Download Document

Introduction* (Symbolic) A. I.
Artificial Intelligence
Aims A.I:
If we can “make”/design intelligence, we can:
Practical
1). Build incredibly powerful technology
Scientific
2). Understand intelligence
•Igor Aleksander & Piers Burnett (1987): “Thinking machines: the search for artificial
intelligence”. Oxford University Press, Oxford.
PROBLEM:
How do we know that we designed
something "intelligent“ ?
Definition-problem
Intelligence:
Something to do with “understanding”
But how do you know that
“something is understood”
by someone other than yourself
?
Performance of
“intelligent behaviour”
What about a machine that behaves AS IF it is intelligent?
Critical reply: That “intelligence” reflects the design of
its creator (machines: the engineer
animals: God or Genes)
• Refute: Then ants have a mind: understand situation
and consciously solve problems
• Accept: Animals are dumb machines and just follow
genetically programmed instruction
Where do we draw the line?
Nest building in birds; beavers making a dam,
we building a house?
IF behaviour of machines/animals has nothing to do with
intelligence
How then should a truly intelligent “entity”
understand ?
In the same way as we
But how do we understand?
Is our intelligence a sufficient basis for understanding
intelligence ?
Is the brain capable of providing an explanation for itself?
“Intelligent Behaviour”
Problem Solving
Is a thermostat intelligent ?
“Solves” the “problem” of temperature regulation
but does it have a “mind”?
Does it use Knowledge
and
Can machines (animals) do
this?
In itself insufficient.
Psychology:
knowledge-independent tests
(“G”: IQ)
Reasoning
Relation between “Mind”,
“Knowledge” and “Reasoning”
goes back to Greek philosophy
Parmenides of Elea (5th century BC)
Power of Reason as seat of Knowledge, instead
of sensory perception
“That what can be thought is identical to that what is”
The universe is ordered following laws of reason
Illusory!
The only science is about that what IS
(“ontogeny”)
“Truth” is that what always IS
Unchangeable
Being instead of Becoming
Static instead of Dynamic
A stick put partly
under water looks
broken, but isn’t
Knowledge is beyond
direct physical experience:
META-PHYSICS
After “naïve realism”:
DOUBT
Human mind discovers that physical experience
is insufficient to explain the “reality”
Metaphysics: thinking about “being” beyond perception
Two observers “see” one and the same oak in a different way.
However, both agree about what they see is an oak
The “objective Oak-in-itself”
The oak we see is an instantiation of the “object oak”
Which in turn belongs to the “class” of “trees”
perfect,
unchangeable
Plato (427-347 BC).
What we see are imperfect projections of ideal
intelligible objects.
An individual tree as we perceive it is non-generic
and cannot be defined, but the ideal “tree” can!
How to study the world of ideas?
When reason is the principled way of knowing (meta physics),
then we should study the rules of reasoning
Aristotle (384-322 BC)
Formal Logic
Later: decoupled
from platonic idealism
Tool that results in knowledge about
that what is
Parmenides: Don’t believe your eyes, but:
What one thinks, is
(one cannot think about something that is not)
First truth: It is
Things can be known only when they are
Descartes: Starts from the subject instead of the object
To find truth: Whatever could be doubted should be rejected
What remains: something that doubts (me)
What is undeniable in thinking is
(one cannot think when one is not)
Cogito ergo sum
I am
Because of doubt, Descartes does not accept the obviousness
of his own senses
Still meta-physics! But focus is on epistemology instead of
on ontology
The correct way to obtain
knowledge (by reasoning = ratio)
Reasoning is beyond perception:
Mind-Body Dualism
What did Descartes think about behaviour ?
1) If automatons had the shape of animals*,
we should have no means of knowing that they
did not possess the same nature as animals
2) If automatons perfectly imitated actions of
animals*, we would be in no doubt that animals
were automatons too
* that lack reason
Can machines be intelligent ?
Behaviour can be understood mechanistically
Turing (1950)
If a computer perfectly imitated answers of
humans, we should have no means of knowing that it
did not possess the same intelligence as humans
Intelligence can be understood as computation
BUT: When a computer “does” something in the way we do it,
does it also understand what it is doing in the same way
as we do?
Daniel Dennett: If a computer behaves as if it tries to win a game
of chess, it is meaningless to ask whether it really
wants to win
“Intentional Stance”
John Searle (1980, 1987): The Chinese Room
Give an English person a Chinese story + detailed
instructions in English how to manipulate the characters,
she will provide answers in Chinese characters about the
story (even when she doesn’t understand a WORD of it!!)
Intentionality: “Knowing what it is about”
Allows Empathy: words recall visions and feelings
How to describe unknown, newly encountered things
without referring to known objects?
Intentionality is based on the ability to build
internal representations
Sensory
Perception
Do Machines need Complicated Sensors to build
Internal Representations
?
A.I.:
NO
Pre-processed versions of real world manifestations suffice
Just tell the machine what it needs to know
to carry out its task
We can plan a trip (to an unknown area)
by just using a map
A “mental map” suffices
SYMBOLS
&
SYMBOL
PROCESSING
… but then you need the ability to interpret symbols
AND: only in a very limited number of cases you can
“pre-pack reality”
in “Models”
and use these to execute meaningful behaviour
f.i. mathematical equations
Relationships between symbols
to represent (in)equalities, functions etcetera
From classical (Newtonian) Kinematics
st + t
st


st + t – st = s
t
s0
CHANGE in Distance =
st + t – st = s
CHANGE/ Unit Time =
t
s
 tg  cons tan t
t
t + t
For very small t (t  0 = dt):
lim t  0
s ds( t )

 s' ( t )  s  cons tan t = velocity
t
dt
Straight line equation for the graph above: s(t) = vt
constant
v(t)
v(t) = a.t + v0 (2)


Similar as to s(t):
v0
t
lim
t 0
v t   t  v t dv ( t )

 v ( t )  v
t
dt
acceleration
ARE WE REALLY DOING THIS
IN OUR HEAD
WHEN ACCELERATING OUR CAR?
s(t) is the surface under the curve =  v(t)dt
v(t)
v0
= surface under triangular part = height  ½ basis
= [v(t) – v0] ½ t
= [(at + v0) – v0] ½ t = ½ at2
PLUS
t surface of rectangular part = v0t
+
s(t) = v0t + ½
at2
Even a mathematician doesn’t
solve equations when playing
tennis
When a child catches a ball it is NOT solving equations
It learns to do this by:
• better muscle control
• improved motor abilities
• experience
Induction instead of Deduction
Now try a computer (or a robot running on software)
getting this done …
“Problem Solving”? But NOT by Computation!