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Artificial Intelligence - Overview
Artificial intelligence (AI) uses computers to solve problems or make automated decisions typically requiring human intelligence. The two major AI techniques are rules-based and machine learning approaches. Rules-based AI uses logical rules to automate processes, while machine learning algorithms find patterns in data to improve performance over time without being explicitly programmed. Today, AI is mostly "weak" and pattern-based, not capable of human-level reasoning, but it has automated many tasks through proxies like statistical patterns. Hybrid systems combining approaches work best.
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Artificial Intelligence - Overview
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- 2. Artificial IntelligenceOverview 1.What isArtificial Intelligence? 2.Major Artificial IntelligenceTechniques • Rulesand LogicBasedApproach • Machine Learning BasedApproach • Hybrid System 3.Limits of Artificial IntelligenceToday
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- 4. Artificial Intelligence (AI) •What is Artificial Intelligence(AI)? • Using computers to solveproblems • Or make automated decisions • For tasks that, when done byhumans, • Typically require intelligence
- 5. Limits of ArtificialIntelligence •“Strong”ArtificialIntelligence • Computers thinking at alevel that meets or surpassespeople • Computers engaging in abstract reasoning & thinking • Thisisnot what we have today • Thereisnoevidencethat we are closeto Strong AI • “Weak”Pattern-Based Artificial Intelligence • Computers solve problems by detecting useful patterns • Pattern-based AI is an Extremely powerful tool • Hasbeen used to automate many processestoday • Driving, language translation • Thisis the dominant mode of AItoday ✔ ✘
- 6. Major AIApproaches TwoMajor AITechniques •Logic and Rules-BasedApproach • Machine Learning (Pattern-BasedApproach)
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- 8. Logic and Rules-BasedApproach •Logic and Rules-BasedApproach • Representing processesor systemsusing logical rules • Top-down rules are created for computer • Computers reason about thoserules • Canbe usedto automateprocesses
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- 10. Machine Learning (Patternbased) •Machine Learning(ML) • Algorithms find patterns in data and infer rules on their own • ”Learn” from data and improve overtime • Thesepatterns canbe usedfor automation orprediction • MLis the dominant mode ofAI today
- 11. Machine LearningUses Self-Driving VehiclesAutomated recommendations Computer Translation
- 12. Machine Learning MainPoints Learning Pattern Detection Data Self-Programming
- 13. Spam or WantedEmail? System detects patterns in Email About likely markers of spam Detected Pattern Emails with “Earn Cash” More likely to be spam email Can use such detected patterns to make automated decisions about future emails Example: Email Spam Filter “EarnCash” “EarnCash” detected in 10% of Spamemails 0%of wantedemails
- 14. Identification Improves Algorithm improvesin performance In auto-identifying spam As it is able to examine more data And find additional indicia of spam Algorithm is “learning” over time from additional examples Example: Email Spam Filter “Free” Probability of Spam Contains “Free” 70%Spam Contains “Earn Cash” 90%Spam From Belarus 85%Spam
- 15. Intelligent Results Without Intelligence Forsome (not all) complex tasks Requiring intelligence Can get “intelligent” automated results without intelligence By finding suitable Proxies or Patterns
- 16. Proxies for IntelligentResults Without Intelligence Statistical Machine Translation People use advanced cognitive skills to translate Google finds statistical correlations by analyzing previously translated documents Produces automated translations using statistical likelihood as a “proxy” for underlying meaning
- 17. Proxy Principle forAutomation Detecting Patterns That can serve as Proxies For some underlying Cognitive Task
- 18. Machine Learning MainPoints Learning Pattern Detection Data Self-Programming
- 19. Summary Major AIApproaches TwoMajorAITechniques • Logic and Rules-BasedApproach • Machine Learning (Pattern-BasedApproach)
- 20. Hybrid Systems • ManysuccessfulAI systemsare hybrids of • Machine learning & Rules-BasedHybrids • e.g. Self-driving carsemploy both approaches • Human intelligence +AI Hybrids • Also, many successfulAI systemswork best when • Theywork with humanintelligence • AI systemssupply information for humans
- 21. Humans + Computers Technology Enhancing (Not Replacing)Humans > Humans Alone ComputersAlone
- 22. Examples of AIin LawToday • Machine Learning • AI in Litigation - E-Discovery and ”PredictiveCoding” • Natural LanguageProcessing(NLP)of Legal Documents • Automated contractanalysis • Predictive Analytics for Litigation • Machine LearningAssisted LegalResearch • Logic and Rules-BasedApproaches • Compliance Engines • Expert Systems • Attorney Workflow RuleSystems • Automated DocumentAssembly
- 23. Limits on ArtificialIntelligence •Artificial IntelligenceAccomplishments • Automate many things that couldn’tdo before • Limits • Many things still beyond the realm of AI • No thinking computers • NoAbstract Reasoning • Often AI systemsHaveAccuracy Limits • Many things difficult tocapture in data • Sometimes Hard to interpretSystems
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