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An introduction to AI (artificial intelligence)
The document provides an overview of the fundamentals and applications of artificial intelligence (AI), discussing key concepts like machine learning, robotics, and natural language processing, and exploring the complexity of the human brain in comparison to current computer capabilities. It traces the history of AI from its inception in the 1940s, including milestones like the Turing Test, and highlights significant AI applications, such as Deep Blue's victory in chess and military logistics during the Gulf War. Lastly, it introduces TensorFlow as a prominent tool for developing AI systems and recognizes the challenges that remain in areas like computer vision and adaptive learning.
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An introduction to AI (artificial intelligence)
- 1. An introduction toAI Let’s code an intelligent system JNJD 2016 INPT
- 2. PlAN Part I: Fundamentalsof 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
- 3. Human Intelligence ?! theability to acquire and utilize(link) knowledge to solve (novel) problems
- 4. Can we buildhardware 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
- 5. Artificial intelligence is intelligencein machine
- 6. AI is Science Deep learning theability to act like humans
- 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
- 9. numerous applications andhuge 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? Testto determine whether or not machines were intelligent Turing test Chinese Room
- 11. Summary of Stateof 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
- 12.
- 13. TensorFlow is an OpenSource (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.
- 14.
- 15. Why is visualrecognition a hard problem?
- 17. Futur Is really unknownbut at this evolution rate it will become more and more complex.
Editor's Notes
- #4 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.
- #6 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).
- #7 cognitif (adj.) إدراكي
- #8 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.
- #11 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.
- #14 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.