CONTEXT and KNOWLEDGE Irina Codreanu and Monica Gavrila Students at Bucharest University

1. CONTEXT and KNOWLEDGE Irina Codreanu and Monica Gavrila Students at Bucharest University Socrates Students at Hamburg University

2. Summary • Context • Knowledge • Human brain • Human brain vs. computer • Can computers be considered intelligent? • Positive examples • DeepBlue • MYCIN • Negative examples • Expressing knowledge through language

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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 covers Such data structures provide a formal methodology for representing a broad class of knowledge  easily stored and manipulated by the computer

9. Human brain and knowledge • Human brain • Highly parallel early vision circuits • Visual cortex neuron clusters • Auditory cortex circuits • The hippocampus • The amygdala

10. 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

11. 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

12. 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

13. 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)

14. 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

15. 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)

16. 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

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

18. 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…

19. 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)

20. 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))

21. 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

22. Expressing Knowledge through Language • Computers sentence-parsing systems can do good jobs at analysing sentences that confuses humans: “This is the cheese that the ratthat the cat that the dog chased bitate”

23. Expressing Knowledge through Language • But 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

24. 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 would Or it could be a command telling us to time only those flies that are similar to arrows - Time flies that are like an arrow Or 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.

25. 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!!!!!!!!

26. 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

27. 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

28. 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