Methods used to create intelligence.
- Neural Networks
- The structure of Neurons
- Binary Neuron Theory
- Boole and Logic
- Brain Circuits and parallel computation
- Impressive results and Conclusion
- Problem Solving
- Expert systems
- Frames and Knowledge representation
IntroductionIn the quest to create intelligent machines, the field of
Artificial Intelligence has split into several different approaches based on the
opinions about the most promising methods and theories. These rivaling theories
have lead researchers in one of two basic approaches; bottom-up and top-down.
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
Neural Networks and Parallel ComputationThe human brain is
made up of a web of billions of cells called neurons, and understanding its
complexities is seen as one of the last frontiers in scientific research. It is
the aim of AI researchers who prefer this bottom-up approach to construct
electronic circuits that act as neurons do in the human brain. Although much of
the working of the brain remains unknown, the complex network of neurons is what
gives humans intelligent characteristics. By itself, a neuron is not
intelligent, but when grouped together, neurons are able to pass electrical
signals through networks.
The neuron "firing", passing a signal to the next in the
Research has shown that a signal received by a neuron
travels through the dendrite region, and down the axon. Separating nerve cells
is a gap called the synapse. In order for the signal to be transferred to the next neuron, the signal must
be converted from electrical to chemical energy. The signal can then be received
by the next neuron and processed.
Warren McCulloch after completing medical school at Yale, along with Walter
Pitts a mathematician proposed a hypothesis to explain the fundamentals of how
neural networks made the brain work. Based on experiments with neurons,
McCulloch and Pitts showed that neurons might be considered devices for
processing binary numbers. An important back of mathematic logic, binary numbers
(represented as 1's and 0's or true and false) were also the basis of the
electronic computer. This link is the basis of computer-simulated neural
networks, also know as Parallel computing.
A century earlier the true / false nature of binary numbers
was theorized in 1854 by George Boole in his postulates concerning the Laws of
Thought. Boole's principles make up what is known as Boolean algebra, the
collection of logic concerning AND, OR, NOT operands. For example
according to the Laws of thought the statement: (for this example consider all
Boole also assumed that the human mind
works according to these laws, it performs logical operations that could be
reasoned. Ninety years later, Claude Shannon applied Boole's principles in
circuits, the blueprint for electronic computers. Boole's contribution to the
future of computing and Artificial Intelligence was immeasurable, and his logic
is the basis of neural networks.
- Apples are red-- is True
- Apples are red AND oranges are purple-- is False
- Apples are red OR oranges are purple-- is True
- Apples are red AND oranges are NOT purple-- is also True
McCulloch and Pitts, using Boole's principles, wrote a paper
on neural network theory. The thesis dealt with how the networks of connected
neurons could perform logical operations. It also stated that, one the level of
a single neuron, the release or failure to release an impulse was the basis by
which the brain makes true / false decisions. Using the idea of feedback theory,
they described the loop which existed between the senses ---> brain --->
muscles, and likewise concluded that Memory could be defined as the signals in a
closed loop of neurons. Although we now know that logic in the brain occurs at a
level higher then McCulloch and Pitts theorized, their contributions were
important to AI because they showed how the firing of signals between connected
neurons could cause the brains to make decisions. McCulloch and Pitt's theory is
the basis of the artificial neural network theory.
Using this theory, McCulloch and Pitts then designed electronic replicas of
neural networks, to show how electronic networks could generate logical
processes. They also stated that neural networks may, in the future, be able to
learn, and recognize patterns. The results of their research and two of Weiner's
books served to increase enthusiasm, and laboratories of computer simulated
neurons were set up across the country.
Two major factors have inhibited the development of full
scale neural networks. Because of the expense of constructing a machine to
simulate neurons, it was expensive even to construct neural networks with the
number of neurons in an ant. Although the cost of components have decreased, the
computer would have to grow thousands of times larger to be on the scale of the
human brain. The second factor is current computer architecture.
The standard Von Neuman computer, the architecture of nearly all computers,
lacks an adequate number of pathways between components. Researchers are now
developing alternate architectures for use with neural networks.
Even with these inhibiting factors, artificial neural networks have presented
some impressive results. Frank Rosenblatt, experimenting with computer simulated
networks, was able to create a machine that could mimic the human thinking
process, and recognize letters. But, with new top-down methods becoming popular,
parallel computing was put on hold. Now neural networks are making a return, and
some researchers believe that with new computer architectures, parallel
computing and the bottom-up theory will be a driving factor in creating
Top Down Approaches; Expert SystemsBecause of the large storage
capacity of computers, expert systems had the potential to interpret statistics,
in order to formulate rules. An expert system works much like a detective solves
a mystery. Using the information, and logic or rules, an expert system can solve
the problem. For example it the expert system was designed to distinguish birds
it may have the following:
like these represent the logic of expert systems. Using a similar set of rules,
experts can have a variety of applications. With improved interfacing, computers
may begin to find a larger place in society.
ChessAI-based game playing programs combine intelligence with
entertainment. On game with strong AI ties is chess. 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. Herbert
Simon suggested that human chess masters are familiar with favorable board
positions, and the relationship with thousands of pieces in small areas.
Computers on the other hand, do not take hunches into account. 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.
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.
ConclusionThis page touched on some of the main methods used to create
intelligence. These approaches have been applied to a variety of programs. As we
progress in the development of Artificial Intelligence, other theories will be
available, in addition to building on today's methods.