Understanding Common Sense in Software Development

1. CYC: Software that partially understands • The impact of that on the AIM dream Computers versus Common Sense Dr. Douglas B. Lenat , 7718 Wood Hollow Drive, Austin, TX 78731 Email: Lenat@cyc.comPhone: 512-342-4001

2. ELIZA (DOCTOR) 1965 Joe Weizenbaum, MIT • Carl Rogers advocated reflection Patient: “I swear a lot.” “How do you feel about the fact that you swear a lot?” • Counted a lot on anthropomorphism • Count on patient-to-doctor respect/awe/… • Patient: “My dog’s mother died recently.” • “Tell me more about your mother.” Veneer of intelligence, not intelligence

3. ALICE (40 years post-Eliza) • JUDGE11[9:26:0]I WILL ASK YOU SOME SIMPLE QUESTIONS AND GIVE SIMPLE COMMANDS • PROGRAM[9:26:0]Do you think your plan will succeed? • JUDGE11[9:26:16]WHAT IS THE COLOR OF A BLUE CAR • PROGRAM[9:26:16]WHAT COLOR IS a blue car • JUDGE11[9:26:35]WHERE IS SUE’S NOSE WHEN SUE IS IN HER HOUSE • PROGRAM[9:26:35]Where it belongs. • Try searching the Web.

6. 2009: Software is still Brittle “Is the Space Needle taller than the Eiffel Tower?” “How old was Martin van Buren when he was elected President of the U.S.?”

7. Natural Language Understanding requires having lots of knowledge • The pen is in the box. The box is in the pen. 2. The police watched the demonstrators because they feared violence. The police watched the demonstrators because becausethey advocated violence. 3. Mary and Sue are sisters. Mary and Sue are mothers. 4. Every American has a mother. Every American has a president. 5. John saw his brother skiing on TV. The fool didn’t have a coat on! John saw his brother skiing on TV. The fool didn’t recognize him!

8. 7. “…include all the re-do CABG procedures utilizing ITA and SVG in 1991”. “And” usually does mean “and”. But in this query, “and” really must mean “or”. Medical knowledge, not grammar, disambiguates this: a single CABG will not have both an ITA and a SVG. 8. “…that the tumor cells are stopping dividing or dying…” Do they mean “stopping dividing or stopping dying”? Of course not, but in 16 of 30 randomly selected syntactically similar constructions from www.clinicaltrials.gov, the coordination (i.e., the wider scope of the modifier, in this case the word “stopping”) was the intended meaning. In each case, only one choice “makes sense” (is consistent with medical knowledge and common sense). 9.“Adult patients who underwent MAZE III with or without Mitral Valve Repair or Replacements.” Is the second half of that query just a waste of space? Discourse pragmatics says no, the physician must have had some reason for saying that. Medical knowledge provides a plausible interpretation: “Adult patients who underwent MAZE III with no concomitant procedures other than Mitral Valve Repair or Replacements”

9. Okay, so let’s tell the computer the same sorts of things that human beings know about cars, and colors, heights, movies, time, driving to a place, etc. all the other stuff that everybody knows. The basic idea: Get the computer to understand, not just store, information. Then it can reason to answer your queries. 2 July 2005

10. MicrowaveOven is a type of Kitchen-Appliance Dishwasher is a type of Kitchen-Appliance The basic idea: Get the computer to understand, not just store, information. Then it can reason to answer your queries. 2 July 2005

11. You can’t useX if italorxes Y but lacks any Y Rthagide-disjaks is a type of Kitchen-Appliance Gracinimumples is a type of Kitchen-Appliance Rthagide-disjaksalorxesVorawnistz. GracinimumplesalorxesVorawnistz and Buzqa. Buzqa is a Thwarn and supplied through Epluns. 2 July 2005

12. etc. all the other stuff that everybody knows. Eventually, after writing millions of these rules, the system knows as much about pipes, liquids, water, electricity, microwave ovens, dishwashers, cars, colors, movies, heights, etc. as you and I do. Ultimately, there is just 1 interpretation of that model, and it corresponds to the real world. Long before that, incrementally, the system gains competence and trustworthiness The basic idea: Get the computer to understand, not just store, information. Then it can reason to answer your queries. 2 July 2005

13. Millions of facts, rules of thumb, etc. that capture human common sense about our everyday world Cyc is… • The typical bird has 1 beak, 1 heart, lots of feathers,… • Hearts are internal organs; feathers are external protrusions • Most vehicles are steered by an awake, sane, adult,… human • Tangible objects can’t be in 2 (disjoint) places at once • Badly injuring a child is much worse than killing a dog • Causes temporally precede (i.e., start before) their effects • A stabbing requires 2 cotemporal and proximate actors • etc.

14. Millions of facts, rules of thumb, etc. that capture human common sense about our everyday world Penitentiary EnglishWord-Plume WritingPen EnglishWord-Pen BirdFeather FrenchWord-Plume Authoring … Cyc is… • Each of these represented in formal logic • Info. about a set of hundreds of thousands of terms • Language-independent ChineseWordForWritingPen

15. Millions of facts, rules of thumb, etc. that capture human common sense about our everyday world • Each of these represented in formal logic • Info. about a set of hundreds of thousands of terms • An inference engine that produces the same sorts of inferences from those that people would. • Interfaces so the system can communicate with people, data bases, spreadsheets, websites, etc. Cyc is…

16. What Needs to be Shared? • bits/bytes/streams/network… • alphabet, special characters,… • words, morphological variants,… • syntactic meta-level markups (HTML) • semantic meta-level markups (SGML, XML) • content (logical representation of doc/page/...) • context (common sense, recent utterances, and n dimensions of metadata: time, space, level of granularity, the source’s purpose, etc.) Sem. Web

17. (ForAll ?P (ForAll ?C        (implies (and           (isa ?P Person)            (children ?P ?C))        (loves ?P ?C)))) How formalized knowledge helps search When you become happy, you smile. You become happy when someone you love accomplishes a milestone. Taking one’s first step is a milestone. Parents love their children. • Query: “Someone smiling” find information by inference (+KB) • Caption: “A man helping his daughter take her first step” .

18. How formalized knowledge helps search Query: “Show me pictures of strong and adventurous people” Caption: “A man climbing a rock face” find information by inference (+KB)

19. How formalized knowledge helps search Query: “Government buildings damaged in terrorist events in Beirut between 1990 and 2001” Document: “1993 pipe bombing of France’s embassy in Lebanon.” Text Document find information by inference (+KB)

20. How can our programs be intelligent, not merely have the veneer of it? • ANSWER: By having a large corpus of knowledge, spanning the gamut from specific domain-dependent all the way up to general common sense. • The computer needs to be able to apply the knowledge, not just store some English gloss • Represent it formally (predicate calculus), and apply logic • Represent it numerically, and apply mathematics/statistics • And after all that: Be compelling to the human deciding

21. One Good Explanation is worth 20 points of IQ • Magic tricks • “How do they do that?!”  “How was I ever fooled by that?!” • Efficacy of punishment vs reward • “Punishment is more effective, and the statistics back me up” • Clinical decision-making (by doctors and by patients) • “Because 0.814” versus “Because < plausible causal rationale >” • Organ donation in European countries: • Why is it so often 15%/85% or 85%/15% ? [Answer: Because when you apply for a drivers license in some countries, you have to check a box to “opt in”; in others, you have to check a box to “opt out”; and in the U.S. and most European countries at least, 85% of the people don’t know what they should do, even though it’s an emotional, serious choice, and end up just leaving it unchecked.] • And after all that: Be compelling to the human deciding

22. Reflection Framing Effect Philadelphia is preparing for a Legionaire’s Disease outbreak expected to kill 600 people today. Two alternative programs to combat the disease have been proposed. The consequences of each program are as follows: = = If Program A is adopted, 200 people will be saved. (72%) If Program B is adopted, there is a 1/3 chance that all 600 will be saved, and a 2/3 chance that no lives will be saved. (28%) If Program A’ is adopted, 400 people will die. (22%) If Program B ’is adopted, there is a 2/3 chance that 600 will die, and a 1/3 chance that no one will die. (78%) For more information, see:Kahneman, D. and Tversky, A. (1984). Choices, values, and frames. American Psychologist, 39, 341-350.

23. Conjunction Fallacy A health survey was conducted in a representative sample of adult males in Chicago of all ages and occupations. Mr. F was included in the sample. He was selected by random chance from the list of participants. Please rank the following statements in terms of which is most likely to be true of Mr. F. (1=more likely to be true, 6=least likely) ____ Mr. F smokes more than 1 cigarette per day on average. ____ Mr. F has had one or more heart attacks. A ____ Mr. F had a flu shot this year. A and B ____ Mr. F eats red meat at least once per week. ____ Mr. F has had one or more heart attacks and he is over 55 years old. ____ Mr. F never flosses his teeth. 58% rated “A and B” more likely than A For more information, see:Tversky, A. and Kahneman, D. (1983). Extensional vs. intui-tive reasoning: The conjunction fallacy in probability judgment. Psych.Rev. 90, 293-315.

24. Why there is a need for meta-logical elements (rationale and POV) to convince decision-makers • Early hominids: pre-rational decision-makers • Later hominids: usually rational • Even later hominids: almost always rational YOU ARE HERE

25. A 67 year old woman suffering from ICM with elevated bilirubin, history of diabetes, body mass index of 39.5, NYHA function class III, mitral valve regurgitation grade (MVRG) of 2+, and no aortic valve regurgitation (AVR) is assigned to CABG surgery.  RF+Cyc is consulted and the RF (random forest statistical reasoning) component, having been trained on a large database, identifies CABG alone as the most likely treatment option, citing an odds ratio of 2.6 over the next most favorable treatment, CABG+MVA. As rationale, the Cyc (AI) component observes that the low MVRG is atypical of MVA which is a surgical procedure typically reserved for patients with severe mitral regurgitation and thus the simpler CABG procedure is preferred.  However, an intraoperative transesophageal echocardiogram (TEE) suggests MVRG is 3+. Based on this, the surgical team overrides the initial diagnosis without consultation, opting instead for CABG+MVA.  The patient dies 3 days later from complications due to surgery.     In this setting, RF+Cyc, if consulted, could have alerted the heart team to additional data that might have swayed their decision, thus potentially saving a life. RF+Cyc would have noted that while an MVRG of 3+ is consistent with CABG+MVA, the odds favoring CABG only marginally decrease from 2.6:1 to 1.7:1 when MVRG is upstaged for this patient from 2+ to 3+, and that surgery under CABG alone offers a 20% increase in median survival compared to CABG+MVA.  RF+Cyc could further argue that intraoperative MVRG can falsely appear to be upstaged due to altered hemodynamics in anesthetized patients.  An Cyc-assisted semantic search of the recent literature reveals that transesophageal transthoracic echocardiograms (TTE) more reliably reflect the degree of mitral regurgitation than TEE. That (+co-morbidities) argues for just CABG. 

26. 4 Pitfalls of Semantic Technology • Ignorance-based: A small theory size (#terms, instances, rules) • Static KB (massively tuned, optimized, cached ahead of time) • Simple assertions (SAT constraints; propositional calculus; Horn clause logic; Description Logic; first order logic) • 1global context (no contradic.’s, tiny domain, simplified world)

27. Applying Cyc • Cyc is a power source, not a single application. Like oil, electricity, telephony, computers,… Cyc can spawn and sustain a knowledge utilityindustry. • It can cost-effectively underlie almost all apps. (Provide a common-sense layer to reduce brittleness when faced with unexpected inputs/situations) • To apply Cyc, we extend its ontology, its KB, and possibly its suite of specialized reasoning modules

28. General Knowledge Terrorism Terrorism Knowledge Knowledge Terrorism Knowledge Base) Base Cyc Cyc Cyc Cyc Reasoning Reasoning Reasoning Reasoning Modules Modules Modules Modules Terrorism Terrorism Knowledge Knowledge Interface to Data Repositories Credit output of Global HID HUMINT INS Geopolitical Data Border Weather Travel Military Satellite SIGINT Message Content Card COTS Text Observa Terrain - Messages Data Crossings Records Data Intel Intel Records Extraction Systems tions Data The Analyst’s Knowledge Base CT Analyst “Were there any attacks on targets of symbolic value to Muslims since 1987 on a Christian holy day?" "What sequences of events could lead to the destruction of Hoover Dam?" Domain Experts Query Query Explanation Explanation Scenario Scenario Formulation Formulator Generation Generator Generation Generator Others’/GOTS Cycorp Tools For: Ontology-Building, -Browsing, -Editing, & Fact/Rule Entry Analysis and General Knowledge Collaboration Components Terrorism Knowledge AKB OWL & Relational DB “projection” of the AKB

29. USGS GNIS DB AMVA KB UN FAO DB DTRA CATS DB RAND R A more recent example “What major US cities are particularly vulnerable to an anthrax attack?” The answer is logically implied by data dispersed through several sources:

30. “What major US cities are particularly vulnerable to an anthrax attack?” “major US city” ?C is aU.S. City with >1M population “particularly vulnerable to an anthrax attack”  • the current ambient temperature at ?C is above freezing, and • ?C has more than 100 people for each hospital bed, and • the number of anthrax host animals near ?C exceeds 100k

31. USGS GNIS DB state |         name          | type  |     county     | state_fips |  -------+-----------------------+-------+----------------+------------+ TX    | Dallas                | ppl   | Dallas         |         48 |  MN    | Hennepin County       | civil | Hennepin       |         27 |    CA    | Sacramento County     | civil | Sacramento     |          6 |    AZ    | Phoenix               | ppl   | Maricopa       |          4 |   primary_lat | primary_long| elevation | population |     status      | ------------+-------------+-----------+------------+------------------+  32.78333 |       -96.8 |       463 |    1022830 | BGN 1978 1959   45.01667 |      -93.45 |         0 |    1032431 |   38.46667 |  -121.31667 |         0 |    1041219 |   33.44833 |  -112.07333 |      1072 |    1048949 | BGN 1931 1900 1897 The Geographic Names Information System (GNIS) DB maintained by the US Geological Survey (USGS).

32. USGS GNIS DB • So how do we explain to our system that: • row 1 of that table is “about” the city of Dallas, TX • the population field of that table contains the number of inhabitants of the city that that row is “about” • here is exactly how to access tuples of that database • that access will be fast, accurate, recent, complete The Geographic Names Information System (GNIS) DB maintained by the US Geological Survey (USGS).

33. USGS GNIS DB • the population field of that table contains the number of inhabitants of the city that that row is “about” • We provide the field encodings and decodings, some of which correspond to explicit fields like population, two-letter state codes, etc: (fieldDecoding Usgs-Gnis-LS ?x        (TheFieldCalled “population”) (numberOfInhabitants (TheReferentOfTheRow Usgs-Gnis) ?x)) The Geographic Names Information System (GNIS) DB maintained by the US Geological Survey (USGS).

34. USGS GNIS DB • how to access tuples of that database • We provide all the information needed for a JDBC connection script: • We assert, in the context (MappingMtFn Usgs-KS), all of these: (passwordForSKS Usgs-KS "geografy") (portNumberForSKS Usgs-KS 4032) (serverOfSKS Usgs-KS "sksi.cyc.com") (sqlProgramForSKS Usgs-KS PostgreSQL) (structuredKnowledgeSourceName Usgs-KS "usgs") (subProtocolForSKS Usgs-KS "postgresql") (userNameForSKS "sksi") The Geographic Names Information System (GNIS) DB maintained by the US Geological Survey (USGS).

35. USGS GNIS DB • that access will be fast, accurate, recent, complete • We provide meta-level assertions about the database, about each table of the database, about the completeness etc. of various kinds of data in the DB, etc. • We assert, in the context (MappingMtFn Usgs-KS): (schemaCompleteExtentKnownForValueTypeInArg Usgs-Gnis-LS USCity numberOfInhabitants 1) The Geographic Names Information System (GNIS) DB maintained by the US Geological Survey (USGS).

36. USGS GNIS DB • that access will be fast, accurate, recent, complete • We provide meta-level assertions about the database, about each table of the database, about the completeness etc. of various kinds of data in the DB, etc. • We assert, in the context (MappingMtFn Usgs-KS): (resultSetCardinality Usgs-Gnis-PS        (TheSet (PhysicalFieldFn Usgs-Gnis-PS "state")) TheEmptySet 60.0)(resultSetCardinality Usgs-Gnis-PS        (TheSet            (PhysicalFieldFn Usgs-Gnis-PS "primary_long")            (PhysicalFieldFn Usgs-Gnis-PS "primary_lat")            (PhysicalFieldFn Usgs-Gnis-PS "name"))        (TheSet            (PhysicalFieldFn Usgs-Gnis-PS "county")            (PhysicalFieldFn Usgs-Gnis-PS "state")) 530.36) The Geographic Names Information System (GNIS) DB maintained by the US Geological Survey (USGS).

37. “What major US cities are particularly vulnerable to an anthrax attack?” “major US city” U.S. City with >1M population “particularly vulnerable to an anthrax attack”  • the current ambient temperature at ?C is above freezing, and • ?C has more than 100 people for each hospital bed, and • the number of anthrax host animals near ?C exceeds 100k Cyc knows that pullets are chickens, so don’t add those two numbers together!

43. Even simple queries often require 1-4 reasoning steps “In what countries bordering Pakistan are there members of the ANVC?” • Each answer that CAE finds for this generally involves a 1-4-step (not 0-step) argument (reasoning chain): • E.g., for the answer “India”, the justification is: • According to the web site ‘Inside Terrorism’, the ANVC’s headquarters has been in Garo Hills, India from the beginning of January, 1996 through today. • If an organization’s HQ is in place x, then there are members of that organization in place x. • If someone is in place x, they are in every super-region of x. • India borders Pakistan. Don’t include Prior & Tacit Knowledge

44. Thing Intangible Thing Individual Sets Relations Spatial Thing Temporal Thing Partially Tangible Thing Space Time Paths Events Scripts Spatial Paths Logic Math Agents Physical Objects Borders Geometry Artifacts Living Things Organ- ization Materials Parts Statics Actors Actions Movement Life Forms Plans Goals State Change Dynamics Organizational Actions Types of Organizations Ecology Human Beings Human Activities Physical Agents Natural Geography Organizational Plans Human Organizations Plants Human Anatomy & Physiology Nations Governments Geo-Politics Human Artifacts Political Geography Agent Organizations Business & Commerce Politics Warfare Animals Emotion Perception Belief Human Behavior & Actions Sports Recreation Entertainment Social Behavior Products Devices Conceptual Works Purchasing Shopping Professions Occupations Weather Law Vehicles Buildings Weapons Mechanical & Electrical Devices Software Literature Works of Art Social Relations, Culture Business, Military Organizations Earth & Solar System Social Activities Transportation & Logistics Travel Communication Everyday Living Language The Cyc Knowledge Base • Represented in: • First Order Logic • Higher Order Logic • Context Logic • Micro-theories Cyc contains: 15,000 Predicates 500,000 Concepts 5,200,000 Assertions These numbers are not a good way to really get a handle on the Cyc KB General Knowledge about Various Domains Specific data, facts, and observations

45. The Cyc Knowledge Base Cyc contains: 15,000 Predicates 500,000 Concepts 5,200,000 Assertions “Is any seagull also a moose?” If Cyc knows 10,000 kinds of animals, it should be able to answer 100,000,000 queries like that. Option 1: Add those 100M assertions to the KB Option 2: Add 50M disjointWith assertions instead Option 3: Add about 10k Linnaean taxonomy assertions to the KB, plus one extra assertion: (isa BiologicalTaxon SiblingDisjointCollectionType) If taxons A and B are not explicitly known (via those 10k assertions) to be in a subset/superset relationship, then assume that they are disjoint. These numbers are not a good way to really get a handle on the Cyc KB A few hundred such SiblingDisjoint assertions take the place of over 6 billion disjointness ones… which in turn take the place of 100 trillion ones like this: (not (isa Cher Moose))

46. There is no one correct monolithic ontology. E.g., Cyc’s 5M axioms are divided into thousands of contexts by: granularity, topic, culture, geospatial place, time,... There is a correct monolithic reasoning mechanism, but it is so deadly slow that we never call on it unless we have to E.g., the Cyc inference engine is a community of 1000 “agents” that attack every problem and, recursively, every subproblem (subgoal). One of these 1000 is a general theorem prover; the others have special-purpose data structures/algorithms to handle the most important, most common cases, very fast.

47. For: • - ETA often executes attacks near national election • - ETA has performed multi-target coordinated attacks • Over the past 30 years, ETA performed 75% of all terrorist attacks in Spain • Over the past 30 years, 98% of all terrorist attacks in Spain were performed • by Spain-based groups, and ETA is a Spain-based group. • Against: • ETA warns (a few minutes ahead of time) of attacks that would result in a • high number civilian casualties, to prevent them. There was no such warning • prior to this attack. • ETA generally takes responsibility for its attacks, and it did not do so this time. • ETA has never been known to falsely deny responsibility for an attack, and it • did deny responsibility for this attack. What factors argue <for/against> the conclusion that <ETA> <performed> <the March 2004 Madrid attacks>?

48. rate of learning amount known Frontier of human knowledge Building Cycqua Engineering Task 1984 2004 today learning via natural language learning by discovery codify&enter each piece of knowledge, by hand CYC 900 person-years 23 realtime years $90 million

50. Temporal Relations 37 Relations Between Temporal Things temporalBoundsContain temporalBoundsIdentical startsDuring overlapsStart startingPoint simultaneousWith after temporalBoundsIntersect temporallyIntersects startsAfterStartingOf endsAfterEndingOf startingDate temporallyContains temporallyCooriginating