the human agent cannot reliablyidentify which dialogue is with the computer.


Thinking Humanly

■ 1960’s cognitive revolution

■ Requires scientific theories of internal activities of the brain■ What level of abstraction? “Knowledge” or “Circuits”■ How to validate?

■ Predicting and testing behavior of human subjects (top-down)■ Direct identification from neurological data (bottom-up)

■ Cognitive Science and Cognitive Neuroscience■ Now distinct from AI


… Thinking Humanly Cognitive Science

■ The interdisciplinary, scientific study of the mind and its processes. It examines the

nature, the tasks, and the functions of cognition in a broad sense.

■ Cognitive scientists study intelligence and behavior, with a focus on how nervoussystems represent, process, and transform information.

Ludwig Wittgenstein: TheLimits of Thought

Ludwig Wittgenstein: TheLimits of Thought

Noam Chomsky: Limits ofLanguage & Mind

Noam Chomsky: Limits ofLanguage & Mind


Thinking and Acting Rationally

Thinking

■ Aristotle: What are correct arguments / thought processes?

■ Logic notation and rules for derivation for thoughts

■ Problems:

■ Not all intelligent behavior is mediated by logical deliberation

■ What is the purpose of thinking? What thoughts should I have?

Acting

■ Rational behavior■ Doing the right thing

■ What is the “right thing”■ That which is expected to maximize goal achievement, given available information

■ We do many (“right”) things without thinking■ Thinking should be in the service of rational action


Strong vs Weak AI

■ Strong AI is artificial intelligence that matches or exceeds human intelligence — theintelligence of a machine that can successfully perform any intellectual task that ahuman being can.

■ The stated goal of many artificial intelligence research and an important topic forscience fiction writers and futurists.

■ Strong AI is also referred to as "artificial general intelligence" or as the abilityto perform "general intelligent action".

■ Including: consciousness, self-awareness, sentience, sapience.

■ Weak AI is an artificial intelligence system which is not intended to match or exceedthe capabilities of human beings.

■ Also known as applied AI or narrow AI.

■ Weak AI only supercedeshuman skills in a narrow field, e.g. calculus


What is the scientific method hypothesis behind AI?

Other possible AI definitions

■ AI is a collection of hard problems which can be solved by humans and other living

things, but for which we don’t have good algorithms for solving.

■ understanding spoken natural language, medical diagnosis, circuit design, learning, self-adaptation,reasoning, chess playing, proving math theories, etc.

■ Some problems used to be thought of as AI but are now considered not■ e. g., compiling Fortran in 1955, symbolic mathematics in 1965, pattern recognition in 1970, what for the

future?


One Working Pragmatic Definition of AI

Artificial intelligence is the study of how to make computers do things thatpeople are currently better at it


ResearchFields in AI


Research Fields in AI

■ Heuristic Search■ Computer Vision■ Games: Adversarial Search■ Fuzzy Logic■ Natural Language Processing■ Knowledge Representation■ Planning■ Learning


Heuristic Search - Clustering

■ Very large search space - Big Data■ Large databases

■ Image sequences

■ Heuristics■ Rule base Heuristics

■ “Rules of thumb”

■ Very fast

■ Clustering - k-means■ System automatically detects "clusters" of correlated data

■ but correlation doesn't mean causality

■ Searching Citizens to adjust their Social Scoring (currently only in China)


Computer Vision

■ Computationally taxing■ Millions of bytes of data per frame■ Thirty frames per second

■ Computers are scalar / Images aremultidimensional

■ Image Enhancement vs. Image

Understanding

■ Can you find Wang Lee that crosses street

not in a proper way?


Wahrnehmung (Kognition) des Menschen

■ Der Mensch nimmt etwa so war, dass es zu

seinem internen Modell "passt".

■ In menschlicher Evolution

wichtig, da oft nurunvollständige

Information


… Computer Vision

■ Computer only recognizes parts■ https://cloud.google.com/vision/


Game Play: Adversarial Search (D: "feindlich")

■ Game theory...■ Two player, zero sum – checkers, chess, etc.

■ Minimax■ My side is MAX■ Opponent is MIN

■ Alpha-Beta■ Evaluation function...”how good is board”■ Not reliable...play game (look ahead) as deep as possible and use minimax.■ Select “best” backed up value.

■ Where will Al-Qaeda strike next? Deep Blue beats Kasparov1996

Deep Blue beats Kasparov1996


Planning: Examples

■ Robotics■ If a robot enters a room and sits down, what is the

“route”.■ A robot should clean a room (with obstacles)

■ Closed world

■ Rule based systems

■ Simple Blocks world

Table

Chair


… Planning

■ Old experiment to check intelligence ofanimals

■ Pickup(x)

■ Ontable(x), clear(x), handempty(),

■ Holding(x)

■ Putdown(x)

■ Holding(x)

■ Ontable(x), clear(x), handempty()

■ Stack(x, y)

■ Holding(x), clear(y)

■ Handempty(), on(x, y), clear(x)

■ Unstack(x, y)

■ Handempty(), clear(x), on(x, y)

■ Holding(x), clear(x)

A

C

B

RobotHand

B

A

C

Clear(B) On(C, A) OnTable(A)

Clear(C) Handempty OnTable(B)

Goal: [On(B, C) ^ On(A, B)]


Approaches toKnowledge

Representation


Knowledge Representation

■ In AI there are 2 fundamental approaches to address the problems

■ Subsymbolic: low level of abstraction

■ Symbolic: high level of abstraction


… Knowledge Representation: Symbolic

■ Logic programming is a type of programming paradigm which is largely based on formal

logic

■ Implications: read declaratively as logical implications:

if B1 and … and Bn -> H

mortal(X) :- human(X).human(socrates).

mortal(X) :- human(X).human(socrates).

> ?- mortal(socrates).true.

> ?- mortal(socrates).true.

rule base inProlog

rule base inProlog

queryquery


… Knowledge Representation: Subsymbolic

■ Basic (boolean) logic is binary■ 0 or 1, true or false, black or white, on or off, etc...

■ Ternary or Fuzzy Logic allows for intermediate values

■ This allows for real world representation of “shades”

Appetite

Light Moderate Heavy

0

1

Calories Eaten Per Day

1000 2000 3000

LinguisticVariable

LinguisticValues


… Knowledge Representation: Subsymbolic

■ Inspired by Human Brain■ Processing and memorization is implemented by

changes in the permeability of synapses

■ Information is not stored locally but distributed in thewhole network

■ About 86 Mia cells

■ Human brain can/must cope with about 50'000 cell lossesper day

■ Artificial Neural Network: ANN

■ Simplified Version of Neuron■ a set of input values are summed up

■ adjusted with an individual Weight■ if a certain threshold is reached

■ output becomes 1

■ There are input,output and hidden layers■ many hidden layers -> deep neural networks

outputoutputinputinput

hiddenhidden

Adjusted permeability:Information is stored here

Adjusted permeability:Information is stored here

Adjusted weights:Information is stored here

Adjusted weights:Information is stored here


Learning

■ Artificial Neural Networks (ANN)

Inpu

t

Out

put

Inpu

t

Out

put

Untrained ANNAll wi are the same

Trained ANNwi are adjustedBackpropagation

wi

wi


ComputerLinguistik


Computational Linguistics

■ Statistical or rule-based modeling of natural language from a computational perspective,

as well as the study of appropriate computational approaches to linguistic questions.

■ Rule-based■ Developmental

■ Language is a cognitive skill which develops throughout the life of an individual.■ Structural

■ based on large linguistic corpora, or samples■ to build models and gain a better understanding of the underlying structures

■ Statistical■ based on large linguistic corpora to train a system (ANN)

Applications

■ Speech recognition software■ Such as Apple's Siri feature, Spell- and Grammar check tools

■ Speech Synthesis Programshttps://www.grammarly.com/grammar-check


Knowledge Representation for NLP

■ Graph Based■ Semantic Networks

■ Frames

■ Predicate Logic■ On(table, lamp)

■ In(corner, table)

■ Near(table, dog)

■ Prolog


Natural Language Processing

S1S1 S2S2 S3S3“The big grey dog”

Net for Basic Noun Group

determiner noun

adjective

S1S1 S2S2 S3S3“by the table in the corner”

Net for Prepositional Group

preposition NOUNG

S1S1 S2S2 S3S3“The big grey dog by thetable in the corner”

Net for Basic Noun Group

determiner noun

adjectivePREPG


Natural Language Processing

■ Speech recognition vs. natural language processing■ NLP is after the words are recognized

■ Ninety/Ten Rule■ Can do 90% of the translation with 10% time, but 10% work takes 90% time

■ Easy for restricted domains■ Dilation

■ Automatic translation

■ Google Translate

SES hat zum Ziel, für die europäische Aviatik einenlegislativen Rahmen zu bilden, der es erlaubt, einleistungsstärkeres ATM in Europa zu entwickeln, um derstetig wachsenden Nachfrage nach Kapazität nachzukommen.

The goal of SES is to create a legislative framework forEuropean aviation that allows for a more powerful ATM inEurope to evolve to the ever-growing demandto meet capacity.

Ziel von SES ist es, einen Rechtsrahmen für die europäischeLuftfahrt zu schaffen, der einen leistungsfähigerenGeldautomaten ermöglicht in Europa auf die ständigwachsende Nachfrage zu entwickeln Kapazität erfüllen.

E

D

D

Air Traffic ManagementAir Traffic Management

Automatic Teller MachineAutomatic Teller Machine


Questions?

The Singularity


A Bit of Humor


Immanuel Kant's Metaphysik

■ Empirismus: aus Beschrieb der Erfahrung/Natur

-> allgemeine Regeln : "Erfahrungswissenschaft"Induktionsprinzip

■ Rationalismus: logisches Regelwerk

-> Erfahrungen/Natur erklären : "Logik","Mathematik"

■ Deduktionsprinzip

■ bis 1791 "Kampf" der beiden philosophischen Richtungen

■ Kopernikanische Wende der Philosophie: "Kritik der reinen Vernunft"

Vernunft/Denken bestimmt die Wahrnehmung/Realität


U

rtei

lsfu

nktio

nen

Kant's Kategorien des Denkens

■ Denken/Urteilen lässt sich in 12 grundlegende Kategorien unterteilen

(a priori = unabhängig von Erfahrung, analytisch = nicht abgeleitet)


Kant's Mündigkeitsbegriff

„Aufklärung ist der Ausgang des Menschen aus seiner selbst verschuldeten

Unmündigkeit. Unmündigkeit ist das Unvermögen, sich seines Verstandes ohne Leitungeines anderen zu bedienen. Selbstverschuldet ist diese Unmündigkeit, wenn die

Ursache derselben nicht am Mangel des Verstandes, sondern der Entschließung und

des Mutes liegt, sich seiner ohne Leitung eines anderen zu bedienen. ‚Sapere aude!Habe Mut, dich deines eigenen Verstandes zu bedienen!‘ ist also der Wahlspruch der

Aufklärung.“

Documents

Artificial Intelligenceradar.zhaw.ch/~rege/tecd_fs20/V13_AI.pdf · Strong AI is artificial intelligence that matches or exceeds human intelligence — the intelligence of a machine