3. ARTIFICIAL INTELLIGENCE
The Term A.I. belongs to a Fifth Generation Computer System
In which the System works in the same manner as human-being.
In another sense,
we can term it
As the study and design of
Intelligent Agents.
4. The field of AI research was founded at a conference on
the campus of Dartmouth College in the summer of 1956.
The attendees, including John McCarthy, Marvin
Minsky, Allen Newell and Herbert Simon, became the
leaders of AI research for many decades. They and their
students wrote programs that were, to most people,
simply astonishing: computers were solving word
problems in algebra, proving logical theorems and
speaking English.
5. By the middle of the 1960s, research in the U.S. was
heavily funded by the Department of Defense and
laboratories had been established around the world. AI's
founders were profoundly optimistic about the future of the
new field: Herbert Simon predicted that "machines will be
capable, within twenty years, of doing any work a man can
do" and Marvin Minsky agreed, writing that "within a
generation ... the problem of creating 'artificial intelligence'
will substantially be solved".
6. In 1974, in response to the criticism of England's Sir
James Lighthill and ongoing pressure from Congress to
fund more productive projects, the U.S. and British
governments cut off all undirected, exploratory research
in AI. The next few years, when funding for projects
was hard to find, would later be called an "AI winter".
7. The most difficult problems in knowledge representation are:
•Default Reasoning
•Qualification Problem
Knowledge representation and knowledge engineering are central to
AI research. Many of the problems machines are expected to solve
will require extensive knowledge about the world. Among the things
that AI needs to represent are: objects, properties, categories and
relations between objects; situations, events, states and time; causes
and effects; knowledge about knowledge (what we know about what
other people know); and many other, less well researched domains.
A complete representation of "what exists" is an ontology (borrowing
a word from traditional philosophy), of which the most general are
called upper ontologies.
8. Natural language processing gives machines the ability to
read and understand the languages that humans speak.
Many researchers hope that a sufficiently
powerful natural language processing
system would be able to acquire knowledge
on its own, by reading the existing text
available over the internet. Some
straightforward applications of natural
language processing include information
retrieval (or text mining) and machine
translation.
9. Emotion and social skills play two roles for an intelligent
agent.
• First, it must be able to predict the actions of others, by
understanding their motives and emotional states. (This
involves elements of game theory, decision theory, as well
as the ability to model human emotions and the
perceptual skills to detect emotions.)
• Also, for good human-computer interaction, an intelligent
machine also needs to display emotions. At the very least
it must appear polite and sensitive to the humans it
interacts with. At best, it should have normal emotions
itself
• Example is Kismet, a robot with rudimentary social skills
10. TOPIO, a robot that can
play table tennis,
A sub-field of AI addresses developed by TOSY.
creativity both theoretically
(from a philosophical
And psychological
perspective)
And practically
(via specific
implementations
of systems that
generate outputs
that can be
considered creative)
11. Artificial neurons
Neurons work by processing information. They receive and provide information
in form of spikes.
x1
w1
x2
x3 w2 Output
n
… z wi xi ; y H ( z)
Inputs
w3 i 1 y
xn-1 .
. w
xn . n-1
wn
The McCullogh-Pitts model
12. Artificial neurons
The McCullogh-Pitts model:
• spikes are interpreted as spike rates;
• synaptic strength are translated as synaptic weights;
• excitation means positive product between the
incoming spike rate and the corresponding synaptic
weight;
• inhibition means negative product between the
incoming spike rate and the corresponding synaptic
weight;
13. Summary
• Artificial
neural networks are inspired by the learning
processes that take place in biological systems.
• Artificial neurons and neural networks try to imitate the
working mechanisms of their biological counterparts.
• Learning can be perceived as an optimisation process.
• Biological neural learning happens by the modification
of the synaptic strength. Artificial neural networks learn
in the same way.
• The synapse strength modification rules for artificial
neural networks can be derived by applying mathematical
optimisation methods.
14. Summary
• Learning tasks of artificial neural networks can be
reformulated as function approximation tasks.
• Neural networks can be considered as nonlinear function
approximating tools (i.e., linear combinations of nonlinear
basis functions), where the parameters of the networks
should be found by applying optimisation methods.
• The optimisation is done with respect to the approximation
error measure.
• In general it is enough to have a single hidden layer neural
network to learn the approximation of a nonlinear function.
In such cases general optimisation can be applied to find the
change rules for the synaptic weights.
15. Learning is acquiring new, or modifying
existing, knowledge, behaviors, skills, values, or preferences and may involve
synthesizing different types of information.
The ability to learn is possessed by humans, animals and some machines.
Progress over time tends to follow learning curves.
Human learning may occur as part of education, personal development,
schooling, or training.
There is evidence for human behavioral learning prenatally, in which
habituation has been observed as early as 32 weeks into gestation, indicating
that the central nervous system is sufficiently developed and primed for
learning and memory to occur very early on in development.
16. Supervised Learning
• It is based on a
labeled training set. Class
• The class of each
Class
A
piece of data in B Class
training set is known.
Class
B
• Class labels are pre-
A
determined and A Class
provided in the Class B
training phase.
17. Unsupervised Learning
• Input : set of patterns P, from n-dimensional space S, but
little/no information about their classification, evaluation,
interesting features, etc.
It must learn these by itself! : )
• Tasks:
– Clustering - Group patterns based on similarity
– Vector Quantization - Fully divide up S into a small set of
regions (defined by codebook vectors) that also helps
cluster P.
– Feature Extraction - Reduce dimensionality of S by
removing unimportant features (i.e. those that do not help
in clustering P)
19. Learning from Experience Plays a Role in
…
Artificial Intelligence
Control Theory and
Operations Research
Psychology
Reinforcement
Learning (RL)
Neuroscience
Artificial Neural Networks
21. Backpropagation Algorithm
• Two phases of computation:
– Forward pass: run the NN and compute the error for each
neuron of the output layer.
– Backward pass: start at the output layer, and pass the errors
backwards through the network, layer by layer, by
recursively computing the local gradient of each neuron.
21
22. Delta Rule
• Functions more like nonlinear parameter fitting - the goal is to
exactly reproduce the output, Y, by incremental methods.
• Thus, weights will not grow without bound unless learning
rate is too high.
• Learning rate is determined by modeler - it constrains the size
of the weight changes.
23. CHARACTERISTICS OF AI &ANN
In the field of robotic minimally invasive surgery, it is apparent that advances
in technology have conferred increased precision during—and the decreased risk
of complications after—a wide range of surgical procedures.
Patients who are operated on by robots controlled by surgeons enjoy shorter
recovery times and fewer visible post-operative scars than those subject to
traditional open-surgical procedures.
With advances in robotic technology in the operating room, though, the
surgeon's hand is no longer the driving force behind the scalpel.
24. THE POTENTIAL AND THE PROCESS
• In recent years, a great deal of effort has been devoted to
development of methodologies for cancer therapy.
• Among them; Heavy Iron therapy is highly under attention.
• We fixed an ultrasonic diagnosis device on the top of a robot
arm and tracked the cancer which was moved on a monitor
with respiration.
• By using the neural network, it became possible to track the
cancer automatically.
25. THE BASIC WORKING CONDITION FOR
CANCER DIAGNOSIS
• Neural networks provide a unique computing archi-tecture
whose potential has only began to be tapped, Used to address
problems that are intractable or cum-bersome with traditional
methods, these new comput-ing architectures, inspired by the
structure of the brain.
• Artificial neural networks take their name from the networks
of nerve cells in the brain.
26. • In a neural network each neuron is linked to many of its
neighbors (typically hundreds or thousands) so that there are
many more interconnects than neurons.
• The power of the neural network lies in the tremendous number
of interconnections.
• The neuron performs a weighted sum on the inputs and uses a
nonlinear threshold function to compute its output.
• The calculated result is sent along the output connections to the
target cell.
27. RESPIRATION
• The respiration information which is fed into the de-signed
neural network as input is a waveform obtained by a strain
gage in of line.
• We fixed the strain gauge around the abdomen and sensor
expanded and contracting by abdominal movement, the
changes is taken as respiration information.
• Then, the informa-tion of respiration (both amplitude and
differential at period ) are fed into the neural network.
28. ROBOT ARM
• The robot arm used in this study is a multi-joint manipulator
with 6 degree-of-freedom.
• Flappers which show the coordinates of diagnosis device
position are x, y, z and the rotation.
• In simulation, since we controlled it in Y axis direction, the
variable parameter is y' only and another parameters (in this
case X,Z axis) are consistant.
30. SIMULATION
• The displacement prediction network and inverse kinematics
networks were obtained by using a neural network simulator
developed by University of Toronto called "Xenon".
• We combined two networks (combination network), and it
gave its output to the robot arm input.
• Using the trained network and off line respiration data, we
controlled the robot arm in one din-tension automatically.
