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AI At Your Service
an exciting panorama of deployed artificial intelligence
AITopics > Applications > AI At Your Service
"What the field of AI is really about is inventing machines that will help people in a variety of ways, by giving machines some of the sophisticated capabilities that humans have, such as the ability to understand spoken words, or interpret images, or to learn from experience. Usually these machines do not look or act at all like people, but they can be amazingly useful to people by improving and assisting our lives, and complementing rather than replacing the things that we humans like to do. And that's the goal we are collectively working toward."
- Tom Mitchell
Innovative Applications of AI Conferences
 - The Innovative Applications of Artificial Intelligence Conference. By Bruce Buchanan and Sam Uthurusamy. AI Magazine 20(1): Spring 1999, 11-12. "What have you done for us lately? The question comes from an old joke about a Boston politician talking to voters in his district. 'Will you vote for me? I gave your father a job at city hall, I found jobs for your wife, your sons, and your daughter. Last year I directed a million dollars worth of business to your company. And I got the city to repair your street.' To which the voter replied, 'I know all that, but what have you done for us lately?' We in AI get the same kind of question. The Annual Conferences on the Innovative Applications of Artificial Intelligence (IAAI) were initiated 10 years ago to provide yearly updates to our answers."
- For example, here's an excerpt from the 2005 briefing: "The following 7 applications will receive the prestigious AAAI Award for Innovative Applications of AI at the conference. These applications illustrate the continued strategic role AI is playing in new computer systems across a broad range of applications and industries; and delivering tremendous returns on investment. This year’s award winners show that recently-deployed, cutting edge AI applications can be found around the world in industries as disparate as finance, transportation and aerospace; they can be found running operations under the ground, on the ground, in the ocean, in the air, and into space; and they can be found assisting engineers, insurance underwriters, F-18 pilots and genomic research scientists."
- Innovative AI Applications: Introduction to This Special Issue. By Neil Jacobstein and Bruce Porter. AI Magazine 27(3): Fall 2006, 13-14. "This editorial introduces the articles published in the AI Magazine special issue on Innovative Applications of Artificial Intelligence (IAAI), based on a selection of papers that appeared in the IAAI-05 conference, which occurred July 9–13 2005 in Pittsburgh, Pennsylvania."
Additional Specific Applications
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Advisory
Aeromedical Evacuation Planning & Execution
Application Names
Case Adaptation
Concepts
Constraint Satisfaction
Construction
Content personalization
Continuity-Guided Regeneration
Expert Rules/Systems
Financial Services
Frames
Fraud Detection
| Government Laws/Regulation
Insurance Application Processing
Internet-based TV Listings
Knowledge Engineering
Microopportunistic Search
Natural Language Processing
Neural Networks
Ontologies
Optical Character Recognition
Planning
Risk Assessment
Rules
Rule Learning
Transportation Planning
User Profile Acquisition
| Application: Falcon Fraud Manager
Deploying Organization: HNC Software
Location: San Diego, CA
Name(s) of Developers
Current Version
Date of Deployment (mo/yr)
Application Domain: Financial Services
Application Subdomain
Type of Task: Fraud Detection
Description of Task: Examine transaction, cardholder and merchant data to detect a wide range of credit and debit card fraud.
Target Users: Payment card issuing financial institutions.
URL for information: http://www.hnc.com/hnc/business_03/industries_03/fs_03/fin04.html
Contact Name: Melinda Bateman
Contact Phone: (858) 799-8370
Contact Address: 5935 Cornerstone Court West, San Diego, CA 92121
Cost of Development
Cost of Deployment
Key Technologies Employed: Neural Networks
Key Qualitative Results
Key Quantitative Results: Improved fraud detection rates by 30-70% while significantly lowering the rate of false-positives.
Value Added ($/yr or other)
Performance Statistics
Keywords XML or standard ontology tags
Date of Entry: 10/2/01
Last Updated
| Application: OSHA Expert Advisors
Deploying Organization: U.S. Department of Labor, OSHA
Location: Washington, DC
Name(s) of Developers
Current Version
Date of Deployment (mo/yr)
Application Domain: Government Laws/Regulations
Application Subdomain
Type of Task: Advisory
Description of Task: Provide help to identify fire and other safety hazards in a given workplace and gives specific information on how OSHA regulations apply to a particular work site.
Target Users: Small businesses
URL for information: http://www.osha-slc.gov/dts/osta/oshasoft/osha-advisors.html
>> 2007 update: http://www.osha.gov/dts/osta/oshasoft/index.html#eTools
Contact Name
Contact Phone
Contact Address: OSHA, Directorate of Health Standards Programs, U.S. Department of Labor, 200 Constitution Avenue, N.W., Washington, D.C. 20210
Cost of Development: $100,000 / expert program
Cost of Deployment
Key Technologies Employed: Expert rules
Key Qualitative Results
Key Quantitative Results
Value Added ($/yr or other)
Performance Statistics
Keywords XML or standard ontology tags
Date of Entry: 10/08/01
Last Updated
| Application: FineReader
Deploying Organization: ABBYY Software House
Location: Moscow, Russia
Name(s) of Developers
Current Version: 5
Date of Deployment (mo/yr): Nov-00
Application Domain: Natural Language Processing
Application Subdomain
Type of Task: Optical Character Recognition
Description of Task: Convert data scanned from paper documents (such as fax, printer, typewriter, or photocopy outputs) into editable electronic forms.
Target Users
URL for information: http://www.abbyyusa.com/
Contact Name
Contact Phone: (510) 226-6717
Contact Address: ABBYY USA, 46560 Fremont Blvd, Suite 105, Fremont, CA 94538
Cost of Development
Cost of Deployment
Key Technologies Employed: Frames; something they call IPA technology (Integral Purposeful Adaptive perceptron). See Reviewer's Comment below.
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Value Added ($/yr or other)
Performance Statistics
Keywords XML or standard ontology tags
Date of Entry: 10/25/01
Last Updated
Reviewer's Comment: I think from their description, this is some kind of learning system, based on learning and training perceptrons for recognition of heterogeneous objects, that might be represented as frames; currently tracking down articles.
| Application: TRAC2ES (TRANSCOM regulating and command and control evacuation system)
Deploying Organization: U.S. Transportation Command (TRANSCOM)
Location
Name(s) of Developers: Members of TRAC2ES project at Logica Carnegie Group
Current Version
Date of Deployment (mo/yr): 4QFY01
Application Domain: Transportation Planning
Application Subdomain: Dynamic Replanning
Type of Task: Aeromedical evacuation planning and execution
Description of Task: Provide decision support for planning and scheduling medical evacuation operations globally by air to suitable medical treatment facilities.
Target Users: Military and non-military hospital personel, transportation personel, staff at centers who plan and monitor patient movents.
URLs for information: http://www.dote.osd.mil/reports/FY00/airforce/00trac2es.html, http://www.transcom.mil/J6/j6o/j6_oi/pubs/p41-1.pdf
Contact Name
Contact Phone:(618) 256-2895
Contact Address: USTRANSCOM/TCSG, 508 Scott Drive, Scott AFB, Illinois, 62225-5357
Cost of Development: over $163 million
Cost of Deployment
Key Technologies Employed: Continuity-guided regeneration (CGR), microopportunistic serach
Key Qualitative Results: Improved communication of patient movements and movement requests; gave users the ability to quickly and easily create solutions to problems caused by disruptive events.
Key Quantitative Results
Value Added ($/yr or other)
Performance Statistics
Keywords XML or standard ontology tags
Date of Entry: 10/25/01
Last Updated
| Application: DISXPERT
Deploying Organization: New York State Department of Social Services Office of Disability Determination
Location: New York State
Name(s) of Developers: James R. Nolan
Current Version
Date of Deployment (mo/yr): 1992
Application Domain: Social Security Disability Screening & Referral
Application Subdomain: Risk Assessment
Type of Task: Case review and assessment
Description of Task: Provide support for making unbiased and consistent assessment decision regarding referral of clients to vocational rehabilitation services
Target Users: paraprofessional caseworkers
URL for information
Contact Name
Contact Phone
Contact Address
Cost of Development: $60,000
Cost of Deployment: $30,000
Key Technologies Employed: Expert rules
Key Qualitative Results: Increased productivity and reimbursement of SSA funds; decrease in referred client dropout rate
Key Quantitative Results: Case assessment increased by 70,000/year; dropout rate declined over 80%
Value Added ($/yr or other)
Performance Statistics
Keywords XML or standard ontology tags: expert systems, knowledge engineering, rule learning
Date of Entry: 11/26/01
Last Updated
| Application: MITA
Deploying Organization: MetLife
Location: New York, NY
Name(s) of Developers: Barry Glasgow, Alan Mandell, Dan Binney, Lila Ghemri, David Fisher, and developers from Brightware
Current Version
Date of Deployment (mo/yr): Jun-97
Application Domain: Insurance Application Processing
Application Subdomain: Automatic underwriting review
Type of Task: free-form textual field analysis
Description of Task: Extract significant medical and occupational concepts from free-form textual fields on life insurance applications to help automate the underwriting review process
Target Users: insurers
URL for information
Contact Name
Contact Phone
Contact Address
Cost of Development
Cost of Deployment
Key Technologies Employed: Information extraction, Pattern matching
Key Qualitative Results: Reduction in underwriting time; greater underwriting consistency
Key Quantitative Results: 89% of textual fields successfully analyzed
Value Added ($/yr or other)
Performance Statistics
Keywords XML or standard ontology tags: natural language processing, concepts, ontologies, rules, knowledge engineering
Date of Entry: 11/26/01
Last Updated
| Application: PTV
Deploying Organization
Location
Name(s) of Developers: Barry Smyth and Paul Cotter
Current Version: PTVPlus
Date of Deployment (mo/yr): Jan-99
Application Domain: Internet-based TV Listings
Application Subdomain: Content personalization
Type of Task: personalize TV listings
Description of Task: Automatically learn individual users' TV viewing preferences and provide them with customized and personalized daily TV program guides
Target Users: television viewers with PC or mobile wireless web access
URL for information: http://www.ptvplus.com/
Contact Name: James Ryan
Contact Phone
Contact Address: james.ryan@changingworlds.com
Cost of Development
Cost of Deployment
Key Technologies Employed: case-based reasoning, collaborative recommendation
Key Qualitative Results
Key Quantitative Results: End-User evaluation: 99% said easy to use for TV listing; 88% said acceptable response time; 3% said poor personalization quality
Value Added ($/yr or other)
Performance Statistics
Keywords XML or standard ontology tags: user profile acquisition
Date of Entry: 11/26/01
Last Updated
| Application: FASTRAK-APT
Deploying Organization: Hyundai Engineering and Construction
Location: Korea
Name(s) of Developers: Kyoung Jun Lee, Hyun Woo Kim, Jae Kyu Lee (KAIST), and Tae Hwan Kim
Current Version
Date of Deployment (mo/yr): 1996
Application Domain: Apartment Construction
Application Subdomain: Project planning and management
Type of Task: apartment construction planning
Description of Task: Develop apartment construction plans by identifying similar cases from a knowledge base, and modifying the most similar case to reflect the specifications and constraints of the new construction project
Target Users: construction project managers
URL for information
Contact Name
Contact Phone
Contact Address
Cost of Development: $621,000
Cost of Deployment
Key Technologies Employed: case-based reasoning, mixed-initiative planning
Key Qualitative Results: Reduction in initial planning time and effort, improved quality and completeness of construction plans
Key Quantitative Results: Initial project planning reduced from 7 person-days to 1 person-day, plan updating and modification reduced from 2 person-days to 0.5 person-days
Value Added ($/yr or other): $616,000/year
Performance Statistics
Keywords XML or standard ontology tags: expert systems, planning, frames, constraint satisfaction, case adaptation
Date of Entry: 11/28/01
Last Updated
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Template for Embedded AI
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