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Samenvatting introduction to AI
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Dit is een samenvatting voor het vak introduction to AI op UVT. Het wordt door eerstejaars bachelor studenten EN pre master studenten gevolgd.
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Comprehensive AI cheatsheet (part1)
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Comprhensive AI cheatsheet (part2)
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Comprehensive AI notes
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Samenvatting artificial intelligence.Hoorcollege 1.
Why study AI? The big AI questions:
- Can we build machines that think?
- Can we build machines that learn?
- Can we build machines that have emotions?
what is the limit of machinery?
Be very skeptical about what you hear in the press/news about AI.
Theodore Roszak says: “AI’s record of barefaced public deception is unparalleled in the annals of
academic study.”
AII researchers have been making bold claims since the late 1950s. Repeatedly, AI’s progress has
been much slower than expected. It’s easy to imagine full AI, hence easy to convince/scare people.
What is AI? Definitions from well-known people: AI is:
- “The science and engineering of making intelligent machines”(John McCarthy).
- “The science of making machines do things that would require intelligence done by men”
(Marvin Minsky).
What do we mean by artificial? If machine intelligence is artificial, what is “real” intelligence? Must
“real” intelligence be made from biological stuff?
- This is known as carbon/protoplasm chauvinism.
- What if we work out how to engineer biological agents?
Must “real” intelligence be the product of biological evolution?
- Should nature be the only source of “real” intelligence?
- What if we work out how to evolve biological agents?
So, we seem to mean two things:
- By artificial, we might mean non-biological. Here, it’s all about the kind of stuff we use to
build an agent.
- By artificial, we might mean constructed by humans. Here, it’s all about the origin of the
agent, and who designed and build it.
What do we mean by intelligence?
- “Intelligence is the computational parts of the ability to achieve goals in the world. Varying
kinds and degrees of intelligence occur in people, many animals and some machines.” (John
McCarthy).
In the context of AI, intelligent is best taken to mean “exhibiting interesting behavior”. Interesting
behavior can be found in ants, termites, fish and most of the other animals. But these animals are
not considered intelligent in the everyday sense of the world you don’t look at a fish and say “that
is an intelligent fish”.
An example of AI = Nigel the vacuum cleaner.
,What is the goal of AI? To answer that, we need to go back to the beginning. The beginning = when
the term AI came into existence. This was at the Dartmouth conference: the founding fathers of AI:
They found the first AI hypothesis:
“every aspect of learning or any other feature of intelligence can in principle be so precisely defined
that a machine can be made to simulate it”.
Since this conference, AI has grown massively and got quite diverse. So, AI has grown into a field of
study with many goals. The classic distinction in AI is the one between strong and weak AI.
So, strong AI is
A related distinction is AI as science and AI as engineering:
So, for AI as engineering,
you ignore the fact of human brains. For example, self-driving cars have nothing to do with human
,Artificial general intelligence and narrow artificial intelligence:
What does AI, as a
field of research, look like? All the big AI questions (as stated on the beginning of this lecture) are not
just questions for AI. How does AI relate to the other disciplines which are interested in these kinds
of questions how does AI fits with all the different kinds of disciplines?
AI is often situated as being a part of cognitive science.
Cognitive science is a collaborative/interdisciplinary field. Why?
- Understanding the mind/brain may be too difficult for a single discipline in isolation.
- Sharing of theories, methods, and results might help us move closer.
- Although often successful, this can be very challenging.
What binds this disciplines together? What makes a psychologist a cognitive scientist as well or what
makes a roboticist a cognitive scientist as well? cognitivism has a few definitions:
,In general: what is going on in our head when we are thinking?
Most AI research doesn’t tackle the big AI questions directly. That is because AI is very fragmented. It
begins with the big questions, and then people make a lot of different kind of decisions to attack
them.
So, even though these big AI questions is what motivates people, people are often focusing on very
specific problems.
Summary:
1. What is AI?
Artificial: computational agents, either robotics or software.
Intelligence: agents capable of achieving goals in the world.
2. What is the goal of AI?
Strong VS weak.
Science VS engineering.
General VS narrow.
3. What does AI, as a field of research, look like?
AI is closely allied to several disciplines.
AI includes a very wide, arguable fragmented, range of research areas.
AI has had its problems! Is it a myth? Can it deliver?
,Voorbereiding hoorcollege 2.
Chapter 1 R&N.
AI is to build intelligent entities.
1.1 What is AI?
Rationality = the system does the right thing, given what it knows.
A human centered approach (left) involve observations and hypothesis about human behavior. A
rationalist approach (right) involves a combination of mathematics and engineering.
Acting humanly: the Turing test approach was assigned to provide a satisfactory operational
definition of intelligence. A computer passes the test if a human interrogator cannot tell whether the
written response come from a person or from a computer. To pass, the computer will need to
process the following capabilities:
- Natural language processing to enable it to communicate in English.
- Knowledge representation to store what it knows and hear to answer questions and to draw
new conclusions.
- Machine learning to adapt to new circumstances and to detect and extrapolate patterns.
There is no physical interaction, because physical simulation is unnecessary for intelligence. The total
Turing test however includes a video signal. To pass, the computer will need:
- Computer vision to perceive objects.
- Robotics to manipulate objects and move around.
AI researchers have devoted little effort to passing the Turing test, believing that it is more important
to study the underlying principles of intelligence than to duplicate an exemplar.
Thinking humanly: the cognitive modeling approach: there are three ways to get inside the actual
workings of a human mind.
1. Through introspection: trying to catch our own thoughts as they go by.
2. Through psychological experiments: observing a person in action.
3. Through brain imaging: observing the brain in action.
,Once we have a sufficiently precise theory of the mind, it becomes possible to express the theory as a
computer program. The interdisciplinary field of cognitive science brings together computer models
form AI and experimental techniques from psychology to construct precise and testable theories of
the human mind.
Thinking rationally: the “laws of thought” approach. Aristotle was the first one to attempt codifying
“right thinking”. His syllogisms provided patterns for argument structures that always yielded correct
conclusions when given correct premises (Socrates is a man, all men are human, Socrates is human).
These laws of thoughts were supposed to govern the operation of the mind: their study initiated the
field called logic. The so called logics tradition within Ai hopes to build on such programs to creat3
intelligent systems. 2 obstacles:
1. It is not easy to fake informal knowledge and state it in the formal terms required by logical
notation.
2. There is a big difference between solving a problem “in principle” and solving it in practice.
Acting rationally: the rational agent approach: an agent is something that acts. A rational agent is one
that acts so as to achieve the best outcome, when there is uncertainty, the best expected outcome.
Making correct inferences is sometimes part of being a rational agent, because one way to act
rationally is to reason logically to the conclusion that a given action will achieve one’s goals and then
to act to that conclusion. On the other hand, correct inference is not all of rationality, in some
situations, there is no provably correct thing to do, but something must still be done. All the skills
needed for the Turing test also allow an agent to act rationally. Ration-agent approach has two
advantages over the other approaches:
1. It is more general than the law of thoughts, because correct inference is just one of several
possible mechanisms for achieving rationality.
2. It is more amenable to scientific development than are approaches based on human
behavior thoughts.
One thing to keep in mind: achieving perfect rationality (always doing the right thing) is not feasible
in complicated environments.
1.2 The foundations of AI.
AI has many approaches:
1. Philosophy:
Can formal rules be used to draw valid conclusions?
How does the mind arise from a physical brain?
Where does knowledge come from?
How does knowledge lead to action?
2. Mathematics:
What are the formal rules to draw valid conclusions?
What can be computed?
How do we reason with uncertain information?
3. Economics:
How should we make decisions so as to maximize pay off?
How should we do this when others may not go along?
How should we do this when the pay off may be far in the future?
4. Neuroscience:
, How do brains process information?
5. Psychology:
How do humans and animals think and act?
6. Computer engineering:
How can we build an efficient computer?
7. Control theory and cybernetics:
How can artifacts operate under their own control?
8. Linguistics:
How does language relate to thought?
1.3 The history of AI.
It is necessary for Ai to be an separate field because of two things:
1. AI embraced the idea of duplicating human facilities from the start. None of the other fields
were addressing these issues.
2. AI is the only one of these fields that is clearly a branch of computer science, and AI is the
only field to attempt to build machines that will function automatically in complex changing
environments.
What can AI do today? There are many activities in many subfields. A few applications:
- Robot vehicles
- Speech recognition
- Autonomous planning and scheduling
- Game playing
- Spam fighting
- Logistics planning
- Robotics
- Machine translation
Summary:
- Different people approach AI with different goals in mind. Two important questions to ask
are: are you concerned with thinking behavior? Do you want to model humans or work from
an ideal standard?
- Intelligence is concerned mainly with rational action. Ideally an intelligent agent takes the
best possible action in a situation.
- Philosophers made AI conceivable by considering the ideas that the mind is in some ways lie
a machine, that it operates on knowledge encoded in some internal language, and that
thought can be used to choose what actions to take.
- Mathematicians provided the tools to manipulate statements of logical certainty as well as
uncertain, probabilistic statements. They also set the ground work for understanding
computation and reasoning about algorithms.
- Economists formalized the problem of making decisions that maximize the expected
outcome to the decision maker.
- Psychologists adopted the idea that humans and animals can be considered information
processing machines. Linguists showed that language use fits into this model.
, - Computer engineers provided the evermore powerful machines that make AI applications
possible.
- Control theory deals with designing devices that act optimally on the basis of feedback from
the environment. Initially, mathematical tools of control theory were quite different from AI,
nut the fields are coming closer together.
- The history of AI has had cycles of success, misplaced optimism, and resulting outback’s in
enthusiasm and funding. There have also been cycles of introducing new creative approaches
and systematically refining the best ones.
- AI has advanced more rapidly in the past decade because of greater use of the scientific
method in experimenting with and comparing approaches.
- Recent progress in understanding the theoretical basis for intelligence has gone hand in hand
with improvements in the capabilities of real systems. The subfields of AI have become more
integrated, and AI has found common ground with other disciplines.
Hoorcollege 2: cognition and computation.
What AI differs from cognitive science = we try to build stuff by AI engineer something that works.
What is going on inside our heads?
We don’t know what is going on inside our brain! We can’t see the
black box. We can speculate what is going on, but we can’t see it.
For example the input is the environment and the output is the
behavior of someone. Throughout history scientist have claimed
that the activity going on inside our heads is mechanical. What is
mechanical is what we mean by mechanical in that year/day. So,
for example during the renaissance, it was thought that this
mechanical activity resembled clockwork device, and later on, a
steam engine. Within the last century, the metaphor of the
telephone exchange has been invoked. What we mean by
mechanical, we have to look into history.
The computer metaphor: “the last metaphor, the metaphor of
now: it need never be supplanted”. What the computer metaphor
means is, that the brain = hardware (physical device) the mind is
like the software because it requires the physical device to
operate, but in itself is not material since it has no mass.
Where does this idea of computer metaphor come from? Let’s
- Age of enlightment (1700-1800, Leibniz): thought was seen as reasoned calculation, even
moral questions could be solved by calculating universal calculus. He believed that once
we discovered this universal calculus any kind of question we want to be answered can then
be answered by calculating.
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