Administrative Notes March 15, 2018 Do you want to present your - PowerPoint PPT Presentation

Administrative Notes March 15, 2018 • Do you want to present your project to the class? If so, sign up using the following link (also listed on the website)! • https://ubc.ca1.qualtrics.com/jfe/form/SV_d6aeH7 wmATnJNIh • Midterm 2 grading status • Just about done. Midterms should be available via handback tonight or tomorrow morning. • Look out for an announcement on Canvas Computational Thinking www.ugrad.cs.ubc.ca/~cs100

While Watson won, it did make an embarrassing mistake. It clearly didn’t fully understand. Computational Thinking http://www.youtube.com/watch?v=7h4baBEi0iA www.ugrad.cs.ubc.ca/~cs100

Is Watson intelligent by Strong AI criteria? Clicker question A. Yes B. No Reminder: Strong AI – is epitomized by the Chinese Room (Section 6 of the reading) – the computer has to be able to THINK Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Is Watson intelligent by Turing/weak AI criteria? Clicker question A. Yes B. No Reminder: Weak AI is epitomized by Turing’s approach – the computer just has to APPEAR intelligent – fool a person for 5 minutes that it’s human Computational Thinking www.ugrad.cs.ubc.ca/~cs100

What is Watson up to now? Fighting cancer… “Using a Watson app developed with Baylor College of Medicine called KnIT (Knowledge Integration Toolkit) that reads and analyzes millions of scientific papers and suggests to researchers where to look and what to look for, a Baylor team has identified six new proteins to target for cancer research. How hard is that? Very. In the last 30 years, scientists have uncovered 28 protein targets, according to IBM. The Baylor team found half a dozen in a month.” http://time.com/3208716/ibm-watson-cancer/ Computational Thinking www.ugrad.cs.ubc.ca/~cs100

What is Watson up to now? Cooking… “Researchers at IBM have teamed up with the Institute of Culinary Education in New York. They've re-programmed Watson to serve as a sort of sous-chef that can spit out novel ingredient combinations and recipes on command. The IBM researchers call it "creative computing." Chefs can specify a key ingredient and a cuisine, and IBM's computer program will come up with millions of ideas.” http://www.npr.org/blogs/thesalt/2014/03/03/285326 611/our-supercomputer-overlord-is-now-running-a- food-truck Computational Thinking www.ugrad.cs.ubc.ca/~cs100

What is Watson up to now? Debating… Computational Thinking https://www.youtube.com/watch?v=6fJOtAzICzw&t=45m26s www.ugrad.cs.ubc.ca/~cs100

The person running the demo asks this question: “Can a computer take raw information and digest and reason on that information, and understand the context?” Does Watson do that here? A. Yes B. No Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Group Exercise Develop a definition of computational intelligence that you're happy with. Consider the examples that we've looked at as well as other examples (e.g., Chrome, Siri) as a way to help make your definition robust. A. My definition is weak AI (Turing's) B. My definition is strong AI C. My definition is neither. Computational Thinking www.ugrad.cs.ubc.ca/~cs100

By your definition, is Watson intelligent or not? A. Yes B. No Computational Thinking www.ugrad.cs.ubc.ca/~cs100

AI Definition round up Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Okay, so that’s what AI is. But how did they do that? • There are LOTS of different parts involved • We’ll look at a few • Note that we’ll cover the general idea of how things work, but not the specific details • We’ll start with looking at how Watson “understands” Computational Thinking www.ugrad.cs.ubc.ca/~cs100

How did they do that? A final look behind the scenes Computational Thinking http://www.youtube.com/watch?v=lI- www.ugrad.cs.ubc.ca/~cs100 M7O_bRNg&t=3m20s

The overall Watson architecture Computational Thinking www.ugrad.cs.ubc.ca/~cs100

First part of how Watson works: Knowledge bases • The answer sources and evidence sources are stored in Watson’s system; the internet is not used directly • These local data stores are called knowledge bases; many applications use them. Computational Thinking www.ugrad.cs.ubc.ca/~cs100

How are knowledge bases organized? • Some knowledge bases are structured databases – the data is put in in a very specific format • Other knowledge bases are unstructured or semi- structured – the data is not as rigidly organized • E.g., Google stores entire webpages (unstructured). Wikipedia has some structure, but it’s not totally rigid (semi-structured) • An index (like in a book) helps find relevant information But how does Watson know what information to find? Computational Thinking www.ugrad.cs.ubc.ca/~cs100

How does Watson process language? Natural Language processing • Natural Language Processing (NLP): automatic processing of human language, e.g., by computers • Examples: • Siri processes human language to appropriately respond to the command “set a 5 minute timer” – that uses NLP. • Your web browser just displays the information you request – that does not require NLP… but your search engine does Computational Thinking http://www.aaai.org/ojs/index.php/aimagazine/article/view/2303 www.ugrad.cs.ubc.ca/~cs100

Group exercise NLP is needed for many different things that computers do these days. List applications that you have used that need NLP and what they used it for. • Google Translate • A computer trying to understand commands • E.g., Siri, Alexa, Google Home, etc. • Call systems • Grammar correctors Computational Thinking www.ugrad.cs.ubc.ca/~cs100

How does NLP work? • NLP is challenging! • NLP draws on many disciplines: linguistics, cognitive science, psychology, logic, computer science, philosophy, engineering, … Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Typical NLP steps 1. Recognize speech (Watson skipped this – it received ASCII versions of the questions) 2. Syntax analysis, or parsing: inferring parts of speech and sentence structure, using a lexicon and grammar 3. Semantic analysis: inferring meaning using syntax and semantic rules 4. Pragmatics: inferring meaning from contextual information Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Parsing: identifying parts of speech and sentence structure using lexicon and grammar Input: Lexicon Grammar Word Category Sentence à NounPhrase, VerbPhrase VerbPhrase à Verb, NounPhrase Cat Noun NounPhrase à Article, Noun Cheese Noun NounPhrase à Noun Ate Verb ate cheese the Article verb noun the rat article noun Noun phrase Output: a parse tree à Noun phrase Verb phrase Sentence Computational Thinking www.ugrad.cs.ubc.ca/~cs100

How parsing helped Watson The structure of some clues and certain keywords tells Watson what the form of the answer will be – without considering semantics. Consider the following clue that Watson can answer: Category : Oooh....Chess Clue : Invented in the 1500s to speed up the game, this maneuver involves two pieces of the same color. Answer : Castling Parsing is key in Watson’s ability to answer this question Computational Thinking www.ugrad.cs.ubc.ca/~cs100

How parsing helped Watson Parsing takes the sentence and shows how the words are assigned parts of speech and build up to form a sentence: Data mining showed that given this structure, the noun between the two verb phrases was the type of thing the answer is. In this case, the answer was a “maneuver.” Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Group exercise: create a parse tree Lexicon Grammar Sentence à NounPhrase, VerbPhrase Word Category VerbPhrase à Verb, NounPhrase Cat Noun NounPhrase à Article, Noun Rat Noun NounPhrase à Article, Adjective, Noun Chased Verb Large Adjective the Article Using the above lexicon and grammar, parse the sentence: “the large cat chased the rat” If you have a choice of rules, pick the one that works best. You don’t have to use all the rules. Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Parsing: identifying parts of speech and sentence structure using lexicon and grammar Lexicon Grammar Sentence à NounPhrase, VerbPhrase Word Category VerbPhrase à Verb, NounPhrase Cat Noun NounPhrase à Article, Noun Rat Noun NounPhrase à Article, Adjective, Noun Chased Verb Large Adjective the Article Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Parse “time flies like an arrow” Group exercise Write down your tree structure and your algorithm. Note: you don’t have to use all the rules! Lexicon Grammar Word Category Sentence à NounPhrase, VerbPhrase an article NounPhrase à Article, Noun arrow noun NounPhrase à Article, Adjective, Noun NounPhrase à Noun flies noun NounPhrase à Noun, Noun flies verb VerbPhrase à Verb, Adverb, NounPhrase time noun VerbPhrase à Verb, NounPhrase time verb like adverb like verb Computational Thinking www.ugrad.cs.ubc.ca/~cs100

Use your algorithm to parse “fruit flies like a banana” Group exercise Did the algorithm work? A. Yes B. No C. Kind of… but “flies” wasn’t quite right. Computational Thinking www.ugrad.cs.ubc.ca/~cs100