Download Dialogue systems: simulations or interfaces?

Dialogue Systems:
Simulations or Interfaces?
Staffan Larsson
Göteborg University
Sweden
Introduction
Basic question
• What is the goal of formal dialogue
research?
• Formal dialogue research =
– formal research on the semantics and
pragmatics of dialogue
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
The convergence assumption
There is an extensive if not complete
overlap between the simulation of human
language use and the engineering of
conversational interfaces.
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
Formal dialogue research and
GOFAI
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
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)
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)
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
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
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?
Phenomenological arguments
against GOFAI
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 4year-old
– AI is still very far from this
• 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
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, ...
• 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
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
“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
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
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)
• 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
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
Simulation and NEFAI
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
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
• 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
• 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
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
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...
• It appears that the research methods
required for simulation are rather different
from those required for interface design
• The convergence assumption appears
very dubious
Formal dialogue research and
dialogue systems design
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)
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
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”)
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
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 humanmachine interaction
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
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
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.
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
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.
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
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
Conclusions
• 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
• 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.
fin
Other views of what FDR is
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
• 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
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
Applied science?
• Formal dialogue research as “applied
science”
– c.f. medicine
– theories of interface design
– theories of (linguistic) human-computer
interaction - LHCI
The role of human-human
communication in LHCI
• What aspects of “natural dialogue” are
– formalisable
– implementable
– useful in HCI
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
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
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
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
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
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 nonsystematic 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
More on skills vs. rules
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.
Everyday skills vs. rules
• Having acquired the necessary social skill,
– one does not need to recognize the situation as appropriate for giftgiving, 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
The B.A.B. objection background
Arguments related to evolution
The ”humans are animals” argument
• What reason do we have to think that nonconscious 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
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
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 brainmatter 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
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!
More on CYC
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?
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
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
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
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
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
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
Neural nets
• 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
• 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
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
Non-symbolic approaches to AI
and dialogue
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
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
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
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”
AI and Turing Test
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)
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)
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
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)
Misc slides
• 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.
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)
Usefulness of formal semantics and
pragmatics
• Systems based on formal representations
provide a great potential for improving on
human-computer interaction
(An aside: human reinforcement)
• 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
– Dreyfus, Haugeland and others trace the idea
back to Plato; it pervades most of western
philosophy
– This shifts the burden of proof onto GOFAI