Could computers understand language?

Phenomenological (and other) arguments against Artificial Intelligence

Staffan Larsson

Dept. of linguistics

Göteborg University

Overview

• Artificial Intelligence

• Phenomenological arguments

• The BaB objection

• Arguments related to evolution

• Conclusion

Artificial Intelligence

The Man Machine?

• Descartes (1596-1650)– Animals are to be considered as machines, automata– Man has a soul and is therefore fundamentally

different from machines• de la Mettrie (1709-1751)

– ”The man machine”– The difference between human and machine is a

qualitative difference of complexity– No fundamental difference!

• These questions are intimately related:– Can machines think?– Are humans machines?

The Turing test

• Can a machine be intelligent? Is ”artificial intelligence” (AI) possible?

• Turing offers an operational definition of intelligence• Turing (1912-1954): ”the Turing test”

– 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 intelligent (able to think; ”a grade A machine”)

– (This is a simplified version of the Turing test)

The Turing test and dialogue

• According to the Turing test – what is fundamentally human?– The ability to carry out a dialogue using natural

language

• Why is this fundamental?– Assumption: In dialogue, all other human capabilities

show themselves (directly or indirectly)

• This means that ...– ... in order to make a computer use natural language

in the same way and on the same level as a human, it needs to be endowed with human-level intelligence

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

A short history of AI

Cognitivism and GOFAI

• Descartes again: – Understanding and thinking is forming and using symbolic

representations

• Until the mid-80’s, the paradigm of AI was cognitivism, the idea that thinking is, essentially, symbol manipulation

• The physical symbol hypothesis (Newell & Simon):– ”A physical symbol system has the necessary and sufficient

means for intelligent action.”– 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 as GOFAI?

• Since around 1986, GOFAI has been abandoned by many AI researchers– Instead, focus on connectionism, embodied interactive automata,

reinforcment learning, probabilistic methods, etc.• However, a large part of current dialogue systems research adheres

to the GOFAI paradigm– Information States, for example…

• Why? – It seems to be the most workable method for the complex problems of

natural language dialogue– It appears to be useful for improving on current human-computer

interfaces, although a major breakthrough of NL interfaces is needed to prove this conclusively

• But is it also a step on the way towards ”human-level” natural language understanding in computers?– Does it scale up?

Phenomenological arguments

Some arguments against weak AI

• 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)

Some problems in AI• Computational complexity in real-time resource-bounded

applications– Reasoning– Planning for conjunctive goals – Plan recognition

• Incompleteness of general FOL reasoning– not to mention modal logic

• Frame problem– updating the “world model”

• Endowing a computer with the common sense of a 4-year-old– AI is still very far from this

• Humans don’t have problems with these things• Is is possible that all these problems have a common cause?

– They all seem to be related to representations and symbol manipulation

The argument from informality of behaviour (Dreyfus, Winograd, Weizenbaum)

• Human behaviour based on our everyday commonsense background understanding / holistic context– 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

• In its widest sense, the background involves all of human culture and experience

• 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– inarticulate, to some extent pre-conceptual

• To achieve GOFAI, – this know-how, along with interests, feelings,

motivations, 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

• ”The background cannot be formalised” – There are no reasons to think that humans represent

and manipulate the background explicitly• Human behaviour is far too complex to be

described by a set of formal rules of the kind that can be followed by computers– (”unwritten rules” are not just unwritten, but un-

writable; any written version will leave something out and overspecify something else)

– (This applies mainly to Von Neumann computers programmed in the standard way; we’ll get to artificial neural networks later)

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

Everyday skills vs. rules

• Dreyfus suggests testing this assumption– 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

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

Everyday skills vs. 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

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

Background and NL• NL interpretation problems

– Analogy, metaphor– It is notoriously hard to exactly pin down implicatures and

presuppositions of natural language utterances• It appears that full It appears that full disambiguation and

understanding of natural language requires access to this background knowledge – Still, this does not normally cause us any problems

• Most (if not all) dialogue systems assume that context can be formalised– This is perhaps plausible for ”micro-worlds”, i.e. highly limited and

systematic domains, such as train timetables or programming a VCR– So we can still do practically useful AI work for limited domains

Counter-argument 1

• Phenomenology studies how things appear to us, not how they are– The fact that we are often not aware of

reasoning simply indicates that a lot of reasoning is non-conscious

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)

Response to counter-argument 1

• 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.”

Counter-argument 2

• This argument only applies to GOFAI!

• A lot of modern AI is not GOFAI– interactionist AI (Brooks, Chapman, Agre)– connectionism / neural networks– reinforcement learning

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

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”

(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

Progress?

• Unfortunately, all current learning techniques rely on the previous availability of explicitly represented knowledge– 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 our view, this is a good reason for a serious redesign of current

models of neural processing so that they can take advantage of previously learned knowledge. 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

The BaB objection

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

The argument from language as social comittment (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

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!

This leads to... The ”Build-a-Baby” (BaB) objection• So, 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 this is line of ”research” to be pursued, it raises

some serious ethical problems– C.f. movies ”A.I.” and ”Bladerunner”

• Is the goal of AI to build more humans?

Arguments related to evolution

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

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 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

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!

Conclusion

Summary

• Dreyfus et al has not proved conclusively that weak AI is impossible (which Dreyfus admits)

• However, they point out that – there are no reasons to believe it is possible,– there are reasons for believing it is not possible

• Specific problems are pointed out– indicating what we may need to do if we want to

achieve weak AI• Provides some interesting ideas which allow us

to think not only about AI...– ... but about human intelligence and behaviour