1. Dialogue Systems: Simulations or Interfaces? Staffan Larsson Göteborg University Sweden

2. Introduction

3. Basic question What is the goal of formal dialogue research? Formal dialogue research = –formal research on the semantics and pragmatics of dialogue

4. Two possible answers Engineering view: the purpose of formal dialogue research is –interface engineering (services and technologies) –enable building better human-computer interfaces Simulation view: the ultimate goal of formal dialogue research is –a complete formal and computational (implementable) theory of human language use and understanding

5. The convergence assumption There is an extensive if not complete overlap between the simulation of human language use and the engineering of conversational interfaces.

6. Aim of this presentation Review an argument against the possibility of human-level natural language understanding in computers (simulation view) Explicitly apply this argument to formal dialogue research, arguing that the covergence assumption is dubious Draw out the consequences of this for formal dialogue research

7. Formal dialogue research and GOFAI

8. The Turing test Can a machine think? Turing offers an operational definition of the ability to think Turing’s imitation game –Test person A has a dialogue (via a text terminal) with B. –A:s goal is to decide whether B is a human or a machine –If B is a machine and manages to deceive A that B is a human, B should be regarded as able to think

9. The Turing test and the Simulation view The Turing Test can be seen as the ultimate test of a simulation of human language use The ability to think is operationalised as the ability to carry out a natural language dialogue in a way that is indiscernible from that of a human The goal of formal dialogue research coincides with the goal of AI (as originally perceived)

10. GOFAI Artificial Intelligence –Goal: simulate human/intelligent behaviour/thinking –Weak AI:Machines can be made to act as if they were intelligent Until the mid-80’s, the dominating paradigm of AI was the idea that thinking is, essentially, symbol manipulation The physical symbol hypothesis –All intelligent behaviour can be captured by a system that reasons logically from a set of facts and rules that describe the domain This is sometimes referred to as GOFAI –(Good Old Fashioned AI)

11. Dialogue systems and GOFAI Since around the mid-80’s, GOFAI has been abandoned by many (but not all) AI researchers Instead, focus on NEFAI (New-Fangled AI) –connectionism, –embodied interactive automata, –reinforcement learning, –probabilistic methods, etc. However, a large part of current dialogue systems research is based on the GOFAI paradigm –Information States, for example… Formal pragmatics is often used as a basis for the implementation of dialogue managers in GOFAI-style approaches

12. Formal semantics and GOFAI GOFAI and formal semantics deals, to a large extent, with similar problems and use similar methods –Formal symbolic representations of meaning –Natural Language Understanding as symbol manipulation –(Even though many early GOFAI researchers appear oblivious to the existence of formal semantics of natural language in the style of Montague, Kamp etc.) Formal semantics perhaps not originally intended to be implemented, and not as part of AI Still, formal semantics shares with GOFAI rests on the assumption that natural language meaning can be captured in formal symbol manipulation systems

13. Why GOFAI? Why GOFAI in formal semantics and pragmatics? –It seems to be the most workable method for the complex problems of natural language dialogue –Natural language dialogue appears to be useful for improving on current human-computer interfaces But is GOFAI-based research also a step on the way towards ”human-level” natural language understanding in computers, i.e. simulation?

14. Phenomenological arguments against GOFAI

15. Some problems in AI Frame problem –updating the “world model” –knowing which aspects of the world are relevant for a certain action Computational complexity in real-time resource-bounded applications –Planning for conjunctive goals –Plan recognition Incompleteness of general FOL reasoning –not to mention modal logic Endowing a computer with the common sense of a 4- year-old –AI is still very far from this

16. Humans don’t have problems with these things Is it possible that all these problems have a common cause? –They all seem to be related to formal representations and symbol manipulation

17. Background and language understanding Dreyfus, Winograd, Weizenbaum Human behaviour based on our everyday commonsense background understanding allows us to experience what is currently relevant, and deal with tings and people crucial to understanding language involves utterance situation, activity, institution, cultural setting,...

18. Dreyfus argues that the background has the form of dispositions, or informal know-how –Normally, ”one simply knows what to do” –a form of skill rather than propositional knowing-that To achieve GOFAI, –this know-how, along with interests, feelings, motivations, social interests, and bodily capacities that go to make a human being,... –... would have to be conveyed to the computer as knowledge in the form of a huge and complex belief system

19. CYC (Lenat) and natural language An attempt to formalise common sense –The kind of knowledge we need to understand NL –using general categories that make no reference to specific uses of the knowledge Lenat’s ambitions: –it’s premature to try to give a computer skills and feelings required for actually coping with things and people –L. is satisfied if CYC can understand books and articles and answer questions about them

20. “The background cannot be formalised” There are no reasons to think that humans represent and manipulate the background explicitly, or that this is possible even in principle “...understanding requires giving the computer a background of commons sense that adult humans have in virtue of having bodies, interacting skilfully with the material world, and being trained into a culture” Why does it appear plausible that the background could be formalised knowing-that? –Breakdowns –Skill acquisition

21. Skills and formal rules When things go wrong - when we fail – there is a breakdown –In such situations, we need to reflect and reason, and may have to learn and apply formal rules but it is a mistake to –read these rules back into the normal situation and –appeal to such rules for a causal explanation of skilful behaviour

22. Dreyfus’ account of skill acquisition 1. Beginner student: Rule-based processing learning and applying rules for manipulating context-free elements There is thus a grain of truth in GOFAI 2. Understanding the domain; seeing meaningful aspects, rather than context-free features 3. Setting goals and looking at the current situation in terms of what is relevant 4. Seeing a situation as having a certain significance toward a certain outcome 5. Expert: The ability of instantaneously selecting correct responses (dispositions)

23. There is no reason to suppose that the beginner’s features and rules (or any features and rules) play any role in expert performance –That we once followed a rule in tying our shoelaces does not mean we are still following the same rule unconsciously –”Since we needed training wheels when learning how to ride a bike, we must now be using invisible training wheels.” Human language use and cognition involves symbol manipulation, but is not based on it

24. Recap Language understanding requires access to human background understanding This background cannot be formalised Since GOFAI works with formal representations, GOFAI systems will never be able to understand language as humans do

25. Simulation and NEFAI

26. What about NEFAI? This argument only applies to GOFAI! A lot of modern AI is not GOFAI New-Fangled AI (NEFAI) –interactionist AI (Brooks, Chapman, Agre) –embodied AI (COG) –connectionism / neural networks –reinforcement learning So maybe human language use and understanding could be simulated if we give up GOFAI and take up NEFAI? –Note that very few have tried this in the area of dialogue –Simply augmenting a GOFAI system with statistics is not enough

27. Progress? Although NEFAI is more promising than GOFAI...... most current learning techniques rely on the previous availability of explicitly represented knowledge – the training data must be interpreted and arranged by humans in the case of learning the background, this means that the background has to be represented before it can be used for training But as we have seen, Dreyfus argues that commonsense background cannot be captured in explicit representations

28. Russel & Norvig, in Artificial Intelligence -A Modern Approach (1999) –In a discussion of Dreyfus’ argument: –”In our view, this is a good reason for a serious redesign of current models of neural processing.... There has been some progress in this direction.” –But no such research is cited So R & N admit that this is a real problem. In fact it is still the exact same problem that Dreyfus pointed out originally –There is still nothing to indicate that Dreyfus is wrong when arguing against the possibility of getting computers to learn commonsense background knowledge

29. But let’s assume for the moment that the current shortcomings of NEFAI could be overcome... –that learning mechanisms can be implemented who learn in the same way humans do –and that appropriate initial structure of these systems can be given –and that all this can be done without providing predigested facts that rely on human interpretation

30. Some factors influencing human language use Embodiment –having a human body, being born and raised by humans Being trained into a culture –by interacting with other humans Social responsibility –entering into social commitments with other people

31. What is needed to achieve simulation? So, perhaps we can do real AI, provided we can build robot infants that are raised by parents and socialised into society by human beings who treat them as equals –This probably requires people to actually think that these AI systems are human –These systems will have the same ethical status as humans If we manage to do it, is there any reason to assume that they would be more useful to us than ordinary (biological) humans? –They are no more likely to take our orders...

32. It appears that the research methods required for simulation are rather different from those required for interface design The convergence assumption appears very dubious

33. Formal dialogue research and dialogue systems design

34. Consequences of the argument for the engineering view If we accept the argument that “the background is not formalisable” and that computers (at least as we know them) cannot simulate human language understanding......what follows with respect to the relations between 1.Formal semantics and pragmatics of dialogue 2.Non-formal theories of human language use 3.Dialogue systems design as interface engineering Both (1) and (2) are still relevant to (3)

35. Winograd on language and computers Even though computers cannot understand language in the way humans can......computers are nevertheless useful tools in areas of human activity where formal representation and manipulation is crucial –e.g. word processing. In addition, many practical AI-style applications do not require human-level understanding of language –e.g. programming a VCR, getting timetable information In such cases, it is possible to develop useful systems that have a limited repertoire of linguistic interaction. This involves the creation of a systematic domain

36. Systematic domains A systematic domain is a set of formal representations that can be used in a computer system Embodies the researcher’s interpretation of the situation in which the system will function. Created on the basis of regularities in conversational behaviour (“domains of recurrence”)

37. so... For certain regular and orderly activities and language phenomena...... it is possible to create formal representations which capture them well enough to build useful tools Formal dialogue research can be regarded as the creation of systematic domains in pragmatics and semantics of dialogue

38. Formal semantics and pragmatics of dialogue as systematic domains Formal theories of language use should be regarded as –the result of a creative process of constructing formal representations (systematic domains) –based on observed regularities in language use These theories can be used in dialogue systems to enable new forms of human- machine interaction

39. Formal pragmatics Pragmatic domains include e.g. –turntaking, feedback and grounding, referent resolution, topic management Winograd gives dialogue game structure as a prime example of a systematic domain –Analysed along the lines of “dialogue games” encoded in finite automata ISU update approach is a variation of this, intended to capture the same regularities in a (possibly) more flexible way It is likely that useful formal descriptions can be created for many aspects of dialogue structure

40. Formal semantics Not a focus of Winograd’s formal analysis, –presumably because Winograd believes that language understanding is not amenable to formal analysis However, even if one accepts the arguments such as those above...... it seems plausible that the idea of systematic domains also applies to semantics That is, for certain “semantically regular” task domains it is indeed possible to create a formal semantics –e.g. in the form of a formal ontology and formal representations of utterance contents This formal semantics will embody the researcher’s interpretation of the domain

41. Relevant issues related to semantic domains How to determine whether (and to what extent) a task domain is amenable to formal semantic description How to decide, for a given task domain, what level of sophistication is required by a formal semantic framework in order for it to be useful in that domain –In some domains, simple feature-value frames may be sufficient while others may require something along the lines of situation semantics, providing treatments of intensional contexts etc. Fine-grainedness and expressivity of the formal semantic representation required for a domain or group of domains –e.g. database search, device programming, collaborative planning,... Creation of application-specific ontologies –How to extract applications ontologies from available data of the domain, e.g. transcripts of dialogues.

42. but... Even though some aspects of language use may indeed be susceptible to formal description This does not mean that human language use actually relies on such formal descriptions represented in the brain or elsewhere –So implementations based on such formalisations are not simulations of human language use and cognition

43. Limits of formalisation Formalisation will only be useful in areas of language use which are sufficiently regular to allow the creation of systematic domains So, repeated failures to formally capture some aspect of human language may be due to the limits of formal theory when it comes to human language use, rather than to some aspect of the theory that just needs a little more tweaking.

44. Non-formalisable language phenomena For other activities and phenomena, it may not possible to come up with formal descriptions that can be implemented –e.g. human language understanding in general, since it requires a background which cannot be formalised –also perhaps aspects of implicit communication, conversational style, politeness in general, creative analogy, creative metaphor, some implicatures This does not mean that they are inaccessible to science. –They can be described non-formally and understood by other humans –Their general abstract features may be formalisable

45. Usefulness of non-formal theory Non-formal theories of human language use are still useful for dialogue systems design Dialogue systems will need to be designed on the basis of theories of human language –They will, after all, interact with a human –May also be useful to have human-like systems (cf. Cassell) This does not require that implementations of these theories have to be (even partial) simulations of human language use and cognition Also, observations of human-human dialogue can of course be a source of inspiration for dialogue systems design

46. Conclusions

47. In important ways the simulation view and the engineering view are different projects requiring different research methods –For the simulation project, the usefulness of systems based on formal representations is questionable –Instead, formal dialogue research can be regarded as the creation of systematic domains that can be used in the engineering of flexible human-computer interfaces –In addition, non-formal theory of human language use can be useful in dialogue systems design

48. If interface engineering is liberated from concerns related to simulation......it can instead be focused on the creation of new forms of human-computer (and computer-mediated) communication...... adapting to and exploring the respective limitations and strengths of humans and computers.

49. fin

50. Other views of what FDR is

51. A variant of the simulation view The goal of formal dialogue research is a complete computational theory of language and cognition for machines cf. Luc Steels –Robots evolving communication Not intended to describe human language use –although some aspects may be similar Arguably interesting in its own right

52. One may even be able to implement computational models that capture some abstract aspects of human language use and understanding –That are not based on symbol manipulation, but involve subsymbolic computation –For example, the evolution of shared language use in robots (Steels et al) However, such formal models and simulations will never be complete simulations of human language understanding (to the extend required by the Turing test) … … unless the machines they run on are human in all aspects relevant to language, i.e. physical, biological, psychological, and social

53. A variant of the simulation view The goal of formal dialogue research is a complete computational theory of language and cognition in general either such a theory subsumes a theory of human language –and thus as difficult or more difficult or not –and thus coherent with idea that only some aspects of language are formalisable, –although it remains to show that the same features are the ones that are essential for language

54. Applied science? Formal dialogue research as “applied science” –c.f. medicine –theories of interface design –theories of (linguistic) human-computer interaction - LHCI

55. The role of human-human communication in LHCI What aspects of “natural dialogue” are –formalisable –implementable –useful in HCI

56. Scientific status of formal descriptions Formal descriptions may have some scientific value as theories of human language use and cognition However, they are –not useful as a basis for simulation of human language use and congition, since this is not based on explicit rules and representations (except for novices and breakdowns) –often radical simplifications (as many other scientific theories) –limited in scope and describe special cases only Even if the creation of a systematic domain is possible for some linguistic phenomena, this does not mean that human language use is based on formal representations

57. Formal dialogue research vs. Dialogue systems research Both share the assumption that human language use and meaning can be captured in formal symbol manipulation systems Human language use and meaning relies on background Background cannot be formalised

58. Language use vs. cognition Turing test tests only behaviour; cognition is a black box So what’s the justification for talking about cognition? Turing’s test intended as an operational definition of thinking, i.e. cognition Possible underlying intuition: –There is no way of passing the Turing test for a system with a style of cognition which is very different from human cognition –Turing assumed that human cognition was based on symbol manipulation

59. Domain-specific simulation? In a regular domain, can a program based on a formalisation of these regularities be regarded as a simulation of human performance in that domain? –Even if there are regularities that can be captured to a useful extent in rules, this does not mean that humans use such rules –unless they are complete novices who have been taught the rules explicitly but have not yet had time to descend down the learning hierarchy

60. General vs. domain-specific intelligence Weizenbaum: there is no such thing as general intelligence –intelligence is always relative to a domain (math, music, playing cards, cooking,...) Therefore, the question whether computers can be intelligent is meaningless –one must ask this question in individual domains

61. The Feigenbaum test Replace the general Turing test with a similar test in limited domains? (proposed by Feigenbaum) –Certainly seems more manageable, especially in systematic domains –On the other hand, it could be argued that it is exactly in the non- systematic domains that the most interesting and unique aspects of human being are to be found –So this test is very different from the original Turing test

62. More on skills vs. rules

63. Everyday skills vs. rules Dreyfus suggests testing the assumption that the background can be formalised –by looking at the phenomenology of everyday know-how –Heidegger, Merleau-Ponty, Pierre Bourdieu What counts as facts depends on our skills; e.g. gift-giving (Bourdieu) –If it is not to constitute an insult, the counter-gift must be deferred and different, because the immediate return of an exact identical object clearly amounts to a refusal.... –It is all a question of style, which means in this case timing and choice of occasion... –...the same act – giving, giving in return, offering one’s services, etc. – can have completely different meanings at different times.

64. Everyday skills vs. rules Having acquired the necessary social skill, –one does not need to recognize the situation as appropriate for gift- giving, and decide rationally what gift to give –”one simply responds in the appropriate circumstances by giving an appropriate gift” Humans can –skilfully cope with changing events and motivations –project understanding onto new situations –understand social innovations one can do something that has not so far counted as appropriate......and have it recognized in retrospect as having been just the right thing to do

65. The B.A.B. objection - background

66. The argument from infant development (Weizenbaum) (Based on writings by child psychologist Erik Erikson) The essence of human being depends crucially on the fact that humans are born of a mother, are raised by a mother and father, and have a human body –”Every organism is socialized by dealing with problems that confront it” (Weizenbaum) –For humans, the problems include breaking the symbiosis with the mother after the infant period –This is fundamental to the human constitution; it lays the ground for all future dealings with other people Men and machines have radically different constitutions and origins –Humans are born by a mother and father –Machines are built by humans OK, so we need to give AI systems a human or human-like body, and let human parents raise them

67. The argument from language as social commitment (Winograd) The essence of human communication is commitment, an essentially social and moral attitude Speech acts work by imposing commitments on speaker and hearer If one cannot be held (morally) responsible for one’s actions, one cannot enter into commitments Computers are not human –so they cannot be held morally responsible –therefore, they cannot enter into commitments Therefore, machines can never be made to truly and fully understand language OK, so we need to treat these AI computers exactly as humans, and hold them morally responsible

68. The argument from ”human being”/Dasein (Heidegger, Dreyfus) Heidegger’s project in Being and Time –Develop an ontology for describing human being –What it’s like to be human This can, according to Heidegger, only be understood ”from the inside” –H:s text is not intended to be understandable by anyone who is not a human Such an explanation is not possible, according to H.; human being cannot be understood ”from scratch” –Yet it is exactly such an explanation that is the goal of AI According to Heidegger/Dreyfus, AI is impossible because (among other things) –Infants are, strictly speaking, not yet fully human; they must first be socialised into a society and a social world –Only humans so socialized can fully understand other humans –Since cultures are different, humans socialized into one culture may have problems understanding humans from another culture –Machines are not socialised, they are programmed by humans OK, so we need to socialise AI systems into society!

69. Arguments related to evolution

70. The ”humans are animals” argument What reason do we have to think that non- conscious reasoning operates by formal reasoning? Humans have evolved from animals, so presumably some non-formal thinking is still part of the human mind –Hard to tell a priori how much

71. The argument from the role of emotions Classical AI deals first with rationality Possibly, we might want to add emotions as an additional layer of complexity However, it seems plausible to assume that emotions are more basic than rationality (Damasio: The Feeling of what happens) –Animals have emotions but not abstract rational reasoning –The human infant is emotional but not rational So machines should be emotional before they are made rational –unfortunately, no-one has a clue how to make machines emotional

72. The argument from brain matter and evolution Weak AI assumes that physical-level simulation is unnecessary for intelligence However, evolution has a reputation for finding and exploiting available shortcuts –works by ”patching” on previous mechanisms If there are any unique properties of biological brain- matter that offers some possible improvement to cognition, it is likely they have been exploited If so, it is not clear if these properties can be emulated by silicon-based computers

73. The argument from giving a damn Humans care; machines don’t give a damn (Haugeland) Caring (about surviving, for example) comes from instincts (drives) which animals, but not machines, have Caring about things is intimately related to the evolution of living organisms –Having a biological body So, can evolution be simulated? –Winograd argues that the only simulation that would do the job would need to be as complex as real evolution –So in 3,5 billion years, we can have AI!

74. More on CYC

75. Problems with formalising commonsense background How is everyday knowledge organized so that one can make inferences from it? –Ontological engineering: finding the primitive elements in which the ontology bottoms out How can skills or know-how be represented as knowing-that? How can relevant knowledge be brought to bear in particular situations?

76. CYC (Lenat) and natural language Formalise common sense –The kind of knowledge we need to understand NL –using general categories that make no reference to specific uses of the knowledge (context free) Lenat’s ambitions: –it’s premature to try to give a computer skills and feelings required for actually coping with things and people –L. is satisfied if CYC can understand books and articles and answer questions about them

77. CYC vs. NL Example (Lenat) –”Mary saw a dog in the window. She wanted it.” Dreyfus: –this sentence seems to appeal to our ability to imagine how we would feel in the situation know-how for getting around in the world (e.g. getting closer to something on the other side of a barrier) –rather than requiring us to consult facts about dogs and windows and normal human reactions So feelings and coping skills that were excluded to simplify the problem return –We shouldn’t be surprised; this is the presupposition behind the Turing Test – that understanding human language cannot be isolated from other human capabilities

78. CYC vs. NL How can relevant knowledge be brought to bear in particular situations? –categorize the situation –search through all facts, following rules to find the facts possibly relevant in this situation –deduce which facts are actually relevant How deal with complexity? –Lenat: add meta-knowledge Dreyfus: –meta-knowledge just makes things worse; more meaningless facts –CYC is based on an untested traditional assumption that people store context-free facts and use meta-rules to cut down the search space

79. Analogy and metaphor... pervade language (example from Lenat): –”Texaco lost a major ruling in its legal battle with Pennzoil. The supreme court dismantled Texaco’s protection against having to post a crippling $12 billion appeals bond, pushing Texaco to the brink of a Chapter 11 filing” (Wall Street Journal) The example drives home the point that, –far from overinflating the need for real-world knowledge in language understanding, –the usual arguments about disambiguation barely scratch the surface

80. Analogy and metaphor... pervade language (example from Lenat): –”Texaco lost a major ruling in its legal battle with Pennzoil. The supreme court dismantled Texaco’s protection against having to post a crippling $12 billion appeals bond, pushing Texaco to the brink of a Chapter 11 filing” (Wall Street Journal) The example drives home the point that, –far from overinflating the need for real-world knowledge in language understanding, –the usual arguments about disambiguation barely scratch the surface

81. Analogy and metaphor Dealing with metaphors is a non-representational mental capacity (Searle) –”Sally is a block of ice” could not be analyzed by listing the features that Sally and ice have in common Metaphors function by association –We have to learn from vast experience how to respond to thousands of typical cases Mention approaches to metaphor, e.g. Abduction - “the boston office called” - isn’t this a solution? Dead vs. Creative metaphors

82. Neural nets

83. Helge! –But people also interpret things differently (but not wildly differently) –Not many researchers believe in tabula rasa Evolutionary alogrithms; but so far not combined with learning –Nns *can* learn without prior strong symbolisation of learning data, but pehaps not very complex stuff like dialogue? –Data can be either discrete or continuous How does this relate to “predigestion”? Is selection of data (e.g. Dividing into frequency ranges= “predig.”? –Main obstacle now: puny amounts of neurons, little knowledge of interaction of evolved initial structure + learning

84. neural nets can learn some things without prior conceptualisation (but some discretisation is necessary, e.g. representation in the weak sense) –strong and weak sense of represenation Other problems with connectionism Current neural networks are much less complex that brains –but maybe this will change Even if we had a working neural network, we would not understand how it works –the scientific goal of AI would thus still not have been reached

85. Learning & generalisation Take in dialogue systems based solely on statistics (superhal?) Mention hybrid vs totally nonsymbolic systems Learning depends on the ability to generalise Good generalisation cannot be achieved without a good deal of background knowledge Example: trees/hidden tanks A network must share our commonsense understanding ot the world if it is to share our sense of appropriate generalisation

86. Some counter-counter-arguments: –The more the computer knows, the longer it will take to find the right information The more a human knows, the easier it is to retrieve relevant information

87. Non-symbolic approaches to AI and dialogue

88. Interactionist AI No need for a representation of the world –instead, look to the world as we experience it Behaviour can be purposive without the agent having in mind a goal or purpose –In many situations, it is obvious what needs to be done –Once you’ve done that, the next thing is likely to be obvious too –Complex series of actions result, without the need for complex decisions or planning However, Interactionist AI does not address problem of informal background familiarity –programmers have to predigest the domain and decide what is relevant –systems lack ability to discriminate relevant distinctions in the skill domain... –... and learn new distinctions from experience

89. Connectionism Apparently does not require being given a theory of a domain in order to behave intelligently –Finding a theory = finding invariant features in terms of which situations can be mapped onto responses Starting with random weights, will neural nets trained on same date pick out the same invariants? –No; it appears the ”tabula rasa” assumption (random initial weights) is wrong Little research on how (possibly evolved) initial structure interact with learnin

90. Learning & generalisation Learning depends on the ability to generalise Good generalisation cannot be achieved without a good deal of background knowledge Example: trees/hidden tanks A network must share our commonsense understanding ot the world if it is to share our sense of appropriate generalisation

91. Reinforcement learning Idea: learn from interacting with the world –Feed back reinforcement signal measuring the immediate cost or benefit of an action –Enables unsupervised learning –(The ”target representation” in humans is neural networks) Dreyfus: To build human intelligence, need to improve this method –assigning fairly accurate actions to novel situations –reinforcement-learning device must ”exhibit global sensitivity by encountering situations under a perspective and actively seeking relevant input”

92. AI and Turing Test

93. Prehistory of AI Plato / Socrates: –All knowledge must be statable in explicit definitions which anyone could apply (cf. definition of algorithm) Descartes again: –Understanding and thinking is forming and using symbolic representations; the rational manipulation of symbols by means of rules –Thinking as computation Leibniz –universal calculus for representing & reasoning –goal: put an end to all conflicts, which are caused by misunderstandings of inexact, informal language Kant: –clear distinction between external world (noumena) and inner life (phenomena, representations)

94. Artificial Intelligence Goal –simulate human/intelligent behaviour/thinking Weak AI –Machines can be made to act as if they were intelligent Strong AI –Agents that act intelligently have real, conscious minds (It is possible to believe in strong AI but not in weak AI)

95. Some arguments against weak AI (Turing 1950) Ada Lovelace’s objection –computers can only do what we tell them to Argument from disability –claims (usually unsupported) of the form ”a machine can never do X” The mathematical objection –based on Gödel’s incompleteness theorem The argument from informality of behaviour (Searle’s Chinese Room –argument concerns strong AI –purports to show that producing intelligent behavoiur is not a sufficient condition for being a mind)

96. The Turing test and dialogue The Turing Test can be seen as the ultimate test of a simulation of human language use The ability to think is operationalised as the ability to carry out a natural language dialogue in a way that is indiscernible from that of a human The machine in question is assumed to be a Turing machine, i.e. a general symbol manipulation device, i.e. a computer

97. The Turing test and the Simulation view According to the “simulation view”, the goal of formal dialogue research is to reproduce, in a machine, the human ability to use and understand language Thus, the Turing test can be regarded as a potential method of evaluating theories of human language use and understanding The goal of formal dialogue research coincides with the goal of AI (as originally perceived)

98. Misc slides

99. Non-formal theories of those aspects of language use which resist formalisation can be used as a basis for design of aspects of dialogue systems that do not need to be modelled by the system itself. For example, it is likely that any speech synthesizer voice has certain emotional or other cognitive connotations –it might sound silly, angry, etc.. It is extremely difficult, if not impossible, to design a completely neutral voice. However, if we have some idea of how different voices are perceived by humans, we can use this (informal) knowledge to provide a dialogue system application with an appropriate voice for that application.

100. Dreyfus’ account of skill acquisition 5 stages –1. Beginner student: Rule-based processing; learning and applying rules for manipulating context-free elements There is thus a grain of truth in GOFAI –2. Understanding the domain; seeing meaningful aspects, rather than context-free features –3. Setting goals and looking at the current situation in terms of what is relevant –4. Seeing a situation as having a certain significance toward a certain outcome –5. Expert: The ability of instantaneously selecting correct responses (dispositions) (Note: this is how adults typically learn; Infants, on the other hand... –learn by imitation –”pick up on a style” that pervades his/her society)

102. The question is still open exactly how far it is possible to go in the formal description of phenomena related to language use The only way to find out is to by trial-and- error (i.e., research). In this, I suggest one might be well- advised to keep in mind the following points..

103. From observations of some domain or aspect of language use...... the researcher creates a formal representation...... and implements it in a dialogue system. When this dialogue system is used and interacts with humans, new domain [?]of interaction comes into being –depending on the design of the system, different conversational patterns will arise Domains of language use that may be susceptible to formalisation (i.e. creation of systematic domains) can be roughly divided into pragmatic and semantic domains

104. Usefulness of formal semantics and pragmatics Systems based on formal representations provide a great potential for improving on human-computer interaction

105. (An aside: human reinforcement)

106. Overview Introduction Formal dialogue research and GOFAI Phenomenological arguments against GOFAI Simulation and NEFAI Formal dialogue research and dialogue systems design Conclusions

107. Currently, programmer must supply machine with rule formulating what to feed back as reinforcement What is the reinforcement signal for humans? –Survival? –Pleasure vs. pain? Requires having needs, desires, emotions Which in turn may depend on the abilities and vulnerabilities of a biological body

108. –Dreyfus, Haugeland and others trace the idea back to Plato; it pervades most of western philosophy –This shifts the burden of proof onto GOFAI