CONTEXT and KNOWLEDGE

Summary Context Knowledge Human brain Human brain vs. computer Can computers be considered intelligent?

Positive examplesDeepBlue MYCIN

Negative examples Expressing knowledge through language

ContextDefinition

Several definitions

– Discourse that surrounds a language unit and helps to determine its interpretation

– The set of facts or circumstances that surround a situation or an event

Context Some context related properties Contexts increase inferential power Learning (new information) occurs in specific context Knowledge can be generalised from specific

contexts to more general ones Contexts themselves can be objects of inference Different contexts can be selected depending on

previous contexts Whether something acts as a context or not could

itself be context dependent

KnowledgeDefinition The act or state of knowing; clear

perception of fact, truth or duty; cognition The psychological result of perception of

learning and reasoning Knowledge is information that has been

pared, shaped, interpreted, selected and transformed (Ray Kurzweil) Facts alone do not constitute knowledge

KnowledgeHuman vs. Computer Human intelligence

– Remarkable ability of creating links between ideas

– Weak at storing information on which knowledge is based

The natural strengths of computers are roughly the opposite powerful allies of the human intellect

Human Knowledge Abstract concepts When we come in contact with a new concept we add new

links Knowledge structures are not affected by the failure of the

hardware (50000 neurons die each day in an adult brain, but our concepts and ideas do not necessary deteriorate)

We are capable of storing apparently contradictory ideas Unless a new idea is reinforced it will eventually die out Strong links between our emotions and our knowledge Our knowledge is closely tied to our pattern-recognition

capabilities We are able to change our minds change our internal

networks of knowledge

Computer Knowledge Propaedia A section of the 15th edition of Encyclopaedia Britannica

(1980) An ambitious attempt to organize all human knowledge in a

single hierarchy Allows multiple classifications Takes time to understand but it is successful in view of the

vast scope of the material it coversSuch data structures provide a formal methodology for representing a broad class of knowledge easily stored and manipulated by the computer

Human brain and knowledge

Human brain

Highly parallel early vision circuitsVisual cortex neuron clustersAuditory cortex circuitsThe hippocampusThe amygdala

Human Brain Human brain on the order of 100 billion neurons One neuron thousands of synaptic connections There is a speculation that certain long-term

memories are chemically coded in neuron cell bodies The capacity of each neuron 1000 bits the

brain has the capacity of 1014 bits If we assume an average redundancy factor of 104,

that gives us 1010 bits per concept 10 6 concepts per human brain

Human Brain

It has been estimated that a “master” of a particular domain of knowledge has mastered about 50000 concepts, which is about 5 percent of the total capacity, according to the above estimate

Human Brain vs. Computer

The human brain uses a type of circuitry that is very slow

For tasks as vision, language or motor control, the brain is more powerful than 1000 super computers

For certain tasks simple tasks such as multiplying digital numbers it is less powerful that the 4-bit microprocessor found in a ten dollar calculator

Computer Learning vs. Biological Learning

The brain is wired to learn in interaction with the world, re-programming themselves over time

Computers don’t learn easy by experience A human child

– Starts out listening to and understanding spoken language– Learns to speak– Learns written language

Computer – Starts with the ability to generate written languge– Learning to understand it– Speak with synthetic voices– Understand continuous human speech (recently)

Deep Blue

Its predecessor Deep Thought appeared at Carnegie Mellon University. In 1989 it was beaten by Kasparov in 41 moves

Project continued at IBM’s T.J. Watson Research centre

Improvements every year: now it has 30 Power Two Super Chip Processors

Is capable of 200 million positions / second (Kasparov of 3 positions / second)

Almost no use of psychology

Deep Blue

Its strenghts are the strenghts of a machine: it has a database of opening games played by grandmasters over the last 100 years

It does not think, it reacts Only one specific job It considers before deciding on a move 4

parameters: material, position (control of the centre), King safety and tempo (losing tempo= wasting time by indecision, and the opponent making productive moves)

MYCIN Created in mid 1970’s by E.H. Shortliffe at

Standford University Medical diagnosis tool (attempts to identify the

cause of infection) Suggests a course of medication It uses 500 rules Each rule has assigned a number its users

can assess the validity of it’s conclusion (WHY) Can recognise approximate 100 causes of

bacterial infection

MYCIN

Uses rules like:MYCIN Rule …IF …THEN …AUTHORS …JUSTIFICATION…LITERATURE…

MYCIN Fragment of a dialog between Mycin and a doctor

>> What is the patient’s name? John Doe >>Male or female? Male >>Age? 52 >>Let’s call the most recent positive culture C1 From what site was C1 taken? …… >>My recommendation is as follows: give gentamycin using a dose

of 119 mg…

Other intelligent programs in medicine: PUFF: a system for interpreting pulmonary

tests ONCOCIN: a system for the design of

oncology chemotherapy protocols CADUCEUS (former Internist): a system for

diagnosis within a broad domain of internal medicine; it contains over 100,000 associations between symptoms (70% of the relevant knowledge in the field)

Other domains

Teknowledge is creating a system for General Motors that will assist garage mechanics

ISA (Intelligent Scheduling Assistant): schedules manufacturing and shop floor activity

DENDRAL: embodied extensive knowledge of molecular structure analysis (Meta-DENDRAL)

SCI (Strategic Computing Initiative): several prototypes, among which is Vision System (will provide real-time analysis of imaging data from intelligent weapons and reconnaissance aircraft))

Expressing Knowledge through Language Language is the principal means by which we

share knowledge Language in both its auditory and written

forms is hierarchical with multiple levels To respond intelligently to human speech,

one need to know, among other things:– The structure of the speech sounds– The way speech is produced– The patterns of sound– The rules of word usage

Expressing Knowledge through Language Computers sentence-parsing systems

can do good jobs at analysing sentences that confuses humans:

“This is the cheese that the rat that the cat that the dog chased bit ate”

Expressing Knowledge through LanguageBut with other types of sentences it

has difficulties:“Time flies like an arrow”

or“Squad Helps Dog Bite Victim”

The difficulties appear when a word has several meanings or are used idiomatic expressions

Expressing Knowledge through Language Explanation to the first sentence:

For the computer this sentence it might mean:The time passes as quickly as an arrow passes,Or maybe it is a command telling us to time flies the same way that an arrow flies - Time flies like an arrow wouldOr it could be a command telling us to time only those flies that are similar to arrows - Time flies that are like an arrowOr perhaps it means that the type of flies known as time flies have a fondness for arrows - Time flies like (that is cherish) an arrow.

Expressing Knowledge through Language

The ambiguity of language is far grater than may appear.

At MIT Speech Lab, a researcher found a sentence published in a technical journal with over 1,000,000 syntactically correct interpretations!!!!!!!!

Expressing Knowledge through Language TRANSLATION:

one of the challenges in developing computerized translation system

Each pair of languages represents a different translation problem

Best solution known was given by a Dutch firm named DLT

Expressing Knowledge through Language Solution found by DLT:

– Developed translators for six languages to and from a standard root language (ESPERANTO)

– A translation from English to German would be accomplished in 2 steps: from English to Esperanto and from Esperanto to German

– Esperanto was selected because it is particularly good at representing concepts in an unambiguous way

– Translating among 6 different languages would ordinarily require 30 different translators, but with the DLT approach only 12 are required

R2D2

Robot in Star Wars Designed to operate in deep space, interfacing

with fighter craft and computer systems to augment the capabilities of ships and their pilots

Monitors flight performance, well-versed in star ship repair, a.s.o.

Converses in a dense electronic language (beeps, chirps, whistles)

Can understand most forms of human speech, but must have his own communication interpreted by other computers