/
Nick Deheck Chelsey Eglseder Joshua Lewis David Summey What is Artificial Intelligence Simulation of human intelligence Alexa Watson Machines learn from experience Netflix Ability to adjust to new inputs and perform humanlike tasks ID: 667967 Download Presentation
Tags :Download Presentation - The PPT/PDF document "Artificial Intelligence Brittany Coffer" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Presentation on theme: "Artificial Intelligence Brittany Coffer"— Presentation transcript
Slide1
Artificial Intelligence
Brittany Coffer
Nick
Deheck
Chelsey
Eglseder
Joshua Lewis
David SummeySlide2
What is Artificial Intelligence?
Simulation of human intelligence
"Alexa"; "Watson
"Machines learn from experienceNetflixAbility to adjust to new inputs and perform human-like tasksDriver-less carsAllows machines to learn without explicit directionsAutomates data modelingUberSlide3
Why Do We Need AI?
Unlimited applications
Usefulness in any industry
Potential to remove human error Problem Solving Adds intelligence to existing products Automates repetitive learning and discovery through data Achieves high accuracy through deep neural networks Gets the most out of dataUnbiased Data ResultsEliminates impromptu data manipulationSlide4
Limitations & Challenges
"Narrow AI"
Designed to perform a defined task
Limited to specific industriesTechnology unlimitedCould fall into "wrong hands"Currently no overarching laws/regulationsAI could be programmed to do something beneficial, but...Arrives at a devastating conclusion"Who do I hit?" scenario
Rapidly escalating international competition over AI
Wars of future will use algorithms like ammunitionSlide5
Healthcare Industry
Ability to detect colorectal cancer early with 86% accuracy
Significant as colorectal cancer is the second deadliest form of cancer, behind lung cancer.
Ability to detect brain bleeds and tuberculosis
Behavior-based security system
Clinical documentation
Smart Tissue Autonomous Robot (STAR)
Benefits
Potentially save lives
Increase security
Better patient experience
Limitations
Behavior changes
Difficulty in normalizing data sets Slide6
Rail Industry
Positive Train Control
Reduce human-factor incidents
GE Smart LocomotivesIncrease efficiency and velocityPredictive Signal SystemsDetect problem in signals before accidents occurFacial RecognitionCommuter rail Merchandise railTrain Delay ReportingIncrease on-time performance LimitationsNot shared
Exploration phaseSlide7
Defense Industry
Aircraft Intelligence
F-35 is example of human-machine collaboration
Automated Recovery/EgressALIS (Autonomic Logistics Information System)Machine LearningIdentify correct targets—reduces risksSolve logistics challengesSpeeds weapon developmentSoftware ProcessingAnalyzes large data quickly
Immersive training
Support war games and generate countless scenarios
Data Mining
SAP HANASlide8
Defense Industry Cont.
Challenges/Limitations of AI
Testing
Relatively new technologiesLifecycle maintenanceAI alone with not solve all concernsCollaboration of human-system neededOperator still neededDecision making Unknown gapsCyber security Trust factorSlide9
Law Enforcement Industry
Investigation Support
Capable identifying: Key Words in Speech, Facial and Object Features
Provides more thorough search, enables investigators to "multi-task"
Limitations
Identifies only what it has been "shown"
Learning phase is slow, many objects are very similar
Identification across accents (language), angles (photos), and video clarity
Overcome
Dedicated work by users to detail searches and expand database
Exposure is keySlide10
Sources
http://www.healthcareitnews.com/slideshow/how-ai-transforming-healthcare-and-solving-problems-2017?page=1
http://www.healthcareitnews.com/news/aetna-replacing-security-passwords-machine-learning-tools
http://fortune.com/2017/10/30/ai-early-cancer-detection/https://www.inverse.com/article/37873-artificial-intelligence-colorectal-cancer-detectionhttps://www.fbi.gov/news/testimony/law-enforcements-use-of-facial-recognition-technologyhttp://www.policemag.com/channel/technology/articles/2017/10/artificial-intelligence-and-law-enforcement.aspx
https://www.nanalyze.com/2017/11/8-companies-ai-law-enforcement/
http://searchcio.techtarget.com/definition/AI
https://en.wikipedia.org/wiki/SAP_HANA
h
ttps://www.rand.org/blog/2017/09/artificial-intelligence-and-the-military.html
http://www.information-age.com/trains-brains-how-artificial-intelligence-transforming-railway-industry-123460379/
https://economictimes.indiatimes.com/industry/transportation/railways/railways-to-use-artificial-intelligence-for-preventing-signal-failures/articleshow/61737103.cms
https://aibusiness.com/ai-revamp-rail-industry-facial-recognition-technology/
http://railwayinnovation.com/tag/artificial-intelligence/
http://www.amadeus.com/blog/16/02/technology-trends-railways/
https://magazine.startus.cc/disrupting-rail-industry-breakdown-startup-driven-innovation/
https://venturebeat.com/2017/10/09/ge-using-ai-to-build-locomotives-that-think/
https://blog.willis.com/2017/05/the-impact-of-artificial-intelligence-in-transportation/
https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html
https://www.topbots.com/healthcare-ai-opportunities-challenges-policy-workflow/
http://www.gnshealthcare.com/5-challenges-applying-ai-machine-learning-healthcare/
https://hcss.nl/sites/default/files/files/reports/Artificial%20Intelligence%20and%20the%20Future%20of%20Defense.pdf