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It’s a way of showing awareness of, and respect for, the Aboriginal Traditional Owners of the land where a meeting or event is held. ‹#›
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The question of machines can think dates back as far as CS One has to be careful with “big” marketing predictions: “in 5 years machine will be as intelligent as us”.
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First slide with Simpson – Turing, then The video at the top to explain what it is, finally Blade Runner video to show it came into movies (some students may not get it, state later that he is finding out whether she is an automaton or a person; she doesn’t know but she is realizing. She was a difficult one to catch, required many questions…) *
Very very quick, just state that even in 1700’s someone said that if a Parrot can talk fluidly then we need to accept it is intelligent *
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So, deciding intelligent does not seem a trivial task, it is very very difficult and cumbersome, muddy, project. We show this video and then jump to the next slide to argue we will rather talk about rationality in AI so to make things easier and more concrete. Still the question of intelligence is interesting and we will come back to it later in the course! *
Rationality as a more approachable notion than “intelligence”. We can formalize “rationality” and build systems for it. But it is just an “approximation”, generaly a “prescriptive” notion of inteligence
Very quick, just state that AI and algorithms are also used in finance
Robots are being used everywhere, from manufacturing to rescue, to space exploration, to agrigulcture
AI can impact everywhere: sports, video games, medicine, personal assistance, cars, art, security, education, markets, etc.
Robots will probably be living with us in important ways, some claim this is dangerous.
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COSC1127/1125 Artificial Intelligence Introduction & Search Semester 1, 2021 Prof. Sebastian Sardina * Some slides are based on those from James Harland. ** Some images gathered via Google image search or Pexels style.visibility
— Wominjeka! Welcome! RMIT University acknowledges the Wurundjeri people of the Kulin Nations as the traditional owners of the land on which the University stands. RMIT University respectfully recognises Elders both past and present.
Acknowledgement I acknowledge the Traditional Owners (Woiwurrung and Boonwurrung form the Kulin nations) of the land on which we will conduct the whole Artificial Intelligence 2021 course during this semester. I, and invite you all to, pay our respects to their Elders, past and present, and the first people of other communities who may be here today. And to be aware of the challenges and injustices that unfortunately continue up to our times . I recognise their continuing connection to land, waters and culture.
Past or present (and future?) June 2021 May 2020 style.visibility style.visibility
You say past? Pathways to Justice–Inquiry into the Incarceration Rate of Aboriginal and Torres Strait Islander Peoples (ALRC Report 133) style.visibility
What I can do? Where can I start? At its heart, reconciliation is about strengthening relationships between Aboriginal and Torres Strait Islander peoples and non-Indigenous peoples, for the benefit of all Australians . style.visibility
Course Book Stuart Russell & Peter Norvig Artificial Intelligence: A Modern Approach , Prentice Hall . 4th Global Edition: ONLINE ACCESS! AI‘21 Quick Course Overview Teaching Staff Lecturer: Prof. Sebastian Sardina Tutors: Andrew Chester & Andres Jaramillo Course Structure 2hrs lectorial: context + questions 1hr tutorial: technical exercises 1hr drop-in lab: software support Discussion & Resource Centre EdStem Forum: https://edstem.org/courses/6081 Why? Students & staff productivity+++ Course Assessments 3+1 Pacman Projects: 15% (+ 3%) Pacman Capture the Flag: 45% Semester THE (Week 9) : 20% Final THE (Week 15): 20% Adhere 100% to RMIT Academic Integrity & AI’21 Honours Code DO NOT LET ME DOWN !!! style.visibility style.visibility style.visibility
Overview Course Overview Introduction to AI: What is it? History. Search Questions?
Code 1550 5600 @ menti.com 1550 5600
It provides the core knowledge of computer science. Hence, serious programmers and software engineers should know about major AI techniques. It can be profitable . It is also fun . It relates to other non-exact sciences . It is different to most other subjects. Why AI is an important course?
The early history of AI Professor Maggie Boden (Department of Informatics at the University of Sussex )
AI/CS has history you need to know… style.visibility style.visibility style.visibility style.visibility style.visibility style.visibility style.visibility style.visibility style.visibility
What is Artificial Intelligence? What is intelligence? “AI is the study of how to make real computers act like the ones in the movies.” style.visibility style.visibility style.visibility style.visibility style.visibility ppt_x
What is AI?
Wh ere is AI?
The age of Intelligent Systems “Beyond Voice Recognition: It’s The Age Of Intelligent Systems” Forbes’ article by CTO of NUANCE: http://www.forbes.com/sites/ciocentral/2013/01/11/beyond-voice-recognition-its-the-age-of-intelligent-systems/ January 2013
So, what is “AI”? “Artificial intelligence is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment.” — Nils J. Nilsson
What is Intelligence? {E4F0F444-259E-4AF3-AB86-9647F25909A9} talk “Hello! How are you today. Could I please have a coffee?” hear Interpret “er… so.. um… as I was saying, what? No! Coffee please!” read Interpret “Time flies like an arrow. Fruit flies like a banana.” think “Low on fuel, it’s getting dark, no clue where I am … Hmmmm….” act Pick things up, move them, walk, build things, … see Distinguish moving objects from static ones, colours, textures, … adapt This isn’t working, so let’s try something else … paint Make judgements about what is pleasing and what is not
You say feelings and emotions?
What is “intelligence”? Code 58 69 60 0 @ menti.com
Bit of history…
So when is X intelligent? Alan Turing Devised Turing machine Lead British codebreakers in WWII Worked on Colossus machine Devised the Turing test: Can machines think? Listen ABC Radio National The Science Show podcast: Alan Turing – Thinker ahead of his time style.visibility style.visibility
The Turing Test “Dr. Turing , can machines think ?’’ “Well, Mr. Simpson, consider this test ….’’ style.visibility ppt_w ppt_h style.visibility ppt_w ppt_h style.visibility
Turing Test ???? “If they find a parrot who could answer to everything, I would claim it to be an intelligent being without hesitation.” — Denis Diderot (French philosopher, 1713-1784) r
techtarget.com
Turing Test: does it w ork? style.visibility ppt_w ppt_h
Deciding Intelligence: Difficult task!
Rationality Turing test: Behaves like a human, but thinks like a human? Doesn’t help much on how to build an intelligent system Rationality as an alternative: Precise mathematical notion of what it means to do the right thing in any particular circumstance. Humans not always rational ! style.visibility style.visibility
So, what is AI? Thus we try to construct systems whose computation achieves or approximates the desired notion of rationality . Hence AI is part of Computer Science. Other areas interested in the study of intelligence lie in other areas of study, e.g., cognitive science, psychology, philosophy, neuroscience: central focus tends to be different. How to achieve intelligent behavior through computational means. style.visibility
Four AI Definitions by Russell & Norvig {F48F8EA8-9010-4267-9676-8E8E4F9DDD2E} Like Humans Not necessary like humans Think Systems that think like humans Systems that think rationally Act Systems that act like humans Systems that act rationally Cognitive Science OUR FOCUS
Stock Exchange Roughly 90% of volume in the public equities markets is traded algorithmically. Roughly 50% of volume in the futures markets is traded algorithmically. Check this post .
Robots are coming!!
The Future: Digital & Full Of Robots
Subareas of AI Perception: vision, speech understanding, etc. Robotics Natural language processing Machine Learning (e.g., Neural networks) Reasoning and decision making Knowledge representation Reasoning (logical, probabilistic) Decision making (search, planning, decision theory)
Artificial Intelligence Course objective: `Basic concepts and techniques of AI’ Search Knowledge representation Planning Intelligent Agents Reasoning under uncertainty Machine Learning
Search Exploration of possibilities . [RN] Sections 3.1-3.4
Water Jugs Given two water jugs (of different sizes), get exactly x litres of water in one of the jugs 0 0 x y Generic “state”: Initial “state”: 3 Final “state”: style.visibility
Search Problems 3 4 2 5 2 0 0 2 3 2 0 0 0 5 style.visibility style.visibility style.visibility style.visibility style.visibility style.visibility style.visibility style.visibility style.visibility style.visibility style.visibility style.visibility
Eight Puzzle 9! = 362, 880 possible states (only need to search 9!/2 = 181,440 of them … )
N-Queens Place N queens on a chessboard so that none of them attack each other (N is usually 8)
Vacuum World Two rooms One vacuum Rooms can be dirty or clean Vacuum has three moves Left Right Suck O nly 8 possible states (!!) Each room 4 possible configurations: fully empty, with dirty, with vacuum, with dirty & vacuum. But do all states make sense if there is only 1 vaccuum?
S earch is an exploration of possibilities . P articularly useful when a ‘direct’ m ethod/algorithm is not known . One of the first topics studied in AI: Newell and Simon (1961) General Problem Solver. Central component to many AI systems: Automated reasoning, theorem proving, robot navigation, VLSI layout, scheduling, game playing,… What is search?
The Search Problem A search problem is defined by: Possible s tates Initial state Actions Transition model Goal test Path “ cost ” Initial State Goal State How to do this? TRANSFORM VIA ACTIONS style.visibility style.visibility
State x y Water Jugs: x in {0,1,2,3} y in {0,1,2,3,4,5} Eight Puzzle: N-Queens: V acuum World:
Initial State Water Jugs: both jugs are empty Eight Puzzle: any state N-Queens: empty chessboard Vacuum World: any state
Actions Water Jugs: empty or fill either jug (from tap) pour jug 1 into jug 2 pour jug 2 into jug 1 Eight Puzzle : move blank square left, right, up, down (not all always possible) N-Queens: add queen to any empty square add queen to leftmost empty column add queen to leftmost empty column in a ‘safe’ row Vacuum World: left, right, suck
Transition model Water Jugs: new state of jugs Eight Puzzle: new puzzle state (if action applicable) N-Queens: new chessboard Vacuum World: new room state
Goal test Water Jugs: 4 litres in larger jug Eight Puzzle: N-Queens: All N queens on board in safe positions Vacuum World:
Path cost Water Jugs: number of exchanges Eight Puzzle: number of tiles moves N-Queens: number of piece moves Vacuum World: number of moves
Chess State: any state of pieces on board (legal ones only?) + turn Initial state: initial board config. + white moves first Action: any legal move (lots of them!) , resign Transition model: complex! (knight moves, taking, castling, promotion, en passant, check restrictions, …) Goal test: checkmate (opponent) Path Cost: ???
The Search Problem A search problem is defined by: Possible s tates Initial state Actions Transition model Goal test Path “ cost ” Initial State Goal State TRANSFORM VIA ACTIONS How to do this? style.visibility style.visibility
Mathematically: G raph reachability Problem : Can we reach state/node 25 from state/node 17?
Search as graph reachability (cont.)
Search as graph reachability (cont.) Initial state Goal states style.visibility style.visibility style.visibility style.visibility
Searching state space State space searching involves finding a path from initial state to goal state – build a search tree starting from initial state – expand a state by applying operators, generating successor (child) states Order we choose states for expansion determined by search strategy …
Data Structures for Search What is in each node-state of the tree? Basic data structure: Search Node/State – Domain state – Parent node and operator applied to parent to reach current node – Cost of the path so far – Depth of the node
Generic Search Procedure function SEARCH ( problem, strategy ) // outputs solution or failure Initialise search tree with initial state of problem Repeat 1. If (no candidate nodes can be expanded) then return failure 2. Choose a leaf node for expansion according to strategy 3. If (chosen node contains a goal state) then return solution 4. Else expand the node as per problem , by applying legal operators to the state within the node, and add new nodes to the search tree.
function SEARCH ( problem, strategy ) // outputs solution or failure Initialize queue by inserting the node corresponding to the initial state Repeat 1. If ( queue is empty) then return failure 2. Dequeue a node as per strategy 3. If (node contains a goal state) then return solution 4. Else Expand the node as per problem , by applying legal operators to the state within the node, and add new nodes to queue . Implementation Details ‹#› Need to keep track of nodes that need to be expanded (fringe): – Use a (prioritized) queue Search algorithms differ in their queuing function! style.visibility
Search Performance Completeness: Is it guaranteed to find a solution if one exists? Optimality: Will it find an optimal solution? Time complexity: How many nodes? Space complexity: How much memory is needed? {F48F8EA8-9010-4267-9676-8E8E4F9DDD2E} b Branching factor d Depth of shallowest goal node m Maximum length of any path in the state space
Search Types Uninformed (blind search) Depth-first Breadth-first Uniform cost Depth-limited Iterative Deepening Heuristic Greedy Best-first search A* ‹#›