programming-for-dummies

Machine Learning 655
Robotics and artificial intelligence
Throughout the years, artificial intelligence (AI)
has always aimed at a moving target. Initially,
opponents boasted that computers could never
beat a chess grandmaster, but when a computer
finally did it, AI critics claimed that chess
computers were nothing more than fast search
algorithms that had little to do with actual reasoning.
Although natural language programs,
like ELIZA, can already claim to have passed
the Turing Test, AI critics claim that parroting
back phrases to trick a human still doesn’t qualify
as true intelligence.
Robotics may be the final test of AI because the
stereotypical robot combines multiple aspects
of AI: Speech recognition, image recognition,
machine learning, and expert systems. AI critics
will have a hard time dismissing an intelligent
robot that can talk, understand spoken
commands, and learn while it walks up and
down a flight of stairs.
Before robotics can ever achieve this ultimate
dream of creating a robot that mimics a human
being, robotic engineers must literally first learn
to crawl before they can walk. Like early AI
research, most robots are designed to excel
within an extremely narrow domain. Assemblyline
robots know how to weld car frames
together but can’t answer a simple question.
Military-drone robots may know how to recognize
targets on the ground but can’t understand
spoken commands.
Despite these limitations, robotics has a growing
future. Essentially, robots are nothing more
than computers capable of moving or manipulating
their environment. Maybe one day we’ll
finally have true artificial intelligence at the
same time we finally have a true robot that
meets the criteria set by science fiction authors
so long ago. Until then, however, robotics is
likely to remain a fledging offshoot of computer
science and artificial intelligence. Don’t expect
a robot servant capable of understanding
spoken commands and able to reason and
learn any time soon, but don’t be surprised
when someone finally invents one either.
A single neuron accepts data and produces a response, much like an ordinary
IF-THEN statement used in an expert system. To train a neural network,
you can feed it specific data and examine the output of the neural
network. Then you can adjust the weights of different neurons to more
closely modify the output to a specific result.
Book VII
Chapter 4
Artificial
Intelligence
Such training can be time-consuming, so another approach is to let the
neural network train itself. Based on its output, a neural network can use its
own output as input to change the overall neural network’s result. Such selftraining
neural networks effectively mimic the learning process.