Dr. Mike Bowman Computer Science and Information Systems Department ACM Spring 2014

2 Acknowledgements Learning Agents Center and Computer Science Department George Mason University lalab.gmu.edu Association for the Advancement of Artificial Intelligence (AAAI)

AI – Thinking Machines? Are you smarter than a machine? Can machines think? What is Skynet? What is the Matrix? 3

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How Smart Are You? Can you count the cars in this picture? Can you estimate the time of day in this picture? 5

How Smart Are You? Can you count the Distribution of letters in a book? Add one thousand 4-digit numbers? Match finger prints? Search a list of a million values for duplicates? 6

AI – What is Real? Are you smarter than a machine? Who was Deep Blue? Who is Watson? What is the Singularity? 7

8 Current Reality? The current reality is that computers and some robots can do some really remarkable complex tasks, and in some cases do them better than humans. But at the same time, no one is yet arguing that machines are conscious or possess human-level intelligence. On the other hand, since no one seems to really understand much about how the human brain works, or what the basis of consciousness is (although some are working on it), the state of AI today is still pretty impressive.

Thinking Machines Artificial intelligence (AI) The study of computer systems that attempt to model and apply the intelligence of the human mind For example, writing a program to pick out objects in a picture 9 Where do you often interact with AI?

AI in Games The Good, the bad, and the ugly (actually the reality) Most modern computer/video games include “intelligent” opposition – AI

The Turing Test Turing test A test to empirically determine whether a computer has achieved intelligence Alan Turing An English mathematician wrote a landmark paper in 1950 that asked the question: Can machines think? He proposed a test to answer the question "How will we know when we’ve succeeded?" 11

The Turing Test In a Turing test, the interrogator must determine which responses are from the computer and which are from the human 12

The Loebner Prize Loebner prize The first formal instantiation (1991) of the Turing test, held annually Chatbots A program designed to carry on a conversation with a human user 13

Some Focus Areas of AI Knowledge representation – represent knowledge so that a computer can apply it Expert Systems – computer systems that embody the knowledge of human experts Neural networks – computer systems that mimic the processing of the human brain Natural language processing – computers that can understand and use human speech Robotics – systems that can complete physical actions to solve problems 14 Just a small sample of AI topics!

Knowledge Representation How can we represent knowledge? We need to create a logical view of the data, based on how we want to process it Natural language is very descriptive, but doesn’t lend itself to efficient processing Semantic networks and search trees are promising techniques for representing knowledge 15

Semantic Networks 16

Intelligent agents contain a representation of an application domain, where information about the domain (objects, relations, classes, laws, actions) are represented as symbolic expressions. This mapping allows the agent to reason about the domain by performing reasoning processes in the domain model, and transferring the conclusions back into the application domain. represents RULE  x,y,z  OBJECT, (ON x y) & (ON y z)  (ON x z) ON CUP1 BOOK1 ON TABLE1 CUP BOOK TABLE INSTANCE-OF OBJECT SUBCLASS-OF Model of the Domain ONTOLOGY  2005, G.Tecuci, Learning Agents Center Knowledge Representation and Reasoning 17 Application Domain If an object is on top of another object that is itself on top of a third object then the first object is also on top of the third object.

Knowledge Base = Ontology + Rules ONTOLOGY FRAGMENT Main condition ?O1isPhD_advisor has_as_employer?O4 has_as_position?O5 ?O2isPhD_student ?O3isresearch_area ?O4isuniversity ?O5istenured_position Except when condition ?O1isperson is_likely_to_move_to?O6 ?O6isemployer IF: Determine whether ?O1 can be a PhD advisor for ?O2 in ?O3. THEN: Determine whether ?O1 would be a good PhD advisor for ?O2 in ?O3. REASONING RULE Determine whether John Smith can be a PhD advisor for Tom Even in Artificial Intelligence. PROBLEM SOLVING TASK 18  2005, G.Tecuci, Learning Agents Center

Search Trees Search tree A structure that represents alternatives in adversarial situations such as game playing The paths down a search tree represent a series of decisions made by the players 19

Search Trees 20

Search Trees Techniques for pruning search space Depth-first A technique that involves the analysis of selected paths all the way down the tree Breadth-first A technique that involves the analysis of all possible paths but only for a short distance down the tree Breadth-first tends to yield the best results 21

Search Trees Depth-first and breadth-first searches 22

Expert Systems Knowledge-based system Software that uses a specific set of information, from which it extracts and processes particular pieces Expert system A software system based on the knowledge of human experts; it is –Rule-based system A software system based on a set of if-then rules –Inference engine The software that processes rules to draw conclusions 23

Expert Systems Gardner Expert System Example 24

Expert Systems Named abbreviations that represent conclusions NONE – apply no treatment at this time TURF – apply a turf-building treatment WEED – apply a weed-killing treatment BUG – apply a bug-killing treatment FEED – apply a basic fertilizer treatment WEEDFEED – apply a weed-killing and fertilizer combination treatment 25

Expert Systems Boolean variables needed to represent state of the lawn –BARE: the lawn has large, bare areas –SPARSE: the lawn is generally thin –WEEDS: the lawn contains many weeds –BUGS: the lawn shows evidence of bugs 26

Expert Systems Some rules –if (CURRENT – LAST < 30) then NONE –if (SEASON = winter) then not BUGS –if (BARE) then TURF –if (SPARSE and not WEEDS) then FEED –if (BUGS and not SPARSE) then BUG –if (WEEDS and not SPARSE) then WEED –if (WEEDS and SPARSE) then WEEDFEED 27

Expert Systems Operation of expert systems can involve dialog between a human and the system –System: Does the lawn have large, bare areas? –User: No –System: Does the lawn show evidence of bugs? –User: No –System: Is the lawn generally thin? –User: Yes –System: Does the lawn contain significant weeds? –User: Yes –System: You should apply a weed-killing and fertilizer combination treatment. 28

Neural Networks Artificial neural networks An attempt to represent knowledge, and solve problems, with approaches that mimic the way the human brain works. – often involves probabilities and statistics to determine (guess?) most likely outcomes. 29

Natural versus Artificial Biological Artificial

Natural Language Processing Three basic types of processing occur during human/computer voice interaction Voice synthesis Using a computer to create the sound of human speech Voice recognition Using a computer to recognizing the words spoken by a human Natural language comprehension Using a computer to apply a meaningful interpretation to human communication 31

Natural Language Comprehension What does this sentence mean? Time flies like an arrow. –Time goes by quickly –Time flies (using a stop watch to measure speed of a fly) as you would time an arrow –Time flies (a kind of fly) are fond of an arrow 32 Crazy? Maybe, but a computer has great difficulty with understanding context.

Imagine a simple rule such as: A + B + C = D It could represent the rule an agent uses to identify a cat: (4 legs) + (fur) + (meow) = CAT A sophisticated agent “learns.” If presented with a dog [(4 legs) + (fur) + (bark) = ??] It should recognize it as not being a cat, and ask a human trainer for clarification, resulting in a new rule: A + B + E = F (4 legs) + (fur) + (bark) = DOG Rules and Machine Learning 33

Start with the same rule: A + B + C = D It could represent the rule an agent uses to identify a cat: (4 legs) + (fur) + (meow) = CAT A very sophisticated agent “learns rapidly.” It might generalize the rule to (~A) + (~B) + (~C) = CAT It will occasionally “miss-fire” (ID a dog as a cat) by misusing the generalized rule, but given “supervision and corrections” by humans (explaining why it misused the rule), it will learn rapidly. Rules and Machine Learning 34

Deep Blue was a special purpose computer that was able to calculate nearly a quarter of a billion chess positions per second. It lost a match with a world champion in 1996, but defeated that same champion in Certain endgame arrangements were always thought to represent a draw -- no human had ever seen a way to win. Deep Blue and other chess playing programs have found ways to win some of these games. The Asian game "go" will not succumb to such brute force methods -- there are simply too many possible moves for even the most powerful current and planned supercomputers. Instead, "real" AI will be needed -- intelligence based on pattern recognition, "insight," and strategy. IBM’s Deep Blue 35 State of the Art in AI

IMB’s Watson – Jeopardy Champion Research question: Can a system be designed that applies advanced data management and analytics to natural language in order to uncover a single, reliable insight — in a fraction of a second? Watson beat human champions in Jeopardy, and is now being used to investigate human health issues. 36 State of the Art in AI

The DARPA Grand Challenge tasked entrants to build an autonomous robotic vehicle that could successfully navigate a long, complex course, without human intervention. None of the entrants in the 2004 Grand Challenge 1 completed more than 10 miles of the 100 mile course. DARPA Grand Challenge State of the Art in AI In the 2005 Grand Challenge II, 5 of the 20+ entrants completed the 132 mile race through the Mojave Desert, well under the 10 hour time limit. The Urban Challenge was to drive through an urban setting. It was successfully completed by multiple entrants! 37

The latest DARPA event is called the Robotics Challenge and tasks entrants to build an all-terrain autonomous device that can successfully navigate through complex obstacles like stairs, without human intervention AND successfully complete missions that are hazardous to humans. DARPA Robotics Challenge State of the Art in AI 38

Where has a famous AI system and someone named Bowman crossed paths? 39 AI versus Bowman CSC370 – Fall 2014 Tuesday and Thursday – 2:00-3:15 PM