X

Download Artificial Intelligence (Department of Computer Sciences) PowerPoint Presentation

SlidesFinder-Advertising-Design.jpg

Login   OR  Register
X


Iframe embed code :



Presentation url :

X

Description :

View and free download Artificial Intelligence (Department of Computer Sciences) powerpoint presentation which is uploaded by search an active user in belonging ppt presentation Computers & Web category.

Tags :

Artificial Intelligence (Department of Computer Sciences)

Home / Computers & Web / Computers & Web Presentations / Artificial Intelligence (Department of Computer Sciences) PowerPoint Presentation

Artificial Intelligence (Department of Computer Sciences) PowerPoint Presentation

Ppt Presentation Embed Code   Zoom Ppt Presentation

PowerPoint is the world's most popular presentation software which can let you create professional Artificial Intelligence (Department of Computer Sciences) powerpoint presentation easily and in no time. This helps you give your presentation on Artificial Intelligence (Department of Computer Sciences) in a conference, a school lecture, a business proposal, in a webinar and business and professional representations.

The uploader spent his/her valuable time to create this Artificial Intelligence (Department of Computer Sciences) powerpoint presentation slides, to share his/her useful content with the world. This ppt presentation uploaded by onlinesearch in Computers & Web ppt presentation category is available for free download,and can be used according to your industries like finance, marketing, education, health and many more.

About This Presentation

Slide 1 - The Foundations of Artificial Intelligence
Slide 2 - ppt slide no 2 content not found
Slide 3 - Our Working Definition of AI Artificial intelligence is the study of how to make computers do things that people are better at or would be better at if: they could extend what they do to a World Wide Web-sized amount of data and not make mistakes.
Slide 4 - Why AI? "AI can have two purposes. One is to use the power of computers to augment human thinking, just as we use motors to augment human or horse power. Robotics and expert systems are major branches of that. The other is to use a computer's artificial intelligence to understand how humans think. In a humanoid way. If you test your programs not merely by what they can accomplish, but how they accomplish it, they you're really doing cognitive science; you're using AI to understand the human mind." - Herb Simon
Slide 5 - A Time Line View the time line
Slide 6 - The Dartmouth Conference and the Name Artificial Intelligence J. McCarthy, M. L. Minsky, N. Rochester, and C.E. Shannon. August 31, 1955. "We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."
Slide 7 - The Origins of AI Hype 1950 Turing predicted that in about fifty years "an average interrogator will not have more than a 70 percent chance of making the right identification after five minutes of questioning". 1957 Newell and Simon predicted that "Within ten years a computer will be the world's chess champion, unless the rules bar it from competition."
Slide 8 - Symbolic vs. Subsymbolic AI Subsymbolic AI: Model intelligence at a level similar to the neuron. Let such things as knowledge and planning emerge. Symbolic AI: Model such things as knowledge and planning in data structures that make sense to the programmers that build them. (blueberry (isa fruit) (shape round) (color purple) (size .4 inch))
Slide 9 - The Origins of Subsymbolic AI 1943 McCulloch and Pitts A Logical Calculus of the Ideas Immanent in Nervous Activity “Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic”
Slide 10 - The Origins of Symbolic AI Games Theorem proving
Slide 11 - Knowledge Acquisition
Slide 12 - What Are the Components of Intelligence?
Slide 13 - Image Perception 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
Slide 14 - Image Perception
Slide 15 - But We’re Still Ahead http://www.captcha.net/
Slide 16 - But We’re Still Ahead
Slide 17 - But We’re Still Ahead
Slide 18 - Reasoning We can describe reasoning as search in a space of possible situations.
Slide 19 - Recall the 8-Puzzle What are the states? http://www.javaonthebrain.com/java/puzz15/ Start state Goal state
Slide 20 - Hotel Maid States: Start state: Operators: Goal state:
Slide 21 - The British Museum Algorithm A simple algorithm: Generate and test But suppose that each time we end a path, we start over at the top and choose the next path randomly. If we try this long enough, we may eventually hit a solution. We’ll call this The British Museum Algorithm or The Monkeys and Typewriters Algorithm http://www.arn.org/docs2/news/monkeysandtypewriters051103.htm
Slide 22 - Branch and Bound Consider the problem of planning a ski vacation. Fly to A $600 Fly to B $800 Fly to C $2000 Stay D $200 (800) Stay E $250 (850) Total cost (1200)
Slide 23 - Problem Reduction Goal: Acquire TV Steal TV Earn Money Buy TV Or another one: Theorem proving in which we reason backwards from the theorem we’re trying to prove.
Slide 24 - What is a Heuristic?
Slide 25 - Example From the initial state, move A to the table. Three choices for what to do next. A local heuristic function: Add one point for every block that is resting on the thing it is supposed to be resting on. Subtract one point for every block that is sitting on the wrong thing.
Slide 26 - A New Heuristic From the initial state, move A to the table. Three choices for what to do next. A global heuristic function: For each block that has the correct support structure (i. e., the complete structure underneath it is exactly as it should be), add one point for every block in the support structure. For each block that has an incorrect support structure, subtract one point for every block in the existing support structure.
Slide 27 - Hill Climbing – Another Example Problem: You have just arrived in Washington, D.C. You’re in your car, trying to get downtown to the Washington Monument.
Slide 28 - Hill Climbing – Some Problems
Slide 29 - Hill Climbing – Is Close Good Enough? A B Is A good enough? Choose winning lottery numbers
Slide 30 - Hill Climbing – Is Close Good Enough? A B Is A good enough? Choose winning lottery numbers Get the cheapest travel itinerary Clean the house
Slide 31 - The Silver Bullet? Is there an “intelligence algorithm”? 1957 GPS (General Problem Solver) Start Goal
Slide 32 - The Silver Bullet? Is there an “intelligence algorithm”? 1957 GPS (General Problem Solver) Start Goal What we think now: Probably not
Slide 33 - But What About Knowledge? Why do we need it? How can we represent it and use it? How can we acquire it? Find me stuff about dogs who save people’s lives.
Slide 34 - But What About Knowledge? Why do we need it? How can we represent it and use it? How can we acquire it? Find me stuff about dogs who save people’s lives. Two beagles spot a fire. Their barking alerts neighbors, who call 911.
Slide 35 - Expert Systems Expert knowledge in many domains can be captured as rules. Dendral (1965 – 1975) If: The spectrum for the molecule has two peaks at masses x1 and x2 such that: x1 + x2 = molecular weight + 28, x1 -28 is a high peak, x2 – 28 is a high peak, and at least one of x1 or x2 is high, Then: the molecule contains a ketone group.
Slide 36 - To Interpret the Rule Mass spectometry Ketone group:
Slide 37 - Expert Systems in Medicine 1975 Mycin attached probability-like numbers to rules: If: (1) the stain of the organism is gram-positive, and (2) the morphology of the organism is coccus, and (3) the growth conformation of the organism is clumps Then: there is suggestive evidence (0.7) that the identity of the organism is stphylococcus.
Slide 38 - Watson How does Watson win? http://www.youtube.com/watch?v=d_yXV22O6n4 Watch a sample round: http://www.youtube.com/watch?v=WFR3lOm_xhE From Day 1 of the real match: http://www.youtube.com/watch?v=seNkjYyG3gI Introduction: http://www.youtube.com/watch?v=FC3IryWr4c8 IBM’s site: http://www-03.ibm.com/innovation/us/watson/what-is-watson/index.html Bad Final Jeopardy: http://www.youtube.com/watch?v=mwkoabTl3vM&feature=relmfu Explanation: http://thenumerati.net/?postID=726
Slide 39 - Expert Systems – Today: Medicine Expert systems work in all these areas: arrhythmia recognition from electrocardiograms coronary heart disease risk group detection monitoring the prescription of restricted use antibiotics early melanoma diagnosis gene expression data analysis of human lymphoma breast cancer diagnosis
Slide 40 - Dr. Watson http://www.wired.com/wiredscience/2012/10/watson-for-medicine/ A machine like that is like 500,000 of me sitting at Google and Pubmed.
Slide 41 - But What About Things That All of Us Know?