An introduction to AI (artificial intelligence)
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2. PlAN
Part I: Fundamentals of AI
Overview of AI
Statistics, Uncertainty, and Bayes networks
Machine Learning
Logic and Planning
Markov Decision Processes and Reinforcement Learning
Hidden Markov Models and Filters
Adversarial and Advanced Planning
Part II: Applications of AI
Image Processing and Computer Vision
Robotics and robot motion planning
Natural Language Processing and Information Retrieval
4. Can we build hardware as complex as the brain?
How complicated is our brain?
a neuron, or nerve cell, is the basic information processing unit
estimated to be on the order of 10 12 neurons in a human brain
many more synapses 10 14) connecting these neurons
cycle time: 10 -3 seconds (1 millisecond)
How complex can we make computers?
108 or more transistors per CPU
supercomputer: hundreds of CPUs, 1012 bits of RAM
cycle times: order of 10 - 9 seconds
7. History of AI
1943: early beginnings. McCulloch & Pitts: Boolean
circuit model of brain (they modeled a simple neural
network using electrical circuits)
1950: Turing.Turing's "Computing Machinery and
Intelligence“
1956: birth of AI. Dartmouth meeting: "Artificial
Intelligence“ name adopted
8.
9. numerous applications and huge possibilities in the field of AI, which continues to
expand human capability beyond our imagination
Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997
During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling
program that involved up to 50,000 vehicles, cargo, and people
10. Is it AI?
Test to determine whether or not machines were intelligent
Turing test
Chinese Room
11. Summary of State of AI Systems in Practice
Speech synthesis, recognition and understanding
very useful for limited vocabulary applications
unconstrained speech understanding is still too hard
Computer vision
works for constrained problems (hand-written zip-codes)
understanding real-world, natural scenes is still too hard
Learning
adaptive systems are used in many applications: have their limits
Planning and Reasoning
only works for constrained problems: e.g., chess
real-world is too complex for general systems
Overall:
many components of intelligent systems are “doable”
there are many interesting research problems
remaining
13. TensorFlow
is an Open Source (Apache 2.0) Software Library
for Machine Intelligence www.tensorflow.org
TensorFlow comes with an easy to use Python interface and a no-nonsense C++ interface to
build and execute your computational graphs. Write stand-alone TensorFlow Python or C++
programs, or try things out in an interactive TensorFlow iPython notebook where you can keep
notes, code, and visualizations logically grouped. This is just the start though -- we’re hoping to
entice you to contribute SWIG interfaces to your favorite language -- be it Go, Java, Lua,
JavaScript, or R.
Intelligence is classically defined as “the ability to acquire and utilize knowledge.”
Intelligence has been defined in many different ways including one's capacity for logic, abstract thought, understanding, self-awareness, communication, learning, emotional knowledge, memory, planning, creativityand problem solving.
Intelligence is most widely studied in humans, but has also been observed in non-human animals and in plants. Artificial intelligence is intelligence in machines (such as software).
cognitif (adj.)
إدراكي
In 1943, neurophysiologist Warren McCulloch and mathematician Walter Pitts wrote a paper on how neurons might work. In order to describe how neurons in the brain might work, they modeled a simple neural network using electrical circuits.
The Turing Test, proposed by Alan Turing (Turing, 1950), was designed to provide a satisfactory operational definition of intelligence. Turing defined intelligent behavior as the ability to achieve human-level performance in all cognitive tasks, sufficient to fool an interrogator. Roughly speaking, the test he proposed is that the computer should be interrogated by a human via a teletype, and passes the test if the interrogator cannot tell if there is a computer or a human at the other end. Chapter 26 discusses the details of the test, and whether or not a computer is really intelligent if it passes. For now, programming a computer to pass the test provides plenty to work on. The computer would need to possess the following capabilities:
natural language processing to enable it to communicate successfully in English (or some other human language);
knowledge representation to store information provided before or during the interrogation;
automated reasoning to use the stored information to answer questions and to draw new conclusions;
machine learning to adapt to new circumstances and to detect and extrapolate patterns.
TensorFlow comes with an easy to use Python interface and a no-nonsense C++ interface to build and execute your computational graphs. Write stand-alone TensorFlow Python or C++ programs, or try things out in an interactive TensorFlow iPython notebook where you can keep notes, code, and visualizations logically grouped. This is just the start though -- we’re hoping to entice you to contribute SWIG interfaces to your favorite language -- be it Go, Java, Lua, JavaScript, or R.