Artificial Intelligence 1 2

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         <p> AI is the science of making machines do things that would
require intelligence if done by men. It includes reasoning, learning,
planning, speech recognition, vision, and language understanding. These
machines are being used today in a wide variety of applications, such as
monitoring credit card fraud, making autonomous decisions on space
missions, watching for attacks from computer network hackers, diagnosing
faults in aircraft, enabling human–machine speech interfaces, and
making the characters in a video game behave in a more human-like
way.<br>

      The main unifying theme is the idea of an intelligent agent. We
define the AI as the study of agents that receive percepts from the
environment and perform actions. Each such agent implements a function
that maps percepts sequences to actions, and we cover different ways to
represent these functions, such as real-time conditional planners, neural
networks, and decision theoretic systems. We treat robotics and vision as
occurring in the service of achieving goals. <br>

Eliza, the program was able to converse about any subject, because it
stored subject information in data banks. <br>

<br>

Keywords: Turing test – Intelligent agents - Neural Networks –
Genetic Programming – Planning – Fuzzy Logic – Robotics – Pattern
recognition - Natural language processing – Deep blue - Eliza - video
clip.<br>

<br>

<br>

                      Index:<br>

<br>

       Introduction                                           <br>

       Turing test<br>

       Classification<br>

       Intelligent agents <br>

       What belongs to AI?<br>

       Applications   <br>

       Chess and AI<br>

       Computer Vs. Human Brain<br>
        Fuzzy-Logic and AI<br>

        Eliza <br>

        Conclusion (AI present & future)<br>

        References <br>

<br>

<br>

<br>

1. Introduction<br>

 <br>

<br>

After WWII, a number of people independently started to work on
intelligent machines. The English mathematician Alan Turing gave a
lecture on it in 1947. I would compare attempts to create AI with man's
historical attempts at flight. <br>

<br>

2. Turing test (Article of Computational Intelligence: 1950) <br>

He argued that if the machine could successfully pretend to be human to a
knowledgeable observer then you certainly should consider it intelligent.
The observer could interact with the machine and a human by teletype (to
avoid requiring that the machine imitate the appearance or voice of the
person), and the human would try to persuade the observer that it was
human and the machine would try to fool the observer. <br>

<br>

3. Classification<br>

 This is a discipline with two strands: science and engineering. The
scientific strand attempts to understand the requirements for, and
mechanisms enabling, intelligence of various kinds in humans, other
animals and information processing machines and robots. The engineering
strand attempts to apply such knowledge in designing useful new kinds of
machines and helping us to deal more effectively with natural
intelligence, e.g. in education and therapy. <br>

Bottom-up theorists believe the best way to achieve artificial
intelligence is to build electronic replicas of the human brain's complex
network of neurons, while the top-down approach attempts to mimic the
brain's behavior with computer programs. Moreover there is a lot of
difference between the AI simulated system and a program with the large
database. This is discussed later on under the topic of chess-AI.<br>
<br>

4. Intelligent Agents <br>

<br>

An agent is anything that can be viewed as perceiving its environment
through sensors and acting upon that environment through actuators. An
agent’s percept sequence is the complete history of everything the
agent has ever perceived.<br>

A rational agent is one that does the right thing. A Performance measures
embodies the criterion of a success of an agent’s behavior. When an
agent is plunked down in an environment, it generates a sequence of
actions according to the percepts it receives.<br>

<br>

The Nature Of Environments <br>

<br>

Specifying environment (PEAS – Performance, Environment, Actuators,
Sensors) under the heading of the task environment we group the
performance measure, the environment, and the agent’s actuators and
sensors. <br>

E.g., an automated taxi driver.<br>

<br>

Agent Type Performance<br>

Measure    Environment Actuators   Sensors<br>

<br>

Taxi driver      Safe, fast, legal, comfortable trip, maximize profits
      Roads, other traffic, pedestrians, customers Steering,
accelerator, brake, signal, horn, display     Cameras, sonar,
speedometer, GPS, odometer, accelerometer, engine sensors, keyboard<br>

<br>

It is better to design performance measures according to what one wants
in the environment, rather than according to how one think the agent
should behave<br>

More specifically, the learning ability needs to be autonomous, goal-
directed, and highly adaptive:<br>

      Autonomous -- Learning occurs both automatically, through exposure
to sense data (unsupervised), and through bi-directional interaction with
the environment, including exploration/ experimentation (self-
supervised).<br>

       Goal-directed – Learning is directed (autonomously) towards
achieving varying and novel goals and sub-goals -- be they ‘hard-
wired’, externally specified, or self-generated. Goal-directedness also
implies very selective learning and data acquisition (from a massively
data-rich, noisy, complex environment).<br>

       Adaptive – Learning is cumulative, integrative, and contextual
and adjusts to changing goals and environments. General adaptivity not
only copes with gradual changes, but also seeds and facilitates the
acquisition of totally novel abilities.<br>

<br>

5. What Belongs to Artificial Intelligence<br>

<br>

        Neural Networks<br>

Artificial Neural Networks, often just called Neural Networks (NN), are
modelled on the human brain. The internal structure of the network,
composed of a small number of artificial neurons, implies that the
information learnt is not perfect. Artificial Neural Networks have been
used successfully in visual pattern recognition, even human faces and
complex industrial components can be differentiated. Artificial Neural
Networks have been used in speech recognition system to decipher audible
language.The technique used is that of a highly parallel network of
simple processing elements. Each element has some similarity with animal
nerve or brain cells called neurons<br>

       <br>

        Genetic Programming<br>

      Genetic programming is an excellent way of evolving algorithms that
will map data to a given result when no set formula is known.
Mathematicians/programmers could normally find algorithms to deal with a
problem with 5 or so variables, but when the problem increases to 10, 20,
50 variables the problem becomes close to impossible to solve. Briefly,
how a GP-powered program works is that a series of randomly generated
expression trees are generated that represent various formulas. These
trees are then tested against the data, poor ones discarded, good ones
kept and breed. Mutation, crossover, and all of the elements in genetic
algorithms are used to breed the 'highest-fitness' tree for the given
problem. At best, this will perfectly match the variables to the answer,
other times it will generate an answer very close to the wanted
answer.<br>

<br>

        Planning, problem solving, automatic design:<br>
Planning involves finding a sequence of actions that can lead from the
current state, to the goal state. Given a complex problem and a
collection of resources, constraints and evaluation criteria create a
solution which meets the constraints and does well or is optimal
according to the criteria, or if that cannot be done propose some good
alternatives. <br>

<br>

       Machine Learning<br>

Machine learning is becoming increasingly popular, and equally important.
People realise that it is theoretically much easier to get a machine to
learn something from facts, rather than have to spend time teaching it
explicitly. The quality of the learning algorithm is of course a major
factor!<br>

<br>

       Constraint Satisfaction<br>

Here, the problem is modeled as a set of variables, which can be assigned
particular values. Different types of constraints are set-up on these
variables (equality, numerical constraints), in order to specify the
requirements for the problem. A search is then performed on the
variables, in order to find the potential solutions. There are many nifty
tricks involved to partly resolve constraints in order to guide the
search more efficiently (this is called a heuristic search). The problems
solved can also be a combinatorial optimisation, where a particular
solution has a better value than another, and the best needs to be found.
The class of problems usually solved is NP-complete, where the complexity
increases exponentially as the problem size increases linearly.<br>

<br>

       Search and Optimization<br>

There are many kinds of searches, the simplest of which involve trying
out all the solutions in a particular order. The entire set of possible
solutions is called the search space.<br>

<br>

       Decision Tree Learning<br>

A decision tree is a structure that allows learning of opinions (e.g.
good or bad) about objects based on their attributes (length, colour…).
Given a series of examples, the learning algorithm can build a decision
tree that will be able of classifying new examples. If the new examples
are handled correctly, nothing is done. Otherwise, the structure of the
tree is modified until the correct results are displayed. The challenge
is getting the algorithm to perform well on very large sets of data,
handling errors in values (noise), and determining the optimal fit of the
tree to the training and test data.<br>

<br>

       Data Mining<br>

This is the process of extracting useful rules from very large sets of
data. Data Mining is a term used to describe the process whereby software
tools examine a company data base in order to locate information which
may have complex parameter connectivity. Such information would normally
be inaccessible to the human expert due to the enormous quantity of data
and combinatorial tests which would have to be performed. A simple
example may be a data base of company products and parameters which
describe their applicability to various sectors of the market. <br>

<br>

       Bayesian Networks<br>

Bayesian Networks models the relationship between variables. This is
called conditional dependence: a state of a variable may depend on many
others. This can be represented as a graph, and there's a clever
algorithm to estimate the probability of unknown events given existing
knowledge. Admittedly, one common complaint against this approach relates
to the design; it can be very tedious to model such networks. As such,
learning the structure and the inference between variables seems like an
appealing option.<br>

       Artificial Life<br>

Artificial Life (A-Life) is the study of artificial or computer based
systems, which exhibit life like behavior. Computer simulations of
individual agents or populations of agents can be used to investigate
many of the properties of living systems. In some cases, mechanically
constructed agents are provided with basic functionality and allowed to
interact with real environments. This is a very popular aspect of
Artificial Intelligence, which involves modeling and mimicking living
systems. This includes anthills, wasp nests, larger forests, towns and
cities. To date, very complex and interesting systems have been created
by a multitude of very simple entities. For example many ants programmed
by very small programs would potentially create an entire system with
signs of emergent intelligence.<br>

6. Applications<br>

<br>

       Robotics<br>

The main aspect of robotics today is mobility. This can be done by
learning the task in a virtual simulation, and then applying it to the
real robot. If specific conditions of training are respected, the problem
has a high probability of working in real life, but this is no guarantee.
In practice when moving robotic arms, the arm has a few movement
possibilities: the shoulder allows rotations according to two axes, and
the elbow also allows two basic rotations. Each of these possibilities is
called one degree of freedom. Usually, one controller is assigned to
provide movement for one DOF. The task at hand is to learn the optimal
combination of controllers, where they can successfully cooperate to
perform a given task.<br>

       Pattern Recognition<br>

Pattern recognition involves determining the characteristics in specific
samples and sorting them into classes; a process called classification.
This is usually done with Machine Learning techniques, allowing the
system to adapt to the data given to it. It can be applied to detecting
single words in speech, recognizing voices, sorting scanned objects by
type and filtering out unwanted pictures (among many others). In
practice, a way of doing this is to represent the sample as a set of
features (e.g. for a sound: pitch, volume, timbre, smoothness). A
training set is then created: i.e. a set of samples where the result is
known (e.g. for facial recognition: Fred has green eyes and brown hair,
Henry has blue eyes and blond hair). The learning mechanism can then
learn to associate the features with the known types of sound or image.
Depending on the representation, more or less samples are required. With
symbolic representations, small numbers of examples are usually required,
whereas for fuzzy learning (in neural networks for example) larger
training sets are needed.<br>

<br>

       Natural language processing<br>

      It includes production and interpretation of spoken and written
language, whether hand-written, printed, or electronic throughout (e.g.
email). One of the first approaches was symbolic, assigning semantic
meaning to each word (verb, noun, adjective). The basic structure of
valid sentences would have to be defined manually, and a search would be
performed to match the template with the current sentence. A lot of time
needed to be spent resolving ambiguous sentences, and getting the person
and tenses of the verbs to match. If the programmer spends enough time
creating the sentence templates, the results would be fairly encouraging.
But this monotonous task needs to be repeated for new sentence constructs
and new languages all together.<br>

A very recent approach is to use statistical analysis of the text. In
essence, large parts of books are processed and learning algorithms
attempt to extract the rules and patterns. This requires a smarter
approach, taking more time to design, but it results in a more flexible
program.<br>

<br>

       Frames<br>
On method that many programs use to represent knowledge are frames.
Pioneered by Marvin Minsky, frame theory revolves around packets of
information. For example, say the situation was a birthday party. A
computer could call on its birthday frame, and use the information
contained in the frame, to apply to the situation. The computer knows
that there is usually cake and presents because of the information
contained in the knowledge frame. Frames can also overlap, or contain
sub-frames. The use of frames also allows the computer to add knowledge.
Although not embraced by all AI developers, frames have been used in
comprehension programs such as Sam.<br>

<br>

       AI in medicine, including interpretation of medical images,
diagnosis, expert systems to aid GPs, monitoring and control in intensive
care units, design of prosthetics, design of drugs, intelligent tutoring
systems for various aspects of medicine. <br>

      AI in many aspects of engineering: fault diagnosis, intelligent
control systems, intelligent manufacturing systems, intelligent design
aids, integrated systems for sales, design, production, maintenance,
expert configuration tools (e.g. ensuring sales staff don't sell system
that won't work). AI in software engineering includes work on program
synthesis, verification, debugging, testing and monitoring of software.
<br>

       AI in education: including various kinds of intelligent tutoring
systems and student management systems. Particular applications might
include diagnosis of a student's knowledge gaps, various kinds of drill
and practice tutors, automatic marking of programming assignments and
essays, etc. <br>

      AI in entertainment: increasingly AI is being used in computer
games and in systems for generating and controlling synthetic characters
either for textual interaction or generating films with cartoon
characters or interactive avatars in virtual worlds. <br>

      AI in biology: there are many hard problems in biology where more
or less intelligent computer-based systems are being developed, e.g.
analysis of DNA, prediction of folded structure of complex molecules,
prediction, modeling many biological processes, evolution, development of
embryos, behaviors of various organisms. <br>

      Architecture, urban design, traffic management: tools to help solve
design problems involving multiple constraints, helping to predict the
behaviors of people in new environments, tools to analyze patterns in
observed phenomena. <br>

      Literature, art and music: identification of authors, modeling of
processes of generation and appreciation, teaching applications. <br>

      Crime prevention and detection: e.g. detection of forgeries,
learning to detect evidence of crooked police officers, software to
monitor internet transactions, helping to plan police operations,
searching police databases for evidence that crimes are committed by the
same person, etc. <br>

         Space: control of space vehicles and autonomous robots too far
from earth to be directly manipulated by humans on earth, because of
transmission delays. <br>

      Military activities: Various   paradigms in AI have been successfully
applied in the military field. For   example, using an EA (evolutionary
algorithm) to evolve algorithms to   detect targets given radar/FLIR data,
or neural networks differentiating   between mines and rocks given sonar
data in a submarine.<br>

<br>

7. Chess and AI<br>

<br>

Deep Blue does not use AI. Then how is AI – deep blue related?<br>

AI-based game playing programs combine intelligence with entertainment.
World-champion chess playing programs can see ahead twenty plus moves in
advance for each move they make. In addition, the programs have an
ability to get progressably better over time because of the ability to
learn. Chess programs do not play chess as humans do. In three minutes,
Deep Thought (a master program) considers 126 million moves, while human
chessmaster on average considers less than 2 moves. The next move comes
from exhaustive searches into all moves, and the consequences of the
moves based on prior learning. Chess programs, running on Cray super
computers have attained a rating of 2600 (senior master), in the range of
Gary Kasparov, the Russian world champion. <br>

DEEP BLUE: First of all, this year Deep Blue will be running on a faster
system - the latest version of the SP -, which uses 30 P2SC or Power Two
Super Chip processors. Last year, Deep Blue averaged about 100 million
chess positions per second. This year Deep Blue will work about twice as
quickly - that is, 200 million chess positions per second. Incidentally,
Garry Kasparov can evaluate approximately three positions per second.<br>

<br>

8. Fuzzy Logic and AI<br>

It is often said that computers are too logical and that they can only
deal in true or false, yes or no etc. However, Fuzzy Logic allows a
computer to deal in everyday human language and actually process terms
such as probably, unlikely, quite near etc. Such terms can take their
place in computations, allowing the computer to arrive at verifiable
results from fuzzy inputs. Another type of fuzzy information is stored in
the famous Neural Networks. This is known as a neuro-fuzziness. The
information inside an artificial neural network is usually imprecise, due
to the weighted connection between neurons (called synapses).<br>
Fuzzy representations have increased in popularity, due to the increased
capabilities of computers: more processing power is usually required to
create such rules, and interpreting them generally also requires a bit
more time. The preferred languages for this type of representation are
usually procedural like C, C++ or Pascal.<br>

<br>

9. Brain Vs. Computer<br>

<br>

A collection of simple cells can lead to thought, action, and
consciousness or, in other words, that brains cause minds. Even thought a
computer is a million times faster in raw switching speed, the brain ends
up being 100,000 times faster at what it does.<br>

<br>

       Computer    Human brain<br>

Computational units       1 CPU, 108 gates 1011 neurons<br>

Storage units      1010       BITS RAM 1011 neurons <br>

       1011   bits disk   1014 synapses <br>

Cycle time 10-9 sec       10-3 sec<br>

Bandwidth     1010 bits/sec      1014 bits/sec<br>

Memory updates/sec        109    1014<br>

<br>

10. ELIZA<br>

Eliza, Joseph Wiezbaum's result of trying to make a program converse in
English amazed people when it appeared in mid 1960's. The program was
able to converse about any subject, because it stored subject information
in data banks. Another feature of Eliza was its ability it picked up
speech patterns<br>

Conclusions:<br>

<br>

Finally, we come up with many conclusions with regards to future and
present of Artificial Intelligence. AI is fascinating, and intelligent
computers are clearly more useful than unintelligent computers, so why
worry? AI has made possible new applications such as speech recognition
systems, inventory control systems, surveillance systems, robots and
search engines.<br>
Finally, it seems likely that a large-scale success in AI—the creation
of human-level intelligence and beyond—would change the lives of
majority of the humankind. The very nature of our work and play would be
altered, as would our view of intelligence, consciousness, and the future
of destiny of the human race. At this level, AI systems could pose a more
direct threat to human autonomy, freedom, and even survival. After all
silicon is cheaper than human life.<br>

In conclusion, we see that AI has made great progress in its short
history, but the final sentence of Alan Turing’s essay on Computing
Machinery and Intelligence is still valid today:<br>

“We can see only a short distance ahead, but we can see that much
remains to be done.―<br>

.<br>

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