# intro
- this is 383
- web site! www-edlab.cs.umass.edu/cs383
- introduce self
- course is full, please see me after class if you are trying to add
- attendance

# today
- basic idea(s) of AI and the field
- example systems

- instructor and TA
- themes we'll spend time on
- policies
- tips

# basics - what is AI?
- “Over the Christmas holiday, Al Newell and I invented a thinking machine.” — Herbert Simon (1956)
-  In 1997, the Skynet Funding Bill was passed. The system went on-line August 4th, 1997. Human decisions were removed from strategic defense. Skynet began to learn at a geometric rate. It became self-aware at 2:14 a.m. Eastern time, August 29th. In a panic, they try to pull the plug. Etc.
-  https://plus.google.com/+GoogleSelfDrivingCars

- "The study 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." - Dartmouth workshop, 1956

- What have you have done today that demonstrated intelligence?
- What are things that we regard as demonstrating a lack of intelligence?

- Which is more intelligent?
    - dog vs rock ("by what it does, not by what it is."
    - dog vs person ("intelligence varies by task")
    - dog vs computer ("intelligence depends upon "software" and knowledge)
    - win95 vs iPhone ("intelligence is about both learning and breadth of knowledge")

- Fictional AI: Data, Terminator, HAL (form vs function)
- Technological Singularities: will AI be one? Will we know? Has it already happened?
- Rather than attempting to match human performance in tasks at which we excel, AI researchers ought to concentrate on surpassing human performance in tasks to which we are ill-adapted.  Research should focus, not on imitating human cognition, but on exploring alternative styles of intelligence.”- Cullen Schaffer (1988). 
- Any sufficiently advanced technology is indistinguishable from magic.” - Arthur C. Clarke- AI research has enabled systems that can...
    - Identify patterns in data no human can find
    - Make better medical diagnoses than medical interns
    - Solve puzzles far faster than any human player

  and you will learn this semester how these systems can do these things.

# milestones and boundaries
- Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997
- Proved a mathematical conjecture (Robbins conjecture) unsolved for decades
- No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego)
- 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
- NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft
- Proverb solves crossword puzzles better than most humans, by using (among other resources) the Web

- Other examples:
    - spam filtering (and filter evasion)
    - fraud detection
    - machine translation
    - flight booking
    - cruise control w/IR sensors
    - vehicle dispatch
    - medical image analysis

- No longer AI(?)
    - Search engines
    - Programming languages
    - Theorem proving
    - Optical character recognition
    - Expert systems
 
- Touches on many other areas:
    - Philosophy — Logic, methods of reasoning, mind as physical system foundations of learning, language, rationality
    - Mathematics — Formal representation and proof algorithms, computation, (un)decidability, (in)tractability, probability
    - Economics — Utility and decision theory 
    - Neuroscience — Physical substrate for mental activity
    - Psychology — Phenomena of perception and motor control, experimental techniques
    - Computer engineering — Building fast computers
    - Control theory — Design systems that maximize an objective function over time 
    - Linguistics — Knowledge representation, grammar
    - Statistics — Uncertainty, inference, learning

# major questions of the field
- What are the necessary and sufficient conditions for intelligent action?
- What are the minimal components such that an agent can learn to act intelligently by interacting with the world?
- How deeply intertwined are knowledge and perception?
- Can intelligence be separated from a specific physical substrate that implements it?

# Taking a step back: this is 383

- Teach you key facts and skills
    - Understand core algorithms and data structures used in AI
    - Abstract real problems into prototypical tasks that have been studied in AI
    - Map between abstract tasks and  basic AI techniques that address them
- Prepare you for the future
    - Learning about new developments in AI
    - Practical application of AI ideas
    - Additional courses in AI

- Themes
    - Search — Uninformed, heuristic, local, and adversarial; constraint satisfaction
    - Knowledge representation and reasoning — Propositional and first-order logic; Bayesian networks
    - Learning — Naive Bayes, classification trees, probabilistic rules, and Bayesian networks

# Administrative stuff
- prereqs (220|230 & 240): I'm assuming you are able to write non-trivial programs, and know how to handle probability, counting, conditional probability and the like.
- TA: Patrick Pegus
- Textbook: AI (Russell and Norvig); 2e can be used but you'll need to xref section #s
- Grading: 30% assign, 25% in class (paper), 10%/exam (cb/cn), 15% final; no extra credit
- generally no late work (but you can ask for an extension given a reasonable need)
- assignments: near weekly, via edlab; a00 is due Friday
- academic honesty: do your own work, unless otherwise specified. See policy for details.
- office hours Monday 1500; Thursday 1600, Patrick TBA

# tips
- do some work every day (read text before class, not just after; start assignments early)
- come to class (25%!) and take notes / ask questions
- email patrick and me if you have trouble and/or come to office hours

# for next class
- see schedule for reading
- A00 due Friday
- please bring paper and pens/pencils