1. Artificial Intelligence
Brittany Coffer
Nick Deheck
Chelsey Eglseder
Joshua Lewis
David Summey

2. What is Artificial Intelligence?
Simulation of human intelligence
"Alexa"; "Watson"
Machines learn from experience
Netflix
Ability to adjust to new inputs and perform human-like tasks
Driver-less cars
Allows machines to learn without explicit directions
Automates data modeling
Uber
*Achieves high accuracy through deep neural networks --> interactions with Alexa, Google search and Google photos are all based on deep learning and they keep getting more accurate the more we use them 

3. Why Do We Need AI? Unlimited applications Problem Solving
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 data
Unbiased Data Results
Eliminates impromptu data manipulation

4. Limitations & Challenges
"Narrow AI"
Designed to perform a defined task
Limited to specific industries
Technology unlimited
Could fall into "wrong hands"
Currently no overarching laws/regulations
AI 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 ammunition
Military chiefs have warned that US can either lead the coming revolution or fall victim to it

5. 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 

6. Rail Industry Positive Train Control GE Smart Locomotives
Reduce human-factor incidents
GE Smart Locomotives
Increase efficiency and velocity
Predictive Signal Systems
Detect problem in signals before accidents occur
Facial Recognition
Commuter rail 
Merchandise rail
Train Delay Reporting
Increase on-time performance 
Limitations
Not shared
Exploration phase

7. Defense Industry Aircraft Intelligence Machine Learning
F-35 is example of human-machine collaboration
Automated Recovery/Egress
ALIS (Autonomic Logistics Information System)
Machine Learning
Identify correct targets—reduces risks
Solve logistics challenges
Speeds weapon development
Software Processing
Analyzes large data quickly
Immersive training 
Support war games and generate countless scenarios 
Data Mining
SAP HANA

8. Defense Industry Cont. Challenges/Limitations of AI Testing
Relatively new technologies
Lifecycle maintenance
AI alone with not solve all concerns
Collaboration of human-system needed
Operator still needed
Decision making 
Unknown gaps
Cyber security 
Trust factor

9. 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 key

10. Sources
failures/articleshow/ cms